(Integer.class, TuningParameter.class);
+
+ @Override
+ public void save() {
+ this.updatedTs = new Timestamp(System.currentTimeMillis());
+ super.save();
+ }
+
+ @Override
+ public void update() {
+ this.updatedTs = new Timestamp(System.currentTimeMillis());
+ super.update();
+ }
}
diff --git a/app/views/help/spark/helpConfigurationHeuristic.scala.html b/app/views/help/spark/helpConfigurationHeuristic.scala.html
index 1d9ec968f..6bf673521 100644
--- a/app/views/help/spark/helpConfigurationHeuristic.scala.html
+++ b/app/views/help/spark/helpConfigurationHeuristic.scala.html
@@ -14,8 +14,7 @@
* the License.
*@
The results from this heuristic primarily inform you about key app
-configuration settings, including driver memory, driver cores, executor cores,
-executor instances, executor memory, and the serializer.
-It also checks the values of dynamically allocated min and max executors, the specified yarn jars, executor and driver memory overhead and whether other configuration values are within threshold.
+configuration settings, including executor cores, executor instances, executor memory, and the serializer.
+It also checks the values of dynamically allocated min and max executors, the specified yarn jars, executor memory overhead and whether other configuration values are within threshold.
Suggestions
Suggestions based on the configurations you have set are given in the heuristic result itself.
\ No newline at end of file
diff --git a/app/views/help/spark/helpDriverHeuristic.scala.html b/app/views/help/spark/helpDriverHeuristic.scala.html
new file mode 100644
index 000000000..83f65a674
--- /dev/null
+++ b/app/views/help/spark/helpDriverHeuristic.scala.html
@@ -0,0 +1,23 @@
+@*
+* Copyright 2016 LinkedIn Corp.
+*
+* Licensed under the Apache License, Version 2.0 (the "License"); you may not
+* use this file except in compliance with the License. You may obtain a copy of
+* the License at
+*
+* http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+* License for the specific language governing permissions and limitations under
+* the License.
+*@
+This is a heuristic for checking whether the driver is well tuned and the configurations are set to a good value.
+It checks the following properties
+Driver Max Peak JVM Used Memory
+This analyses whether the driver memory is set to a good value. To avoid wasted memory, it checks if the peak JVM used memory by the driver is reasonably close to the user allocated driver memory which is specified in spark.driver.memory. If the peak JVM memory is much smaller, then the driver memory should be reduced.
+Time spent by driver in GC
+This checks if your job spends too much time in GC. We recommend increasing spark.driver.memory if it does.
+Checking configuration thresholds
+The values of spark.driver.memory, spark.driver.cores and spark.yarn.driver.memoryOverhead are checked to verify if they are within threshold values.
\ No newline at end of file
diff --git a/app/views/help/spark/helpExecutorStorageSpillHeuristic.scala.html b/app/views/help/spark/helpExecutorStorageSpillHeuristic.scala.html
new file mode 100644
index 000000000..a23efb735
--- /dev/null
+++ b/app/views/help/spark/helpExecutorStorageSpillHeuristic.scala.html
@@ -0,0 +1,23 @@
+@*
+* Copyright 2016 LinkedIn Corp.
+*
+* Licensed under the Apache License, Version 2.0 (the "License"); you may not
+* use this file except in compliance with the License. You may obtain a copy of
+* the License at
+*
+* http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+* License for the specific language governing permissions and limitations under
+* the License.
+*@
+Spark performs best when data is kept in memory. Spilled execution memory is tracked by memoryBytesSpilled, which is available executor level. If execution memory is being spilled, then the warnings are as follows:
+Low: memoryBytesSpilled is non-zero for 1 or more executors, greater than zero for < 20% of executors, and max size is < .05 * spark.executor.memory.
+Moderate: memoryBytesSpilled is non-zero for 1 or more executors, greater than zero for < 20% of executors, and avg size is < .05 * spark.executor.memory.
+Severe: memoryBytes Spilled is greater than zero for > 20% of executors and avg size is < .05 * spark.executor.memory.
+Critical: memoryBytes Spilled is greater than zero for > 20% of executors and/or avg size is >= .05 * spark.executor.memory.
+Suggestions
+If number of cores (spark.executor.cores) is more than 4 and executor memory is > 10GB : Try decreasing the number of cores which would decrese the number of tasks running in parallel, hence decreasing the number of bytes spilled.
+You can also try increasing the spark.executor.memory which will reduce memory spilled.
diff --git a/app/views/help/spark/helpJvmUsedMemoryHeuristic.scala.html b/app/views/help/spark/helpJvmUsedMemoryHeuristic.scala.html
new file mode 100644
index 000000000..8db78a999
--- /dev/null
+++ b/app/views/help/spark/helpJvmUsedMemoryHeuristic.scala.html
@@ -0,0 +1,19 @@
+@*
+* Copyright 2016 LinkedIn Corp.
+*
+* Licensed under the Apache License, Version 2.0 (the "License"); you may not
+* use this file except in compliance with the License. You may obtain a copy of
+* the License at
+*
+* http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+* License for the specific language governing permissions and limitations under
+* the License.
+*@
+This is a heuristic for peak JVM used memory.
+Executor Max Peak JVM Used Memory
+This is to analyse whether the executor memory is set to a good value. To avoid wasted memory, it checks if the peak JVM used memory by the executor is reasonably close to the user allocated executor memory which is specified in spark.executor.memory. If the peak JVM memory is much smaller, then the executor memory should be reduced.
+ Note: Please note that for calculation purposes Dr. Elephant considers 1024 Bytes in 1 KB whereas the spark history server considers 1000 Bytes. So please don't get confused if you find discrepancy in values from these two places.
\ No newline at end of file
diff --git a/app/views/help/spark/helpStagesWithFailedTasks.scala.html b/app/views/help/spark/helpStagesWithFailedTasks.scala.html
new file mode 100644
index 000000000..501366815
--- /dev/null
+++ b/app/views/help/spark/helpStagesWithFailedTasks.scala.html
@@ -0,0 +1,22 @@
+@*
+* Copyright 2016 LinkedIn Corp.
+*
+* Licensed under the Apache License, Version 2.0 (the "License"); you may not
+* use this file except in compliance with the License. You may obtain a copy of
+* the License at
+*
+* http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+* License for the specific language governing permissions and limitations under
+* the License.
+*@
+Tasks (and stages and jobs) can fail if an error occurs while the task is executing.
+
+In Spark, each stage is divided into tasks which run individually. Now a task may fail due to more than one reasons. Out of memory and overhead memory issues are the most common reasons.
+Due to errors in tasks, their corresponding stages might also fail. The reasons for tasks failure are analysed and suggestions are given.
+Suggestions
+Failed due to Overhead Memory issues: Please try increasing spark.yarn.executor.memoryOverhead by 500MB in spark.yarn.executor.memoryOverhead.
+Failed due to OOM: Try increasing spark.executor.memory or decreasing spark.memory.fraction (take a look at unified memory heuristic) or decreasing number of cores.
diff --git a/app/views/help/spark/helpUnifiedMemoryHeuristic.scala.html b/app/views/help/spark/helpUnifiedMemoryHeuristic.scala.html
new file mode 100644
index 000000000..352e9e3ff
--- /dev/null
+++ b/app/views/help/spark/helpUnifiedMemoryHeuristic.scala.html
@@ -0,0 +1,25 @@
+@*
+* Copyright 2016 LinkedIn Corp.
+*
+* Licensed under the Apache License, Version 2.0 (the "License"); you may not
+* use this file except in compliance with the License. You may obtain a copy of
+* the License at
+*
+* http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+* License for the specific language governing permissions and limitations under
+* the License.
+*@
+Peak Unified Memory Heuristic identifies and flags jobs which have over allocated Unified Memory region.
+Peak Unified Memory
+If the job's Peak Unified Memory Consumption is much smaller than the allocated Unified Memory space, then we recommend decreasing the allocated Unified Memory Region for your job.
+Action Items
+The Allocated Unified Memory Region can be reduced in the following ways:
+1. If your job's Executor Memory is already low, then reduce spark.memory.fraction which will reduce the amount of space allocated to the Unified Memory Region.
+2. If your job's Executor Memory is high, then we recommend reducing the spark.executor.memory itself which will lower the Allocated Unified Memory space.
+Note:
+spark.memory.fraction: This is the fraction of JVM Used Memory (Executor memory - Reserved memory) dedicated to the unified memory region (execution + storage). It basically partitions user memory from execution and storage memory.
+ Note: Please note that for calculation purposes Dr. Elephant considers 1024 Bytes in 1 KB whereas the spark history server considers 1000 Bytes. So please don't get confused if you find discrepancy in values from these two places.
\ No newline at end of file
diff --git a/conf/evolutions/default/2.sql b/conf/evolutions/default/2.sql
index 9f9e39e51..4f3d899b7 100644
--- a/conf/evolutions/default/2.sql
+++ b/conf/evolutions/default/2.sql
@@ -1,4 +1,20 @@
-# --- Indexing on queue for seach by queue feature
+#
+# Copyright 2016 LinkedIn Corp.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"); you may not
+# use this file except in compliance with the License. You may obtain a copy of
+# the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+# License for the specific language governing permissions and limitations under
+# the License.
+#
+
+# --- Indexing on queue for search by queue feature
# --- !Ups
create index yarn_app_result_i8 on yarn_app_result (queue_name);
diff --git a/conf/evolutions/default/3.sql b/conf/evolutions/default/3.sql
index a8cd34093..e01a3a8c8 100644
--- a/conf/evolutions/default/3.sql
+++ b/conf/evolutions/default/3.sql
@@ -1,3 +1,19 @@
+#
+# Copyright 2016 LinkedIn Corp.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"); you may not
+# use this file except in compliance with the License. You may obtain a copy of
+# the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+# License for the specific language governing permissions and limitations under
+# the License.
+#
+
# --- Indexing on queue for seach by queue feature
# --- !Ups
diff --git a/conf/evolutions/default/4.sql b/conf/evolutions/default/4.sql
index c3d9b844b..c9852e7e8 100644
--- a/conf/evolutions/default/4.sql
+++ b/conf/evolutions/default/4.sql
@@ -1,3 +1,19 @@
+#
+# Copyright 2016 LinkedIn Corp.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"); you may not
+# use this file except in compliance with the License. You may obtain a copy of
+# the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+# License for the specific language governing permissions and limitations under
+# the License.
+#
+
# --- Indexing on severity,finish_time for count on welcome page
# --- !Ups
diff --git a/conf/evolutions/default/5.sql b/conf/evolutions/default/5.sql
index 174d42ee0..640c292be 100644
--- a/conf/evolutions/default/5.sql
+++ b/conf/evolutions/default/5.sql
@@ -28,10 +28,12 @@ CREATE TABLE IF NOT EXISTS tuning_algorithm (
optimization_metric enum('RESOURCE','EXECUTION_TIME') DEFAULT NULL COMMENT 'metric to be optimized',
created_ts timestamp NOT NULL DEFAULT CURRENT_TIMESTAMP,
updated_ts timestamp NOT NULL DEFAULT CURRENT_TIMESTAMP ,
- PRIMARY KEY (id)
+ PRIMARY KEY (id),
+ UNIQUE KEY tuning_algorithm_u1 (optimization_algo, optimization_algo_version)
) ENGINE=InnoDB;
-INSERT INTO tuning_algorithm VALUES (1, 'PIG', 'PSO', '1', 'RESOURCE', current_timestamp(0), current_timestamp(0));
+INSERT INTO tuning_algorithm(id, job_type, optimization_algo, optimization_algo_version, optimization_metric, created_ts, updated_ts)
+VALUES (1, 'PIG', 'PSO', '1', 'RESOURCE', current_timestamp(0), current_timestamp(0));
/**
* This table represents hadoop parameters to be optimized for each algo in tuning_algorithm.
@@ -52,15 +54,16 @@ CREATE TABLE IF NOT EXISTS tuning_parameter (
CONSTRAINT tuning_parameter_ibfk_1 FOREIGN KEY (tuning_algorithm_id) REFERENCES tuning_algorithm (id)
) ENGINE=InnoDB;
-INSERT INTO tuning_parameter VALUES (1,'mapreduce.task.io.sort.mb',1,100,50,1920,50, 0, current_timestamp(0), current_timestamp(0));
-INSERT INTO tuning_parameter VALUES (2,'mapreduce.map.memory.mb',1,2048,1536,8192,128, 0, current_timestamp(0), current_timestamp(0));
-INSERT INTO tuning_parameter VALUES (3,'mapreduce.task.io.sort.factor',1,10,10,150,10 ,0, current_timestamp(0), current_timestamp(0));
-INSERT INTO tuning_parameter VALUES (4,'mapreduce.map.sort.spill.percent',1,0.8,0.6,0.9,0.1, 0, current_timestamp(0), current_timestamp(0));
-INSERT INTO tuning_parameter VALUES (5,'mapreduce.reduce.memory.mb',1,2048,1536,8192,128, 0, current_timestamp(0), current_timestamp(0));
-INSERT INTO tuning_parameter VALUES (6,'pig.maxCombinedSplitSize',1,536870912,536870912,536870912,128, 0, current_timestamp(0), current_timestamp(0));
-INSERT INTO tuning_parameter VALUES (7,'mapreduce.reduce.java.opts',1,1536,1152,6144,128, 1, current_timestamp(0), current_timestamp(0));
-INSERT INTO tuning_parameter VALUES (8,'mapreduce.map.java.opts',1,1536,1152,6144,128, 1, current_timestamp(0), current_timestamp(0));
-INSERT INTO tuning_parameter VALUES (9,'mapreduce.input.fileinputformat.split.maxsize',1,536870912,536870912,536870912,128, 1, current_timestamp(0), current_timestamp(0));
+INSERT INTO tuning_parameter (id, param_name, tuning_algorithm_id, default_value, min_value, max_value, step_size, is_derived, created_ts, updated_ts) VALUES
+(1,'mapreduce.task.io.sort.mb',1,100,50,1920,50, 0, current_timestamp(0), current_timestamp(0)),
+(2,'mapreduce.map.memory.mb',1,2048,1536,8192,128, 0, current_timestamp(0), current_timestamp(0)),
+(3,'mapreduce.task.io.sort.factor',1,10,10,150,10 ,0, current_timestamp(0), current_timestamp(0)),
+(4,'mapreduce.map.sort.spill.percent',1,0.8,0.6,0.9,0.1, 0, current_timestamp(0), current_timestamp(0)),
+(5,'mapreduce.reduce.memory.mb',1,2048,1536,8192,128, 0, current_timestamp(0), current_timestamp(0)),
+(6,'pig.maxCombinedSplitSize',1,536870912,536870912,536870912,128, 0, current_timestamp(0), current_timestamp(0)),
+(7,'mapreduce.reduce.java.opts',1,1536,1152,6144,128, 1, current_timestamp(0), current_timestamp(0)),
+(8,'mapreduce.map.java.opts',1,1536,1152,6144,128, 1, current_timestamp(0), current_timestamp(0)),
+(9,'mapreduce.input.fileinputformat.split.maxsize',1,536870912,536870912,536870912,128, 1, current_timestamp(0), current_timestamp(0));
create index index_tp_algo_id on tuning_parameter (tuning_algorithm_id);
@@ -71,8 +74,10 @@ CREATE TABLE IF NOT EXISTS flow_definition (
id int(10) unsigned NOT NULL AUTO_INCREMENT COMMENT 'Auto increment unique id',
flow_def_id varchar(700) NOT NULL COMMENT 'unique flow definition id from scheduler like azkaban, oozie, appworx etc',
flow_def_url varchar(700) NOT NULL COMMENT 'flow definition URL from scheduler like azkaban, oozie, appworx etc',
+ created_ts timestamp NOT NULL DEFAULT CURRENT_TIMESTAMP,
+ updated_ts timestamp NOT NULL DEFAULT CURRENT_TIMESTAMP,
PRIMARY KEY (id),
- UNIQUE KEY flow_def_id (flow_def_id)
+ UNIQUE KEY flow_definition_u1 (flow_def_id)
) ENGINE=InnoDB AUTO_INCREMENT=10000;
/**
@@ -83,15 +88,15 @@ CREATE TABLE IF NOT EXISTS flow_definition (
CREATE TABLE IF NOT EXISTS job_definition (
id int(10) unsigned NOT NULL AUTO_INCREMENT COMMENT 'Auto increment unique id',
job_def_id varchar(700) NOT NULL COMMENT 'unique job definition id from scheduler like azkaban, oozie etc',
+ job_def_url varchar(700) NOT NULL COMMENT 'job definition URL from scheduler like azkaban, oozie, appworx etc',
flow_definition_id int(10) unsigned NOT NULL COMMENT 'foreign key from flow_definition table',
job_name varchar(700) DEFAULT NULL COMMENT 'name of the job',
- job_def_url varchar(700) NOT NULL COMMENT 'job definition URL from scheduler like azkaban, oozie, appworx etc',
scheduler varchar(100) NOT NULL COMMENT 'name of the scheduler like azkaban. oozie ',
username varchar(100) NOT NULL COMMENT 'name of the user',
created_ts timestamp NOT NULL DEFAULT CURRENT_TIMESTAMP,
updated_ts timestamp NOT NULL DEFAULT CURRENT_TIMESTAMP,
PRIMARY KEY (id),
- UNIQUE KEY job_def_id (job_def_id) ,
+ UNIQUE KEY job_definition_u1 (job_def_id) ,
CONSTRAINT job_definition_ibfk_1 FOREIGN KEY (flow_definition_id) REFERENCES flow_definition (id)
) ENGINE=InnoDB AUTO_INCREMENT=100000;
@@ -110,6 +115,7 @@ CREATE TABLE IF NOT EXISTS tuning_job_definition (
average_input_size_in_bytes bigint(20) DEFAULT NULL COMMENT 'Average input size in bytes when optimization started on this job',
allowed_max_resource_usage_percent double DEFAULT NULL COMMENT 'Limit on resource usage, For ex 150 means it should not go beyond 150% ',
allowed_max_execution_time_percent double DEFAULT NULL COMMENT 'Limit on execution time, For ex 150 means it should not go beyond 150% ',
+ tuning_disabled_reason text NULL COMMENT 'reason for disabling tuning, if any',
created_ts timestamp NOT NULL DEFAULT CURRENT_TIMESTAMP,
updated_ts timestamp NOT NULL DEFAULT CURRENT_TIMESTAMP,
CONSTRAINT tuning_job_definition_ibfk_1 FOREIGN KEY (job_definition_id) REFERENCES job_definition (id),
@@ -127,6 +133,8 @@ CREATE TABLE IF NOT EXISTS flow_execution (
flow_exec_id varchar(700) NOT NULL COMMENT 'unique flow execution id from scheduler like azkaban, oozie etc ',
flow_exec_url varchar(700) NOT NULL COMMENT 'execution url from scheduler like azkaban, oozie etc',
flow_definition_id int(10) unsigned NOT NULL COMMENT 'foreign key from flow_definition table',
+ created_ts timestamp NOT NULL DEFAULT CURRENT_TIMESTAMP,
+ updated_ts timestamp NOT NULL DEFAULT CURRENT_TIMESTAMP,
PRIMARY KEY (id),
CONSTRAINT flow_execution_ibfk_1 FOREIGN KEY (flow_definition_id) REFERENCES flow_definition (id)
) ENGINE=InnoDB AUTO_INCREMENT=1000;
@@ -140,11 +148,11 @@ create index index_fe_flow_definition_id on flow_execution (flow_definition_id);
*/
CREATE TABLE IF NOT EXISTS job_execution (
id int(10) unsigned NOT NULL AUTO_INCREMENT COMMENT 'Auto increment unique id',
- job_exec_id varchar(700) DEFAULT NULL COMMENT 'unique job execution id from scheduler like azkaban, oozie etc',
- job_exec_url varchar(700) DEFAULT NULL COMMENT 'job execution url from scheduler like azkaban, oozie etc',
+ job_exec_id varchar(700) NOT NULL COMMENT 'unique job execution id from scheduler like azkaban, oozie etc',
+ job_exec_url varchar(700) NOT NULL COMMENT 'job execution url from scheduler like azkaban, oozie etc',
job_definition_id int(10) unsigned NOT NULL COMMENT 'foreign key from job_definition table',
- flow_execution_id int(10) unsigned DEFAULT NULL COMMENT 'foreign key from flow_execution table',
- execution_state enum('SUCCEEDED','FAILED','NOT_STARTED','IN_PROGRESS','CANCELLED') DEFAULT NULL COMMENT 'current state of execution of the job ',
+ flow_execution_id int(10) unsigned NOT NULL COMMENT 'foreign key from flow_execution table',
+ execution_state enum('SUCCEEDED','FAILED','NOT_STARTED','IN_PROGRESS','CANCELLED') NOT NULL COMMENT 'current state of execution of the job ',
resource_usage double DEFAULT NULL COMMENT 'resource usage in GB Hours for this execution of the job',
execution_time double DEFAULT NULL COMMENT 'execution time excluding delay for this execution of the job',
input_size_in_bytes bigint(20) DEFAULT NULL COMMENT 'input size in bytes for this execution of the job',
@@ -164,19 +172,28 @@ create index index_je_flow_execution_id on job_execution (flow_execution_id);
* This table represent jobs from one execution of a flow and contains auto tuning related information.
* This one execution is corresponding to one set of parameters.
*/
-CREATE TABLE IF NOT EXISTS tuning_job_execution (
- job_execution_id int(10) unsigned NOT NULL COMMENT 'foreign key from job_execution table',
+CREATE TABLE IF NOT EXISTS job_suggested_param_set (
+ id int(10) unsigned NOT NULL AUTO_INCREMENT COMMENT 'Auto increment unique id',
+ job_definition_id int(10) unsigned NOT NULL COMMENT 'foreign key from job_definition table',
tuning_algorithm_id int(10) unsigned NOT NULL COMMENT 'foreign key from tuning_algorithm table',
- param_set_state enum('CREATED','SENT','EXECUTED','FITNESS_COMPUTED','DISCARDED') DEFAULT NULL COMMENT 'state of this execution parameter set',
- is_default_execution tinyint(4) NOT NULL COMMENT 'Is this default execution',
+ param_set_state enum('CREATED','SENT','EXECUTED','FITNESS_COMPUTED','DISCARDED') NOT NULL COMMENT 'state of this execution parameter set',
+ are_constraints_violated tinyint(4) default 0 NOT NULL COMMENT 'are constraints violated for the parameter set',
+ is_param_set_default tinyint(4) DEFAULT 0 NOT NULL COMMENT 'Is parameter set default',
+ is_param_set_best tinyint(4) DEFAULT 0 NOT NULL COMMENT 'Is parameter set best',
fitness double DEFAULT NULL COMMENT 'fitness of this parameter set',
- UNIQUE KEY job_execution_id_2 (job_execution_id),
- CONSTRAINT tuning_job_execution_ibfk_1 FOREIGN KEY (tuning_algorithm_id) REFERENCES tuning_algorithm (id),
- CONSTRAINT tuning_job_execution_ibfk_2 FOREIGN KEY (job_execution_id) REFERENCES job_execution (id)
-) ENGINE=InnoDB ;
+ fitness_job_execution_id int(10) unsigned NULL COMMENT 'foreign key from job_execution table',
+ created_ts timestamp NOT NULL DEFAULT CURRENT_TIMESTAMP,
+ updated_ts timestamp NOT NULL DEFAULT CURRENT_TIMESTAMP,
+ PRIMARY KEY (id),
+ CONSTRAINT job_suggested_param_set_f1 FOREIGN KEY (tuning_algorithm_id) REFERENCES tuning_algorithm (id),
+ -- The following statement is commented as it leads to unit test failures though it works fine when deployed.
+ -- This is happening because unlike mysql, h2 database doesn't support nullable foreign keys.
+ -- CONSTRAINT job_suggested_param_set_f2 FOREIGN KEY (fitness_job_execution_id) REFERENCES job_execution (id),
+ CONSTRAINT job_suggested_param_set_f3 FOREIGN KEY (job_definition_id) REFERENCES job_definition (id)
+) ENGINE=InnoDB AUTO_INCREMENT=1000;
-create index index_tje_job_execution_id on tuning_job_execution (job_execution_id);
-create index index_tje_tuning_algorithm_id on tuning_job_execution (tuning_algorithm_id);
+create index index_tje_job_definition_id on job_suggested_param_set (job_definition_id);
+create index index_tje_tuning_algorithm_id on job_suggested_param_set (tuning_algorithm_id);
/**
* Internal table for optimization algorithm. Stores the current state of job to be optimized/
@@ -192,31 +209,47 @@ CREATE TABLE IF NOT EXISTS job_saved_state (
/**
- * Suggested parameter value corresponding to one execution of the job.
+ * Stores the suggested parameter value corresponding to one execution of the job.
*/
CREATE TABLE IF NOT EXISTS job_suggested_param_value (
id int(10) unsigned NOT NULL AUTO_INCREMENT COMMENT 'Auto increment unique id',
- job_execution_id int(10) unsigned NOT NULL COMMENT 'foreign key from job_execution table',
+ job_suggested_param_set_id int(10) unsigned NOT NULL COMMENT 'foreign key from job_suggested_param_set table',
tuning_parameter_id int(10) unsigned NOT NULL COMMENT 'foreign key from tuning_parameter table',
- param_value double NOT NULL COMMENT 'value of the parameter suggested by algo',
+ param_value double NOT NULL COMMENT 'suggested value of the parameter',
created_ts timestamp NOT NULL DEFAULT CURRENT_TIMESTAMP,
updated_ts timestamp NOT NULL DEFAULT CURRENT_TIMESTAMP,
PRIMARY KEY (id),
- UNIQUE KEY job_execution_id (job_execution_id,tuning_parameter_id),
- CONSTRAINT job_suggested_param_values_f1 FOREIGN KEY (job_execution_id) REFERENCES job_execution (id),
- CONSTRAINT job_suggested_param_values_f2 FOREIGN KEY (tuning_parameter_id) REFERENCES tuning_parameter (id)
-) ENGINE=InnoDB AUTO_INCREMENT=1000 ;
+ UNIQUE KEY job_suggested_param_value_u1 (job_suggested_param_set_id, tuning_parameter_id),
+ CONSTRAINT job_suggested_param_value_f1 FOREIGN KEY (job_suggested_param_set_id) REFERENCES job_suggested_param_set (id),
+ CONSTRAINT job_suggested_param_value_f2 FOREIGN KEY (tuning_parameter_id) REFERENCES tuning_parameter (id)
+) ENGINE=InnoDB AUTO_INCREMENT=1000;
create index index_jspv_tuning_parameter_id on job_suggested_param_value (tuning_parameter_id);
+/**
+ * Stores the mapping of job execution and corresponding parameter set
+ */
+
+CREATE TABLE IF NOT EXISTS tuning_job_execution_param_set (
+ job_suggested_param_set_id int(10) unsigned NOT NULL COMMENT 'foreign key from job_suggested_param_set table',
+ job_execution_id int(10) unsigned NOT NULL COMMENT 'foreign key from job_execution table',
+ tuning_enabled tinyint(4) NOT NULL COMMENT 'Is tuning enabled for the execution',
+ created_ts timestamp NOT NULL DEFAULT CURRENT_TIMESTAMP,
+ updated_ts timestamp NOT NULL DEFAULT CURRENT_TIMESTAMP,
+ UNIQUE KEY tuning_job_execution_param_set_u1 (job_suggested_param_set_id, job_execution_id),
+ CONSTRAINT tuning_job_execution_param_set_f1 FOREIGN KEY (job_suggested_param_set_id) REFERENCES job_suggested_param_set (id),
+ CONSTRAINT tuning_job_execution_param_set_f2 FOREIGN KEY (job_execution_id) REFERENCES job_execution (id)
+) ENGINE=InnoDB;
+
# --- !Downs
-drop table job_suggested_param_value ;
+drop table tuning_job_execution_param_set;
+drop table job_suggested_param_value;
drop table job_saved_state;
-drop table tuning_job_execution;
+drop table job_suggested_param_set;
drop table tuning_job_definition;
drop table job_execution;
drop table flow_execution;
drop table job_definition;
drop table flow_definition;
drop table tuning_parameter;
-drop table tuning_algorithm;
\ No newline at end of file
+drop table tuning_algorithm;
diff --git a/conf/evolutions/default/6.sql b/conf/evolutions/default/6.sql
deleted file mode 100644
index f16030727..000000000
--- a/conf/evolutions/default/6.sql
+++ /dev/null
@@ -1,11 +0,0 @@
-# --- Support for auto tuning spark
-# --- !Ups
-
-ALTER TABLE tuning_algorithm ADD UNIQUE KEY tuning_algorithm_uk1(optimization_algo, optimization_algo_version);
-ALTER TABLE tuning_job_execution ADD COLUMN is_param_set_best tinyint(4) default 0 NOT NULL;
-ALTER TABLE tuning_job_definition ADD COLUMN tuning_disabled_reason text;
-
-# --- !Downs
-ALTER TABLE tuning_job_definition DROP COLUMN tuning_disabled_reason;
-ALTER TABLE tuning_job_execution DROP COLUMN is_param_set_best;
-ALTER TABLE tuning_algorithm DROP INDEX tuning_algorithm_uk1;
\ No newline at end of file
diff --git a/test/com/linkedin/drelephant/spark/SparkMetricsAggregatorTest.scala b/test/com/linkedin/drelephant/spark/SparkMetricsAggregatorTest.scala
index 77e3e1d29..8d6a79024 100644
--- a/test/com/linkedin/drelephant/spark/SparkMetricsAggregatorTest.scala
+++ b/test/com/linkedin/drelephant/spark/SparkMetricsAggregatorTest.scala
@@ -17,14 +17,18 @@
package com.linkedin.drelephant.spark
import java.util.Date
+import java.util.Calendar
import scala.collection.JavaConverters
import com.linkedin.drelephant.analysis.ApplicationType
import com.linkedin.drelephant.configurations.aggregator.AggregatorConfigurationData
+import com.linkedin.drelephant.math.Statistics
import com.linkedin.drelephant.spark.data.{SparkApplicationData, SparkLogDerivedData, SparkRestDerivedData}
import com.linkedin.drelephant.spark.fetchers.statusapiv1.{ApplicationAttemptInfoImpl, ApplicationInfoImpl, ExecutorSummaryImpl}
+import com.linkedin.drelephant.util.MemoryFormatUtils
import org.apache.spark.scheduler.SparkListenerEnvironmentUpdate
+import org.apache.commons.io.FileUtils
import org.scalatest.{FunSpec, Matchers}
class SparkMetricsAggregatorTest extends FunSpec with Matchers {
@@ -47,19 +51,21 @@ class SparkMetricsAggregatorTest extends FunSpec with Matchers {
}
val executorSummaries = Seq(
- newFakeExecutorSummary(id = "1", totalDuration = 1000000L),
- newFakeExecutorSummary(id = "2", totalDuration = 3000000L)
+ newFakeExecutorSummary(id = "1", totalDuration = 1000000L, Map("jvmUsedMemory" -> 394567123)),
+ newFakeExecutorSummary(id = "2", totalDuration = 3000000L, Map("jvmUsedMemory" -> 23456834))
+
)
val restDerivedData = {
SparkRestDerivedData(
applicationInfo,
jobDatas = Seq.empty,
stageDatas = Seq.empty,
- executorSummaries = executorSummaries
+ executorSummaries = executorSummaries,
+ stagesWithFailedTasks = Seq.empty
)
}
- describe("when it has log-derived data") {
+ describe("when it has data") {
val logDerivedData = {
val environmentUpdate = newFakeSparkListenerEnvironmentUpdate(
Map(
@@ -81,31 +87,20 @@ class SparkMetricsAggregatorTest extends FunSpec with Matchers {
val result = aggregator.getResult
- it("calculates resources used") {
- val totalExecutorMemoryMb = 2 * 4096
- val applicationDurationSeconds = 8000
- val executorMemoryMb = 4096
- val totalExecutorTaskTimeSeconds = 1000 + 3000
- result.getResourceUsed should be(totalExecutorMemoryMb * applicationDurationSeconds)
+ it("calculates resources used (allocated)") {
+ result.getResourceUsed should be(4096000+12288000)
}
it("calculates resources wasted") {
- val totalExecutorMemoryMb = 2 * 4096
- val applicationDurationSeconds = 8000
- val resourceAllocated = totalExecutorMemoryMb * applicationDurationSeconds;
-
- val executorMemoryMb = 4096
- val totalExecutorTaskTimeSeconds = 1000 + 3000
- val resourceUsed = executorMemoryMb * totalExecutorTaskTimeSeconds;
-
-
- result.getResourceWasted should be(resourceAllocated - resourceUsed * 1.5)
+ val resourceAllocated = 4096000+12288000
+ val resourceUsed = 676288+967110
+ result.getResourceWasted should be(resourceAllocated.toDouble - resourceUsed.toDouble * 1.5)
}
it("doesn't calculate total delay") {
result.getTotalDelay should be(0L)
}
- it("sets resourceused as 0 when duration is negative") {
+ it("sets resource used as 0 when duration is negative") {
//make the duration negative
val applicationInfo = {
val applicationAttemptInfo = {
@@ -119,7 +114,8 @@ class SparkMetricsAggregatorTest extends FunSpec with Matchers {
applicationInfo,
jobDatas = Seq.empty,
stageDatas = Seq.empty,
- executorSummaries = executorSummaries
+ executorSummaries = executorSummaries,
+ stagesWithFailedTasks = Seq.empty
)
val data = SparkApplicationData(appId, restDerivedData, Some(logDerivedData))
@@ -178,7 +174,8 @@ object SparkMetricsAggregatorTest {
def newFakeExecutorSummary(
id: String,
- totalDuration: Long
+ totalDuration: Long,
+ peakJvmUsedMemory: Map[String, Long]
): ExecutorSummaryImpl = new ExecutorSummaryImpl(
id,
hostPort = "",
@@ -189,12 +186,18 @@ object SparkMetricsAggregatorTest {
failedTasks = 0,
completedTasks = 0,
totalTasks = 0,
+ maxTasks = 0,
totalDuration,
+ addTime = Calendar.getInstance().getTime,
+ endTime = None,
totalInputBytes = 0,
totalShuffleRead = 0,
totalShuffleWrite = 0,
maxMemory = 0,
totalGCTime = 0,
- executorLogs = Map.empty
+ totalMemoryBytesSpilled = 0,
+ executorLogs = Map.empty,
+ peakJvmUsedMemory,
+ peakUnifiedMemory = Map.empty
)
}
diff --git a/test/com/linkedin/drelephant/spark/data/SparkApplicationDataTest.scala b/test/com/linkedin/drelephant/spark/data/SparkApplicationDataTest.scala
index e6ec6d51d..e44c319ff 100644
--- a/test/com/linkedin/drelephant/spark/data/SparkApplicationDataTest.scala
+++ b/test/com/linkedin/drelephant/spark/data/SparkApplicationDataTest.scala
@@ -42,7 +42,8 @@ class SparkApplicationDataTest extends FunSpec with Matchers {
new ApplicationInfoImpl(appId, "app", Seq(applicationAttemptInfo)),
jobDatas = Seq.empty,
stageDatas = Seq.empty,
- executorSummaries = Seq.empty
+ executorSummaries = Seq.empty,
+ stagesWithFailedTasks = Seq.empty
)
val configurationProperties = Map(
diff --git a/test/com/linkedin/drelephant/spark/fetchers/SparkFetcherTest.scala b/test/com/linkedin/drelephant/spark/fetchers/SparkFetcherTest.scala
index 80476ded2..22cbf10d0 100644
--- a/test/com/linkedin/drelephant/spark/fetchers/SparkFetcherTest.scala
+++ b/test/com/linkedin/drelephant/spark/fetchers/SparkFetcherTest.scala
@@ -59,7 +59,8 @@ class SparkFetcherTest extends FunSpec with Matchers with MockitoSugar {
),
jobDatas = Seq.empty,
stageDatas = Seq.empty,
- executorSummaries = Seq.empty
+ executorSummaries = Seq.empty,
+ stagesWithFailedTasks = Seq.empty
)
val logDerivedData = SparkLogDerivedData(SparkListenerEnvironmentUpdate(Map.empty))
diff --git a/test/com/linkedin/drelephant/spark/fetchers/SparkRestClientTest.scala b/test/com/linkedin/drelephant/spark/fetchers/SparkRestClientTest.scala
index 7b79fbfd2..c20223fb8 100644
--- a/test/com/linkedin/drelephant/spark/fetchers/SparkRestClientTest.scala
+++ b/test/com/linkedin/drelephant/spark/fetchers/SparkRestClientTest.scala
@@ -172,14 +172,13 @@ class SparkRestClientTest extends AsyncFunSpec with Matchers {
.register(classOf[FetchClientModeDataFixtures.StagesResource])
.register(classOf[FetchClientModeDataFixtures.ExecutorsResource])
.register(classOf[FetchClientModeDataFixtures.LogsResource])
+ .register(classOf[FetchClientModeDataFixtures.StagesWithFailedTasksResource])
case config => config
}
}
-
fakeJerseyServer.setUp()
val historyServerUri = fakeJerseyServer.target.getUri
-
val sparkConf = new SparkConf().set("spark.yarn.historyServer.address", s"${historyServerUri.getHost}:${historyServerUri.getPort}")
val sparkRestClient = new SparkRestClient(sparkConf)
@@ -304,11 +303,14 @@ object SparkRestClientTest {
@Path("applications/{appId}/{attemptId}/stages")
def getStages(): StagesResource = new StagesResource()
- @Path("applications/{appId}/{attemptId}/executors")
+ @Path("applications/{appId}/{attemptId}/allexecutors")
def getExecutors(): ExecutorsResource = new ExecutorsResource()
@Path("applications/{appId}/{attemptId}/logs")
def getLogs(): LogsResource = new LogsResource()
+
+ @Path("applications/{appId}/{attemptId}/stages/failedTasks")
+ def getStagesWithFailedTasks(): StagesWithFailedTasksResource = new StagesWithFailedTasksResource()
}
@Produces(Array(MediaType.APPLICATION_JSON))
@@ -359,6 +361,13 @@ object SparkRestClientTest {
} else throw new Exception()
}
}
+
+ @Produces(Array(MediaType.APPLICATION_JSON))
+ class StagesWithFailedTasksResource {
+ @GET
+ def getStagesWithFailedTasks(@PathParam("appId") appId: String, @PathParam("attemptId") attemptId: String): Seq[StageDataImpl] =
+ if (attemptId == "2") Seq.empty else throw new Exception()
+ }
}
object FetchClientModeDataFixtures {
@@ -376,11 +385,14 @@ object SparkRestClientTest {
@Path("applications/{appId}/stages")
def getStages(): StagesResource = new StagesResource()
- @Path("applications/{appId}/executors")
+ @Path("applications/{appId}/allexecutors")
def getExecutors(): ExecutorsResource = new ExecutorsResource()
@Path("applications/{appId}/logs")
def getLogs(): LogsResource = new LogsResource()
+
+ @Path("applications/{appId}/stages/failedTasks")
+ def getStagesWithFailedTasks(): StagesWithFailedTasksResource = new StagesWithFailedTasksResource()
}
@Produces(Array(MediaType.APPLICATION_JSON))
@@ -429,6 +441,13 @@ object SparkRestClientTest {
Response.ok(newFakeLog(appId, None)).build()
}
}
+
+ @Produces(Array(MediaType.APPLICATION_JSON))
+ class StagesWithFailedTasksResource {
+ @GET
+ def getStagesWithFailedTasks(@PathParam("appId") appId: String): Seq[StageDataImpl] =
+ Seq.empty
+ }
}
def newFakeApplicationAttemptInfo(
diff --git a/test/com/linkedin/drelephant/spark/heuristics/ConfigurationHeuristicTest.scala b/test/com/linkedin/drelephant/spark/heuristics/ConfigurationHeuristicTest.scala
index f61d741e2..80be4541c 100644
--- a/test/com/linkedin/drelephant/spark/heuristics/ConfigurationHeuristicTest.scala
+++ b/test/com/linkedin/drelephant/spark/heuristics/ConfigurationHeuristicTest.scala
@@ -27,7 +27,9 @@ import org.apache.spark.scheduler.SparkListenerEnvironmentUpdate
import org.scalatest.{FunSpec, Matchers}
import java.util.Date
-
+/**
+ * Test class for Configuration Heuristic. It checks whether all the values used in the heuristic are calculated correctly.
+ */
class ConfigurationHeuristicTest extends FunSpec with Matchers {
import ConfigurationHeuristicTest._
@@ -60,67 +62,50 @@ class ConfigurationHeuristicTest extends FunSpec with Matchers {
val heuristicResultDetails = heuristicResult.getHeuristicResultDetails
it("returns the size of result details") {
- heuristicResultDetails.size() should be(9)
+ heuristicResultDetails.size() should be(8)
}
it("returns the severity") {
heuristicResult.getSeverity should be(Severity.NONE)
}
- it("returns the driver memory") {
- val details = heuristicResultDetails.get(0)
- details.getName should include("spark.driver.memory")
- details.getValue should be("2 GB")
- }
-
it("returns the executor memory") {
- val details = heuristicResultDetails.get(1)
+ val details = heuristicResultDetails.get(0)
details.getName should include("spark.executor.memory")
details.getValue should be("1 GB")
}
it("returns the executor instances") {
- val details = heuristicResultDetails.get(2)
+ val details = heuristicResultDetails.get(1)
details.getName should include("spark.executor.instances")
details.getValue should be("900")
}
it("returns the executor cores") {
- val details = heuristicResultDetails.get(3)
+ val details = heuristicResultDetails.get(2)
details.getName should include("spark.executor.cores")
details.getValue should include("default")
}
it("returns the application duration") {
- val details = heuristicResultDetails.get(4)
+ val details = heuristicResultDetails.get(3)
details.getName should include("spark.application.duration")
details.getValue should include("10")
}
it("returns the dynamic allocation flag") {
- val details = heuristicResultDetails.get(5)
+ val details = heuristicResultDetails.get(4)
details.getName should include("spark.dynamicAllocation.enabled")
details.getValue should be("true")
}
-
- it("returns the driver cores") {
- val details = heuristicResultDetails.get(6)
- details.getName should include("spark.driver.cores")
- details.getValue should include("default")
- }
-
- it("returns the driver overhead memory") {
- val details = heuristicResultDetails.get(7)
- details.getName should include("spark.yarn.driver.memoryOverhead")
- details.getValue should include("500 MB")
- }
}
describe("apply with Severity") {
val configurationProperties = Map(
"spark.serializer" -> "dummySerializer",
"spark.shuffle.service.enabled" -> "false",
- "spark.dynamicAllocation.enabled" -> "true"
+ "spark.dynamicAllocation.enabled" -> "true",
+ "spark.executor.cores" -> "5"
)
val data = newFakeSparkApplicationData(configurationProperties)
@@ -136,24 +121,30 @@ class ConfigurationHeuristicTest extends FunSpec with Matchers {
}
it("returns the dynamic allocation flag") {
- val details = heuristicResultDetails.get(5)
+ val details = heuristicResultDetails.get(4)
details.getName should include("spark.dynamicAllocation.enabled")
details.getValue should be("true")
}
it("returns the serializer") {
- val details = heuristicResultDetails.get(9)
+ val details = heuristicResultDetails.get(8)
details.getName should include("spark.serializer")
details.getValue should be("dummySerializer")
details.getDetails should be("KyroSerializer is Not Enabled.")
}
it("returns the shuffle service flag") {
- val details = heuristicResultDetails.get(10)
+ val details = heuristicResultDetails.get(9)
details.getName should include("spark.shuffle.service.enabled")
details.getValue should be("false")
details.getDetails should be("Spark shuffle service is not enabled.")
}
+
+ it("returns executor cores") {
+ val details = heuristicResultDetails.get(10)
+ details.getName should include("Executor cores")
+ details.getValue should be("The number of executor cores should be <5. Please change it in the field spark.executor.cores")
+ }
}
describe(".Evaluator") {
@@ -163,16 +154,6 @@ class ConfigurationHeuristicTest extends FunSpec with Matchers {
new Evaluator(configurationHeuristic, newFakeSparkApplicationData(configurationProperties))
}
- it("has the driver memory bytes when they're present") {
- val evaluator = newEvaluatorWithConfigurationProperties(Map("spark.driver.memory" -> "2G"))
- evaluator.driverMemoryBytes should be(Some(2L * 1024 * 1024 * 1024))
- }
-
- it("has no driver memory bytes when they're absent") {
- val evaluator = newEvaluatorWithConfigurationProperties(Map.empty)
- evaluator.driverMemoryBytes should be(None)
- }
-
it("has the executor memory bytes when they're present") {
val evaluator = newEvaluatorWithConfigurationProperties(Map("spark.executor.memory" -> "1g"))
evaluator.executorMemoryBytes should be(Some(1L * 1024 * 1024 * 1024))
@@ -198,21 +179,11 @@ class ConfigurationHeuristicTest extends FunSpec with Matchers {
evaluator.executorCores should be(Some(2))
}
- it("has the driver cores when they're present") {
- val evaluator = newEvaluatorWithConfigurationProperties(Map("spark.driver.cores" -> "3"))
- evaluator.driverCores should be(Some(3))
- }
-
it("has no executor cores when they're absent") {
val evaluator = newEvaluatorWithConfigurationProperties(Map.empty)
evaluator.executorCores should be(None)
}
- it("has no driver cores when they're absent") {
- val evaluator = newEvaluatorWithConfigurationProperties(Map.empty)
- evaluator.driverCores should be(None)
- }
-
it("has the serializer when it's present") {
val evaluator = newEvaluatorWithConfigurationProperties(Map("spark.serializer" -> "org.apache.spark.serializer.KryoSerializer"))
evaluator.serializer should be(Some("org.apache.spark.serializer.KryoSerializer"))
@@ -327,7 +298,8 @@ object ConfigurationHeuristicTest {
new ApplicationInfoImpl(appId, name = "app", applicationAttempts),
jobDatas = Seq.empty,
stageDatas = Seq.empty,
- executorSummaries = Seq.empty
+ executorSummaries = Seq.empty,
+ stagesWithFailedTasks = Seq.empty
)
SparkApplicationData(appId, restDerivedData, Some(logDerivedData))
diff --git a/test/com/linkedin/drelephant/spark/heuristics/DriverHeuristicTest.scala b/test/com/linkedin/drelephant/spark/heuristics/DriverHeuristicTest.scala
new file mode 100644
index 000000000..a61256455
--- /dev/null
+++ b/test/com/linkedin/drelephant/spark/heuristics/DriverHeuristicTest.scala
@@ -0,0 +1,107 @@
+package com.linkedin.drelephant.spark.heuristics
+
+import com.linkedin.drelephant.analysis.{ApplicationType, Severity}
+import com.linkedin.drelephant.configurations.heuristic.HeuristicConfigurationData
+import com.linkedin.drelephant.spark.data.{SparkApplicationData, SparkRestDerivedData}
+import com.linkedin.drelephant.spark.fetchers.statusapiv1.{ApplicationInfoImpl, ExecutorSummaryImpl}
+import com.linkedin.drelephant.spark.heuristics.DriverHeuristic.Evaluator
+import org.scalatest.{FunSpec, Matchers}
+
+import scala.collection.JavaConverters
+import scala.concurrent.duration.Duration
+import java.util.Calendar
+
+/**
+ * Test class for Driver Metrics Heuristic. It checks whether all the values used in the heuristic are calculated correctly.
+ */
+class DriverHeuristicTest extends FunSpec with Matchers {
+
+ import DriverHeuristicTest._
+
+ val heuristicConfigurationData = newFakeHeuristicConfigurationData()
+
+ val driverHeuristic = new DriverHeuristic(heuristicConfigurationData)
+
+ val executorData = Seq(
+ newDummyExecutorData("1", 400000, Map("executionMemory" -> 300000, "storageMemory" -> 94567), null, 0, 0),
+ newDummyExecutorData("2", 400000, Map("executionMemory" -> 200000, "storageMemory" -> 34568), null, 0, 0),
+ newDummyExecutorData("3", 400000, Map("executionMemory" -> 300000, "storageMemory" -> 34569), null, 0, 0),
+ newDummyExecutorData("4", 400000, Map("executionMemory" -> 20000, "storageMemory" -> 3456), null, 0, 0),
+ newDummyExecutorData("5", 400000, Map("executionMemory" -> 200000, "storageMemory" -> 34564), null, 0, 0),
+ newDummyExecutorData("driver", 400000, Map("executionMemory" -> 300000, "storageMemory" -> 94561), Map("jvmUsedMemory" -> 394567123),
+ totalGCTime = Duration("2min").toMillis, totalDuration = Duration("15min").toMillis)
+ )
+ describe(".apply") {
+ val data = newFakeSparkApplicationData(executorData)
+ val heuristicResult = driverHeuristic.apply(data)
+ val heuristicResultDetails = heuristicResult.getHeuristicResultDetails
+
+ it("has severity") {
+ heuristicResult.getSeverity should be(Severity.SEVERE)
+ }
+
+ describe("Evaluator") {
+ val evaluator = new Evaluator(driverHeuristic, data)
+ it("has max driver peak JVM memory") {
+ evaluator.maxDriverPeakJvmUsedMemory should be(394567123)
+ }
+ it("ratio of time spend in Gc to total duration") {
+ evaluator.ratio should be(0.13333333333333333)
+ }
+ }
+ }
+}
+
+object DriverHeuristicTest {
+
+ import JavaConverters._
+
+ def newFakeHeuristicConfigurationData(params: Map[String, String] = Map.empty): HeuristicConfigurationData =
+ new HeuristicConfigurationData("heuristic", "class", "view", new ApplicationType("type"), params.asJava)
+
+ def newDummyExecutorData(
+ id: String,
+ maxMemory: Long,
+ peakUnifiedMemory: Map[String, Long],
+ peakJvmUsedMemory: Map[String, Long],
+ totalGCTime: Long,
+ totalDuration: Long
+ ): ExecutorSummaryImpl = new ExecutorSummaryImpl(
+ id,
+ hostPort = "",
+ rddBlocks = 0,
+ memoryUsed = 0,
+ diskUsed = 0,
+ activeTasks = 0,
+ failedTasks = 0,
+ completedTasks = 0,
+ totalTasks = 0,
+ maxTasks = 0,
+ totalDuration,
+ addTime = Calendar.getInstance().getTime,
+ endTime = None,
+ totalInputBytes = 0,
+ totalShuffleRead = 0,
+ totalShuffleWrite = 0,
+ maxMemory,
+ totalGCTime,
+ totalMemoryBytesSpilled = 0,
+ executorLogs = Map.empty,
+ peakJvmUsedMemory,
+ peakUnifiedMemory
+ )
+
+ def newFakeSparkApplicationData(executorSummaries: Seq[ExecutorSummaryImpl]): SparkApplicationData = {
+ val appId = "application_1"
+ val restDerivedData = SparkRestDerivedData(
+ new ApplicationInfoImpl(appId, name = "app", Seq.empty),
+ jobDatas = Seq.empty,
+ stageDatas = Seq.empty,
+ executorSummaries = executorSummaries,
+ stagesWithFailedTasks = Seq.empty
+ )
+
+ SparkApplicationData(appId, restDerivedData, logDerivedData = None)
+ }
+}
+
diff --git a/test/com/linkedin/drelephant/spark/heuristics/ExecutorGcHeuristicTest.scala b/test/com/linkedin/drelephant/spark/heuristics/ExecutorGcHeuristicTest.scala
index 869b9cb67..2fa1dc39a 100644
--- a/test/com/linkedin/drelephant/spark/heuristics/ExecutorGcHeuristicTest.scala
+++ b/test/com/linkedin/drelephant/spark/heuristics/ExecutorGcHeuristicTest.scala
@@ -20,58 +20,150 @@ import scala.collection.JavaConverters
import com.linkedin.drelephant.analysis.{ApplicationType, Severity, SeverityThresholds}
import com.linkedin.drelephant.configurations.heuristic.HeuristicConfigurationData
import com.linkedin.drelephant.spark.data.{SparkApplicationData, SparkLogDerivedData, SparkRestDerivedData}
-import com.linkedin.drelephant.spark.fetchers.statusapiv1.{ApplicationInfoImpl, ExecutorSummaryImpl, StageDataImpl}
+import com.linkedin.drelephant.spark.fetchers.statusapiv1.{ApplicationAttemptInfoImpl, ApplicationInfoImpl, ExecutorSummaryImpl, StageDataImpl}
import org.apache.spark.scheduler.SparkListenerEnvironmentUpdate
import org.scalatest.{FunSpec, Matchers}
-
+import java.util.{Calendar,Date}
import scala.concurrent.duration.Duration
-
+/**
+ * Test class for Executor GC Heuristic. It checks whether all the values used in the heuristic are calculated correctly.
+ */
class ExecutorGcHeuristicTest extends FunSpec with Matchers {
import ExecutorGcHeuristicTest._
describe("ExecutorGcHeuristic") {
- val heuristicConfigurationData = newFakeHeuristicConfigurationData(
- Map(
- "max_to_median_ratio_severity_thresholds" -> "1.414,2,4,16",
- "ignore_max_bytes_less_than_threshold" -> "4000000",
- "ignore_max_millis_less_than_threshold" -> "4000001"
- )
- )
+ val heuristicConfigurationData = newFakeHeuristicConfigurationData()
val executorGcHeuristic = new ExecutorGcHeuristic(heuristicConfigurationData)
+ val dateArray : Array[Date] = new Array[Date](20)
+ val cal: Calendar = Calendar.getInstance()
+ dateArray(0) = cal.getTime
+ cal.add(Calendar.MILLISECOND,700)
+ dateArray(1) = cal.getTime
+ cal.add(Calendar.MILLISECOND,300)
+ for(i <- 2 to 11)
+ {
+ dateArray(i) = cal.getTime
+ cal.add(Calendar.MINUTE,1)
+ }
+ for(i <- 12 to 19)
+ {
+ dateArray(i) = cal.getTime
+ cal.add(Calendar.MINUTE,5)
+ }
+ val appAttemptInfo = Seq(
+ newFakeApplicationAttemptInfo(endTime = dateArray(14))
+ )
+
+ val appAttemptInfo1 = Seq(
+ newFakeApplicationAttemptInfo(endTime = dateArray(1))
+ )
+ val appAttemptInfo2 = Seq(
+ newFakeApplicationAttemptInfo(endTime = dateArray(6))
+ )
+
+ val appAttemptInfo3 = Seq(
+ newFakeApplicationAttemptInfo(endTime = dateArray(9))
+ )
val executorSummaries = Seq(
newFakeExecutorSummary(
id = "1",
totalGCTime = Duration("2min").toMillis,
- totalDuration = Duration("15min").toMillis
+ totalDuration = Duration("15min").toMillis,
+ addTime = dateArray(12),
+ endTime = Option(dateArray(15))
),
newFakeExecutorSummary(
id = "2",
totalGCTime = Duration("6min").toMillis,
- totalDuration = Duration("14min").toMillis
+ totalDuration = Duration("14min").toMillis,
+ addTime = dateArray(8),
+ endTime = None
),
newFakeExecutorSummary(
id = "3",
totalGCTime = Duration("4min").toMillis,
- totalDuration = Duration("20min").toMillis
- ),
+ totalDuration = Duration("20min").toMillis,
+ addTime = dateArray(2),
+ endTime = None
+ ),
newFakeExecutorSummary(
id = "4",
totalGCTime = Duration("8min").toMillis,
- totalDuration = Duration("30min").toMillis
+ totalDuration = Duration("30min").toMillis,
+ addTime = dateArray(13),
+ endTime = Option(dateArray(19))
+ )
+ )
+
+ val executorSummaries1 = Seq(
+ newFakeExecutorSummary(
+ id = "1",
+ totalGCTime = 500,
+ totalDuration = 700,
+ addTime = dateArray(0),
+ endTime = Option(dateArray(1))
+ )
+ )
+
+ val executorSummaries2 = Seq(
+ newFakeExecutorSummary(
+ id = "1",
+ totalGCTime = 12000,
+ totalDuration = Duration("4min").toMillis,
+ addTime = dateArray(2),
+ endTime = None
+ ),
+ newFakeExecutorSummary(
+ id = "2",
+ totalGCTime = 13000,
+ totalDuration = Duration("1min").toMillis,
+ addTime = dateArray(5),
+ endTime = None
+ )
+ )
+
+ val executorSummaries3 = Seq(
+ newFakeExecutorSummary(
+ id = "1",
+ totalGCTime = 9000,
+ totalDuration = Duration("2min").toMillis,
+ addTime = dateArray(7),
+ endTime = None
)
)
describe(".apply") {
- val data1 = newFakeSparkApplicationData(executorSummaries)
- val heuristicResult = executorGcHeuristic.apply(data1)
+ val data = newFakeSparkApplicationData(executorSummaries, appAttemptInfo)
+ val data1 = newFakeSparkApplicationData(executorSummaries1, appAttemptInfo1)
+ val data2 = newFakeSparkApplicationData(executorSummaries2, appAttemptInfo2)
+ val data3 = newFakeSparkApplicationData(executorSummaries3, appAttemptInfo3)
+ val heuristicResult = executorGcHeuristic.apply(data)
+ val heuristicResult1 = executorGcHeuristic.apply(data1)
+ val heuristicResult2 = executorGcHeuristic.apply(data2)
+ val heuristicResult3 = executorGcHeuristic.apply(data3)
val heuristicResultDetails = heuristicResult.getHeuristicResultDetails
+ val heuristicResultDetails1 = heuristicResult1.getHeuristicResultDetails
+ val heuristicResultDetails2 = heuristicResult2.getHeuristicResultDetails
+ val heuristicResultDetails3 = heuristicResult3.getHeuristicResultDetails
it("returns the severity") {
heuristicResult.getSeverity should be(Severity.CRITICAL)
}
+ it("return the low severity") {
+ heuristicResult2.getSeverity should be(Severity.LOW)
+ }
+
+ it("return NONE severity for runtime less than 5 min") {
+ heuristicResult2.getSeverity should be(Severity.LOW)
+ }
+
+ it("return none severity") {
+ heuristicResult3.getSeverity should be(Severity.NONE)
+ }
+
it("returns the JVM GC time to Executor Run time duration") {
val details = heuristicResultDetails.get(0)
details.getName should include("GC time to Executor Run time ratio")
@@ -81,13 +173,25 @@ class ExecutorGcHeuristicTest extends FunSpec with Matchers {
it("returns the total GC time") {
val details = heuristicResultDetails.get(1)
details.getName should include("Total GC time")
- details.getValue should be("1200000")
+ details.getValue should be("20 Minutes")
}
it("returns the executor's run time") {
val details = heuristicResultDetails.get(2)
details.getName should include("Total Executor Runtime")
- details.getValue should be("4740000")
+ details.getValue should be("1 Hours 19 Minutes")
+ }
+
+ it("returns total Gc Time in millisec") {
+ val details = heuristicResultDetails1.get(1)
+ details.getName should include("Total GC time")
+ details.getValue should be("500 msec")
+ }
+
+ it("returns executor run Time in millisec") {
+ val details = heuristicResultDetails1.get(2)
+ details.getName should include("Total Executor Runtime")
+ details.getValue should be("700 msec")
}
}
}
@@ -102,7 +206,9 @@ object ExecutorGcHeuristicTest {
def newFakeExecutorSummary(
id: String,
totalGCTime: Long,
- totalDuration: Long
+ totalDuration: Long,
+ addTime: Date,
+ endTime: Option[Date]
): ExecutorSummaryImpl = new ExecutorSummaryImpl(
id,
hostPort = "",
@@ -113,25 +219,41 @@ object ExecutorGcHeuristicTest {
failedTasks = 0,
completedTasks = 0,
totalTasks = 0,
+ maxTasks = 0,
totalDuration,
+ addTime,
+ endTime,
totalInputBytes=0,
totalShuffleRead=0,
totalShuffleWrite= 0,
- maxMemory= 0,
+ maxMemory = 0,
totalGCTime,
- executorLogs = Map.empty
+ totalMemoryBytesSpilled = 0,
+ executorLogs = Map.empty,
+ peakJvmUsedMemory = Map.empty,
+ peakUnifiedMemory = Map.empty
)
+ def newFakeApplicationAttemptInfo(
+ endTime: Date
+ ): ApplicationAttemptInfoImpl = new ApplicationAttemptInfoImpl(
+ attemptId = Option("attemptId_1"),
+ startTime= Calendar.getInstance().getTime,
+ endTime,
+ sparkUser = "",
+ completed = true
+ )
def newFakeSparkApplicationData(
- executorSummaries: Seq[ExecutorSummaryImpl]
+ executorSummaries: Seq[ExecutorSummaryImpl] , appAttemptInfo: Seq[ApplicationAttemptInfoImpl]
): SparkApplicationData = {
val appId = "application_1"
val restDerivedData = SparkRestDerivedData(
- new ApplicationInfoImpl(appId, name = "app", Seq.empty),
+ new ApplicationInfoImpl(appId, name = "app", appAttemptInfo),
jobDatas = Seq.empty,
stageDatas = Seq.empty,
- executorSummaries = executorSummaries
+ executorSummaries = executorSummaries,
+ stagesWithFailedTasks = Seq.empty
)
SparkApplicationData(appId, restDerivedData, None)
}
diff --git a/test/com/linkedin/drelephant/spark/heuristics/ExecutorStorageSpillHeuristicTest.scala b/test/com/linkedin/drelephant/spark/heuristics/ExecutorStorageSpillHeuristicTest.scala
new file mode 100644
index 000000000..a0a8d802d
--- /dev/null
+++ b/test/com/linkedin/drelephant/spark/heuristics/ExecutorStorageSpillHeuristicTest.scala
@@ -0,0 +1,151 @@
+/*
+ * Copyright 2016 LinkedIn Corp.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License"); you may not
+ * use this file except in compliance with the License. You may obtain a copy of
+ * the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+ * License for the specific language governing permissions and limitations under
+ * the License.
+ */
+
+package com.linkedin.drelephant.spark.heuristics
+
+import scala.collection.JavaConverters
+import com.linkedin.drelephant.analysis.{ApplicationType, Severity, SeverityThresholds}
+import com.linkedin.drelephant.configurations.heuristic.HeuristicConfigurationData
+import com.linkedin.drelephant.spark.data.{SparkApplicationData, SparkLogDerivedData, SparkRestDerivedData}
+import com.linkedin.drelephant.spark.fetchers.statusapiv1.{ApplicationInfoImpl, ExecutorSummaryImpl, StageDataImpl}
+import com.linkedin.drelephant.spark.heuristics.ExecutorStorageSpillHeuristic.Evaluator
+import org.apache.spark.scheduler.SparkListenerEnvironmentUpdate
+import java.util.Calendar
+import org.scalatest.{FunSpec, Matchers}
+
+/**
+ * Test class for Executor Storage Spill Heuristic. It checks whether all the values used in the heuristic are calculated correctly.
+ */
+class ExecutorStorageSpillHeuristicTest extends FunSpec with Matchers {
+ import ExecutorStorageSpillHeuristicTest._
+
+ describe("ExecutorStorageSpillHeuristic") {
+ val heuristicConfigurationData = newFakeHeuristicConfigurationData(
+ Map.empty
+ )
+ val executorStorageSpillHeuristic = new ExecutorStorageSpillHeuristic(heuristicConfigurationData)
+
+ val appConfigurationProperties = Map("spark.executor.memory" -> "4g", "spark.executor.cores"->"4", "spark.executor.instances"->"4")
+
+ val executorSummaries = Seq(
+ newFakeExecutorSummary(
+ id = "1",
+ totalMemoryBytesSpilled = 200000L
+ ),
+ newFakeExecutorSummary(
+ id = "2",
+ totalMemoryBytesSpilled = 100000L
+ ),
+ newFakeExecutorSummary(
+ id = "3",
+ totalMemoryBytesSpilled = 300000L
+ ),
+ newFakeExecutorSummary(
+ id = "4",
+ totalMemoryBytesSpilled = 200000L
+ )
+ )
+
+ describe(".apply") {
+ val data1 = newFakeSparkApplicationData(executorSummaries, appConfigurationProperties)
+ val heuristicResult = executorStorageSpillHeuristic.apply(data1)
+ val heuristicResultDetails = heuristicResult.getHeuristicResultDetails
+ val evaluator = new Evaluator(executorStorageSpillHeuristic, data1)
+
+ it("returns the severity") {
+ heuristicResult.getSeverity should be(Severity.SEVERE)
+ }
+
+ it("returns the total memory spilled") {
+ val details = heuristicResultDetails.get(0)
+ details.getName should include("Total memory spilled")
+ details.getValue should be("781.25 KB")
+ }
+
+ it("returns the max memory spilled") {
+ val details = heuristicResultDetails.get(1)
+ details.getName should include("Max memory spilled")
+ details.getValue should be("292.97 KB")
+ }
+
+ it("returns the mean memory spilled") {
+ val details = heuristicResultDetails.get(2)
+ details.getName should include("Mean memory spilled")
+ details.getValue should be("195.31 KB")
+ }
+
+ it("has the memory spilled per task") {
+ evaluator.totalMemorySpilledPerTask should be(800000)
+ }
+ }
+ }
+}
+
+object ExecutorStorageSpillHeuristicTest {
+ import JavaConverters._
+
+ def newFakeHeuristicConfigurationData(params: Map[String, String] = Map.empty): HeuristicConfigurationData =
+ new HeuristicConfigurationData("heuristic", "class", "view", new ApplicationType("type"), params.asJava)
+
+ def newFakeExecutorSummary(
+ id: String,
+ totalMemoryBytesSpilled: Long
+ ): ExecutorSummaryImpl = new ExecutorSummaryImpl(
+ id,
+ hostPort = "",
+ rddBlocks = 0,
+ memoryUsed=0,
+ diskUsed = 0,
+ activeTasks = 0,
+ failedTasks = 0,
+ completedTasks = 0,
+ totalTasks = 0,
+ maxTasks = 10,
+ totalDuration=0,
+ addTime = Calendar.getInstance().getTime,
+ endTime = None,
+ totalInputBytes=0,
+ totalShuffleRead=0,
+ totalShuffleWrite= 0,
+ maxMemory= 2000,
+ totalGCTime = 0,
+ totalMemoryBytesSpilled,
+ executorLogs = Map.empty,
+ peakJvmUsedMemory = Map.empty,
+ peakUnifiedMemory = Map.empty
+ )
+
+ def newFakeSparkApplicationData(
+ executorSummaries: Seq[ExecutorSummaryImpl],
+ appConfigurationProperties: Map[String, String]
+ ): SparkApplicationData = {
+ val appId = "application_1"
+
+ val restDerivedData = SparkRestDerivedData(
+ new ApplicationInfoImpl(appId, name = "app", Seq.empty),
+ jobDatas = Seq.empty,
+ stageDatas = Seq.empty,
+ executorSummaries = executorSummaries,
+ stagesWithFailedTasks = Seq.empty
+ )
+
+ val logDerivedData = SparkLogDerivedData(
+ SparkListenerEnvironmentUpdate(Map("Spark Properties" -> appConfigurationProperties.toSeq))
+ )
+
+ SparkApplicationData(appId, restDerivedData, Some(logDerivedData))
+ }
+}
diff --git a/test/com/linkedin/drelephant/spark/heuristics/ExecutorsHeuristicTest.scala b/test/com/linkedin/drelephant/spark/heuristics/ExecutorsHeuristicTest.scala
index 7dbeea921..ca20ca95d 100644
--- a/test/com/linkedin/drelephant/spark/heuristics/ExecutorsHeuristicTest.scala
+++ b/test/com/linkedin/drelephant/spark/heuristics/ExecutorsHeuristicTest.scala
@@ -21,11 +21,15 @@ import scala.collection.JavaConverters
import com.linkedin.drelephant.analysis.{ApplicationType, Severity, SeverityThresholds}
import com.linkedin.drelephant.configurations.heuristic.HeuristicConfigurationData
import com.linkedin.drelephant.spark.data.{SparkApplicationData, SparkLogDerivedData, SparkRestDerivedData}
-import com.linkedin.drelephant.spark.fetchers.statusapiv1.{ApplicationInfoImpl, ExecutorSummaryImpl}
+import com.linkedin.drelephant.spark.fetchers.statusapiv1.{ApplicationAttemptInfoImpl,
+ApplicationInfoImpl, ExecutorSummaryImpl}
import org.apache.spark.scheduler.SparkListenerEnvironmentUpdate
import org.scalatest.{FunSpec, Matchers}
+import java.util.{Calendar,Date}
-
+/**
+ * Test class for Executors Heuristic. It checks whether all the values used in the heuristic are calculated correctly.
+ */
class ExecutorsHeuristicTest extends FunSpec with Matchers {
import ExecutorsHeuristicTest._
@@ -38,6 +42,20 @@ class ExecutorsHeuristicTest extends FunSpec with Matchers {
)
)
val executorsHeuristic = new ExecutorsHeuristic(heuristicConfigurationData)
+ val dateArray : Array[Date] = new Array[Date](20)
+ val cal: Calendar = Calendar.getInstance()
+ dateArray(0) = cal.getTime
+ cal.add(Calendar.SECOND,1000)
+ cal.add(Calendar.MILLISECOND,1)
+ for(i <- 1 to 19)
+ {
+
+ dateArray(i) = cal.getTime
+ cal.add(Calendar.SECOND,1000)
+
+ }
+ val appAttemptInfo = Seq(newFakeApplicationAttemptInfo(endTime = dateArray(4)))
+
val maxMemory = 5000000L
@@ -46,6 +64,8 @@ class ExecutorsHeuristicTest extends FunSpec with Matchers {
id = "1",
memoryUsed = 1000000L,
totalDuration = 1000001L,
+ addTime = dateArray(0),
+ endTime = Option(dateArray(1)),
totalInputBytes = 1000002L,
totalShuffleRead = 1000003L,
totalShuffleWrite = 1000004L,
@@ -55,6 +75,8 @@ class ExecutorsHeuristicTest extends FunSpec with Matchers {
id = "2",
memoryUsed = 2000000L,
totalDuration = 2000001L,
+ addTime = dateArray(0),
+ endTime = Option(dateArray(2)),
totalInputBytes = 2000002L,
totalShuffleRead = 2000003L,
totalShuffleWrite = 2000004L,
@@ -64,6 +86,8 @@ class ExecutorsHeuristicTest extends FunSpec with Matchers {
id = "3",
memoryUsed = 3000000L,
totalDuration = 3000001L,
+ addTime = dateArray(0),
+ endTime = Option(dateArray(3)),
totalInputBytes = 3000002L,
totalShuffleRead = 3000003L,
totalShuffleWrite = 3000004L,
@@ -73,6 +97,8 @@ class ExecutorsHeuristicTest extends FunSpec with Matchers {
id = "4",
memoryUsed = 4000000L,
totalDuration = 4000001L,
+ addTime = dateArray(0),
+ endTime = None,
totalInputBytes = 4000002L,
totalShuffleRead = 4000003L,
totalShuffleWrite = 4000004L,
@@ -81,7 +107,7 @@ class ExecutorsHeuristicTest extends FunSpec with Matchers {
)
describe(".apply") {
- val data = newFakeSparkApplicationData(executorSummaries)
+ val data = newFakeSparkApplicationData(executorSummaries,appAttemptInfo)
val heuristicResult = executorsHeuristic.apply(data)
val heuristicResultDetails = heuristicResult.getHeuristicResultDetails
@@ -148,7 +174,7 @@ class ExecutorsHeuristicTest extends FunSpec with Matchers {
import ExecutorsHeuristic.Evaluator
import ExecutorsHeuristic.Distribution
- val data = newFakeSparkApplicationData(executorSummaries)
+ val data = newFakeSparkApplicationData(executorSummaries,appAttemptInfo)
val evaluator = new Evaluator(executorsHeuristic, data)
it("has the total storage memory allocated") {
@@ -230,6 +256,8 @@ object ExecutorsHeuristicTest {
id: String,
memoryUsed: Long,
totalDuration: Long,
+ addTime: Date,
+ endTime: Option[Date],
totalInputBytes: Long,
totalShuffleRead: Long,
totalShuffleWrite: Long,
@@ -244,23 +272,39 @@ object ExecutorsHeuristicTest {
failedTasks = 0,
completedTasks = 0,
totalTasks = 0,
+ maxTasks = 0,
totalDuration,
+ addTime,
+ endTime,
totalInputBytes,
totalShuffleRead,
totalShuffleWrite,
maxMemory,
totalGCTime = 0,
- executorLogs = Map.empty
+ totalMemoryBytesSpilled = 0,
+ executorLogs = Map.empty,
+ peakJvmUsedMemory = Map.empty,
+ peakUnifiedMemory = Map.empty
)
+ def newFakeApplicationAttemptInfo(
+ endTime: Date
+ ): ApplicationAttemptInfoImpl = new ApplicationAttemptInfoImpl(
+ attemptId = Option("ATTEMPTid_1"),
+ startTime= Calendar.getInstance().getTime,
+ endTime,
+ sparkUser = "",
+ completed = true
+ )
- def newFakeSparkApplicationData(executorSummaries: Seq[ExecutorSummaryImpl]): SparkApplicationData = {
+ def newFakeSparkApplicationData(executorSummaries: Seq[ExecutorSummaryImpl],appAttemptInfo: Seq[ApplicationAttemptInfoImpl]): SparkApplicationData = {
val appId = "application_1"
val restDerivedData = SparkRestDerivedData(
- new ApplicationInfoImpl(appId, name = "app", Seq.empty),
+ new ApplicationInfoImpl(appId, name = "app", appAttemptInfo),
jobDatas = Seq.empty,
stageDatas = Seq.empty,
- executorSummaries = executorSummaries
+ executorSummaries = executorSummaries,
+ stagesWithFailedTasks = Seq.empty
)
SparkApplicationData(appId, restDerivedData, logDerivedData = None)
diff --git a/test/com/linkedin/drelephant/spark/heuristics/JobsHeuristicTest.scala b/test/com/linkedin/drelephant/spark/heuristics/JobsHeuristicTest.scala
index 240f80d7d..435eddcdb 100644
--- a/test/com/linkedin/drelephant/spark/heuristics/JobsHeuristicTest.scala
+++ b/test/com/linkedin/drelephant/spark/heuristics/JobsHeuristicTest.scala
@@ -26,7 +26,9 @@ import org.apache.spark.JobExecutionStatus
import org.apache.spark.scheduler.SparkListenerEnvironmentUpdate
import org.scalatest.{FunSpec, Matchers}
-
+/**
+ * Test class for Jobs Heuristic. It checks whether all the values used in the heuristic are calculated correctly.
+ */
class JobsHeuristicTest extends FunSpec with Matchers {
import JobsHeuristicTest._
@@ -157,7 +159,8 @@ object JobsHeuristicTest {
new ApplicationInfoImpl(appId, name = "app", Seq.empty),
jobDatas,
stageDatas = Seq.empty,
- executorSummaries = Seq.empty
+ executorSummaries = Seq.empty,
+ stagesWithFailedTasks = Seq.empty
)
SparkApplicationData(appId, restDerivedData, logDerivedData = None)
diff --git a/test/com/linkedin/drelephant/spark/heuristics/JvmUsedMemoryHeuristicTest.scala b/test/com/linkedin/drelephant/spark/heuristics/JvmUsedMemoryHeuristicTest.scala
new file mode 100644
index 000000000..b1a42e74d
--- /dev/null
+++ b/test/com/linkedin/drelephant/spark/heuristics/JvmUsedMemoryHeuristicTest.scala
@@ -0,0 +1,127 @@
+package com.linkedin.drelephant.spark.heuristics
+
+import com.linkedin.drelephant.analysis.{ApplicationType, Severity}
+import com.linkedin.drelephant.configurations.heuristic.HeuristicConfigurationData
+import com.linkedin.drelephant.spark.data.{SparkApplicationData, SparkLogDerivedData, SparkRestDerivedData}
+import com.linkedin.drelephant.spark.fetchers.statusapiv1.{ApplicationInfoImpl, ExecutorSummaryImpl}
+import org.apache.spark.scheduler.SparkListenerEnvironmentUpdate
+import org.scalatest.{FunSpec, Matchers}
+import java.util.Calendar
+
+import scala.collection.JavaConverters
+
+/**
+ * Test class for JVM Used memory. It checks whether all the values used in the heuristic are calculated correctly.
+ */
+class JvmUsedMemoryHeuristicTest extends FunSpec with Matchers {
+
+ import JvmUsedMemoryHeuristicTest._
+
+ val heuristicConfigurationData = newFakeHeuristicConfigurationData()
+
+ val peakJvmUsedMemoryHeuristic = new JvmUsedMemoryHeuristic(heuristicConfigurationData)
+
+ val appConfigurationProperties = Map("spark.driver.memory"->"40000000000", "spark.executor.memory"->"50000000000")
+
+ val executorData = Seq(
+ newDummyExecutorData("1", Map("jvmUsedMemory" -> 394567123)),
+ newDummyExecutorData("2", Map("jvmUsedMemory" -> 2834)),
+ newDummyExecutorData("3", Map("jvmUsedMemory" -> 3945667)),
+ newDummyExecutorData("4", Map("jvmUsedMemory" -> 16367890)),
+ newDummyExecutorData("5", Map("jvmUsedMemory" -> 2345647)),
+ newDummyExecutorData("driver", Map("jvmUsedMemory" -> 394561))
+ )
+ describe(".apply") {
+ val data = newFakeSparkApplicationData(appConfigurationProperties, executorData)
+ val heuristicResult = peakJvmUsedMemoryHeuristic.apply(data)
+ val heuristicResultDetails = heuristicResult.getHeuristicResultDetails
+
+ it("has severity") {
+ heuristicResult.getSeverity should be(Severity.CRITICAL)
+ }
+
+ it("has all the details") {
+ heuristicResultDetails.size() should be(4)
+ }
+
+ describe(".Evaluator") {
+ import JvmUsedMemoryHeuristic.Evaluator
+
+ val data = newFakeSparkApplicationData(appConfigurationProperties, executorData)
+ val heuristicResult = peakJvmUsedMemoryHeuristic.apply(data)
+ val heuristicResultDetails = heuristicResult.getHeuristicResultDetails
+ val evaluator = new Evaluator(peakJvmUsedMemoryHeuristic, data)
+
+ it("has severity executor") {
+ evaluator.severity should be(Severity.CRITICAL)
+ }
+
+ it("has max peak jvm memory") {
+ evaluator.maxExecutorPeakJvmUsedMemory should be (394567123)
+ }
+
+ it("has reasonable size") {
+ val details = heuristicResultDetails.get(3)
+ details.getName should be ("Suggested spark.executor.memory")
+ details.getValue should be ("452 MB")
+ }
+ }
+ }
+}
+
+object JvmUsedMemoryHeuristicTest {
+
+ import JavaConverters._
+
+ def newFakeHeuristicConfigurationData(params: Map[String, String] = Map.empty): HeuristicConfigurationData =
+ new HeuristicConfigurationData("heuristic", "class", "view", new ApplicationType("type"), params.asJava)
+
+ def newDummyExecutorData(
+ id: String,
+ peakJvmUsedMemory: Map[String, Long]
+ ): ExecutorSummaryImpl = new ExecutorSummaryImpl(
+ id,
+ hostPort = "",
+ rddBlocks = 0,
+ memoryUsed = 0,
+ diskUsed = 0,
+ activeTasks = 0,
+ failedTasks = 0,
+ completedTasks = 0,
+ totalTasks = 0,
+ maxTasks = 0,
+ totalDuration = 0,
+ addTime=Calendar.getInstance().getTime,
+ endTime=None,
+ totalInputBytes = 0,
+ totalShuffleRead = 0,
+ totalShuffleWrite = 0,
+ maxMemory = 0,
+ totalGCTime = 0,
+ totalMemoryBytesSpilled = 0,
+ executorLogs = Map.empty,
+ peakJvmUsedMemory,
+ peakUnifiedMemory = Map.empty
+ )
+
+ def newFakeSparkApplicationData(
+ appConfigurationProperties: Map[String, String],
+ executorSummaries: Seq[ExecutorSummaryImpl]
+ ): SparkApplicationData = {
+
+ val logDerivedData = SparkLogDerivedData(
+ SparkListenerEnvironmentUpdate(Map("Spark Properties" -> appConfigurationProperties.toSeq))
+ )
+ val appId = "application_1"
+
+ val restDerivedData = SparkRestDerivedData(
+ new ApplicationInfoImpl(appId, name = "app", Seq.empty),
+ jobDatas = Seq.empty,
+ stageDatas = Seq.empty,
+ executorSummaries = executorSummaries,
+ stagesWithFailedTasks = Seq.empty
+ )
+
+ SparkApplicationData(appId, restDerivedData, Some(logDerivedData))
+ }
+}
diff --git a/test/com/linkedin/drelephant/spark/heuristics/StagesHeuristicTest.scala b/test/com/linkedin/drelephant/spark/heuristics/StagesHeuristicTest.scala
index 7e1d8acb1..e6aae4fe1 100644
--- a/test/com/linkedin/drelephant/spark/heuristics/StagesHeuristicTest.scala
+++ b/test/com/linkedin/drelephant/spark/heuristics/StagesHeuristicTest.scala
@@ -22,11 +22,14 @@ import scala.concurrent.duration.Duration
import com.linkedin.drelephant.analysis.{ApplicationType, Severity}
import com.linkedin.drelephant.configurations.heuristic.HeuristicConfigurationData
import com.linkedin.drelephant.spark.data.{SparkApplicationData, SparkLogDerivedData, SparkRestDerivedData}
-import com.linkedin.drelephant.spark.fetchers.statusapiv1.{ApplicationInfoImpl, JobDataImpl, StageDataImpl}
+import com.linkedin.drelephant.spark.fetchers.statusapiv1.{ApplicationInfoImpl, StageDataImpl}
import com.linkedin.drelephant.spark.fetchers.statusapiv1.StageStatus
import org.apache.spark.scheduler.SparkListenerEnvironmentUpdate
import org.scalatest.{FunSpec, Matchers}
+/**
+ * Test class for Stages Heuristic. It checks whether all the values used in the heuristic are calculated correctly.
+ */
class StagesHeuristicTest extends FunSpec with Matchers {
import StagesHeuristicTest._
@@ -81,13 +84,6 @@ class StagesHeuristicTest extends FunSpec with Matchers {
|stage 4, attempt 0 (task failure rate: 0.800)""".stripMargin
)
}
-
- it("returns the list of stages with long runtimes") {
- heuristicResultDetails.get(4).getValue should be(
- s"""|stage 8, attempt 0 (runtime: 45 min)
- |stage 9, attempt 0 (runtime: 1 hr)""".stripMargin
- )
- }
}
describe(".Evaluator") {
@@ -113,15 +109,6 @@ class StagesHeuristicTest extends FunSpec with Matchers {
evaluator.stagesWithHighTaskFailureRates.map { case (stageData, taskFailureRate) => (stageData.stageId, taskFailureRate) }
stageIdsAndTaskFailureRates should contain theSameElementsInOrderAs(Seq((3, 0.6D), (4, 0.8D)))
}
-
- it("has the list of stages with long average executor runtimes") {
- val stageIdsAndRuntimes =
- evaluator.stagesWithLongAverageExecutorRuntimes.map { case (stageData, runtime) => (stageData.stageId, runtime) }
- stageIdsAndRuntimes should contain theSameElementsInOrderAs(
- Seq((8, Duration("45min").toMillis), (9, Duration("60min").toMillis))
- )
- }
-
it("computes the overall severity") {
evaluator.severity should be(Severity.CRITICAL)
}
@@ -178,7 +165,8 @@ object StagesHeuristicTest {
new ApplicationInfoImpl(appId, name = "app", Seq.empty),
jobDatas = Seq.empty,
stageDatas = stageDatas,
- executorSummaries = Seq.empty
+ executorSummaries = Seq.empty,
+ stagesWithFailedTasks = Seq.empty
)
val logDerivedData = SparkLogDerivedData(
diff --git a/test/com/linkedin/drelephant/spark/heuristics/StagesWithFailedTasksHeuristicTest.scala b/test/com/linkedin/drelephant/spark/heuristics/StagesWithFailedTasksHeuristicTest.scala
new file mode 100644
index 000000000..cdfdc11ea
--- /dev/null
+++ b/test/com/linkedin/drelephant/spark/heuristics/StagesWithFailedTasksHeuristicTest.scala
@@ -0,0 +1,168 @@
+/*
+ * Copyright 2016 LinkedIn Corp.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License"); you may not
+ * use this file except in compliance with the License. You may obtain a copy of
+ * the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+ * License for the specific language governing permissions and limitations under
+ * the License.
+ */
+
+package com.linkedin.drelephant.spark.heuristics
+
+import java.util.Date
+import scala.collection.JavaConverters
+import scala.concurrent.duration.Duration
+import org.apache.spark.scheduler.SparkListenerEnvironmentUpdate
+import org.scalatest.{FunSpec, Matchers}
+
+import com.linkedin.drelephant.spark.fetchers.statusapiv1.StageStatus
+import com.linkedin.drelephant.analysis.{ApplicationType, Severity}
+import com.linkedin.drelephant.configurations.heuristic.HeuristicConfigurationData
+import com.linkedin.drelephant.spark.data.{SparkApplicationData, SparkRestDerivedData}
+import com.linkedin.drelephant.spark.fetchers.statusapiv1.{ApplicationInfoImpl, StageDataImpl, TaskDataImpl}
+
+/**
+ * Test class for Stages With Failed Tasks Heuristic. It checks whether all the values used in the heuristic are calculated correctly.
+ */
+class StagesWithFailedTasksHeuristicTest extends FunSpec with Matchers {
+ import StagesWithFailedTasksHeuristicTest._
+
+ val OOM_ERROR = "java.lang.OutOfMemoryError"
+ val OVERHEAD_MEMORY_ERROR = "killed by YARN for exceeding memory limits"
+
+ describe("StagesHeuristic") {
+ val heuristicConfigurationData = newFakeHeuristicConfigurationData()
+ val stagesWithFailedTasksHeuristic = new StagesWithFailedTasksHeuristic(heuristicConfigurationData)
+ val failedTaskData = Seq(
+ newFakeStageData(StageStatus.COMPLETE, 0, numCompleteTasks = 10, OOM_ERROR, OVERHEAD_MEMORY_ERROR),
+ newFakeStageData(StageStatus.COMPLETE, 1, numCompleteTasks = 100, OOM_ERROR, OVERHEAD_MEMORY_ERROR),
+ newFakeStageData(StageStatus.COMPLETE, 2, numCompleteTasks = 5, OOM_ERROR, ""),
+ newFakeStageData(StageStatus.FAILED, 3, numCompleteTasks = 3, OVERHEAD_MEMORY_ERROR, OVERHEAD_MEMORY_ERROR),
+ newFakeStageData(StageStatus.FAILED, 4, numCompleteTasks = 102, OVERHEAD_MEMORY_ERROR, OVERHEAD_MEMORY_ERROR),
+ newFakeStageData(StageStatus.COMPLETE, 5, numCompleteTasks = 100, OOM_ERROR, OOM_ERROR),
+ newFakeStageData(StageStatus.COMPLETE, 6, numCompleteTasks = 10, OOM_ERROR, ""),
+ newFakeStageData(StageStatus.COMPLETE, 7, numCompleteTasks = 10, OOM_ERROR, OVERHEAD_MEMORY_ERROR),
+ newFakeStageData(StageStatus.COMPLETE, 8, numCompleteTasks = 10, "", ""),
+ newFakeStageData(StageStatus.COMPLETE, 9, numCompleteTasks = 20, OOM_ERROR, OOM_ERROR)
+ )
+ val appConfigurationProperties = Map.empty
+
+ describe(".apply") {
+ val data = newFakeSparkApplicationData(failedTaskData)
+ val heuristicResult = stagesWithFailedTasksHeuristic.apply(data)
+ val heuristicResultDetails = heuristicResult.getHeuristicResultDetails
+
+ it("returns the severity") {
+ heuristicResult.getSeverity should be(Severity.SEVERE)
+ }
+ }
+
+ describe(".Evaluator") {
+ import StagesWithFailedTasksHeuristic.Evaluator
+ val data = newFakeSparkApplicationData(failedTaskData)
+ val evaluator = new Evaluator(stagesWithFailedTasksHeuristic, data)
+
+ it("has OOM and Overhead severity") {
+ evaluator.severityOOMStages should be(Severity.SEVERE)
+ evaluator.severityOverheadStages should be (Severity.SEVERE)
+ }
+ it("has correct number of stages having error") {
+ evaluator.stagesWithOOMError should be (7)
+ evaluator.stagesWithOverheadError should be (5)
+ }
+ }
+ }
+}
+
+object StagesWithFailedTasksHeuristicTest {
+ import JavaConverters._
+
+ def newFakeHeuristicConfigurationData(params: Map[String, String] = Map.empty): HeuristicConfigurationData =
+ new HeuristicConfigurationData("heuristic", "class", "view", new ApplicationType("type"), params.asJava)
+
+ def newFakeStageData(
+ status: StageStatus,
+ stageId: Int,
+ numCompleteTasks: Int,
+ error1: String,
+ error2: String
+ ): StageDataImpl = new StageDataImpl(
+ status,
+ stageId,
+ attemptId = 0,
+ numActiveTasks = numCompleteTasks,
+ numCompleteTasks,
+ numFailedTasks = 3,
+ executorRunTime = 0,
+ inputBytes = 0,
+ inputRecords = 0,
+ outputBytes = 0,
+ outputRecords = 0,
+ shuffleReadBytes = 0,
+ shuffleReadRecords = 0,
+ shuffleWriteBytes = 0,
+ shuffleWriteRecords = 0,
+ memoryBytesSpilled = 0,
+ diskBytesSpilled = 0,
+ name = "foo",
+ details = "",
+ schedulingPool = "",
+ accumulatorUpdates = Seq.empty,
+ tasks = new Some(Map(0.toLong -> new TaskDataImpl(
+ taskId = 0,
+ index = 1,
+ attempt = 0,
+ launchTime = new Date(),
+ executorId = "1",
+ host = "SomeHost",
+ taskLocality = "ANY",
+ speculative = false,
+ accumulatorUpdates = Seq(),
+ errorMessage = Some(error1),
+ taskMetrics = None), 1.toLong -> new TaskDataImpl(
+ taskId = 1,
+ index = 1,
+ attempt = 0,
+ launchTime = new Date(),
+ executorId = "1",
+ host = "SomeHost",
+ taskLocality = "ANY",
+ speculative = false,
+ accumulatorUpdates = Seq(),
+ errorMessage = Some(error2),
+ taskMetrics = None), 2.toLong -> new TaskDataImpl(
+ taskId = 1,
+ index = 1,
+ attempt = 0,
+ launchTime = new Date(),
+ executorId = "1",
+ host = "SomeHost",
+ taskLocality = "ANY",
+ speculative = false,
+ accumulatorUpdates = Seq(),
+ errorMessage = None,
+ taskMetrics = None)
+ )),
+ executorSummary = None
+ )
+
+ def newFakeSparkApplicationData
+ (stagesWithFailedTasks: Seq[StageDataImpl]): SparkApplicationData = {
+ val appId = "application_1"
+ val restDerivedData = SparkRestDerivedData(
+ new ApplicationInfoImpl(appId, name = "app", Seq.empty),
+ jobDatas = Seq.empty,
+ stageDatas = Seq.empty,
+ executorSummaries = Seq.empty,
+ stagesWithFailedTasks = stagesWithFailedTasks
+ )
+ SparkApplicationData(appId, restDerivedData, None)
+ }
+}
\ No newline at end of file
diff --git a/test/com/linkedin/drelephant/spark/heuristics/UnifiedMemoryHeuristicTest.scala b/test/com/linkedin/drelephant/spark/heuristics/UnifiedMemoryHeuristicTest.scala
new file mode 100644
index 000000000..ba0bea048
--- /dev/null
+++ b/test/com/linkedin/drelephant/spark/heuristics/UnifiedMemoryHeuristicTest.scala
@@ -0,0 +1,168 @@
+package com.linkedin.drelephant.spark.heuristics
+
+import com.linkedin.drelephant.analysis.{ApplicationType, Severity}
+import com.linkedin.drelephant.configurations.heuristic.HeuristicConfigurationData
+import com.linkedin.drelephant.spark.data.{SparkApplicationData, SparkLogDerivedData, SparkRestDerivedData}
+import com.linkedin.drelephant.spark.fetchers.statusapiv1.{ApplicationInfoImpl, ExecutorSummaryImpl}
+import com.linkedin.drelephant.spark.heuristics.UnifiedMemoryHeuristic.Evaluator
+import org.apache.spark.scheduler.SparkListenerEnvironmentUpdate
+import org.scalatest.{FunSpec, Matchers}
+import java.util.Calendar
+
+import scala.collection.JavaConverters
+
+/**
+ * Test class for Unified Memory Heuristic. It checks whether all the values used in the heuristic are calculated correctly.
+ */
+class UnifiedMemoryHeuristicTest extends FunSpec with Matchers {
+
+ import UnifiedMemoryHeuristicTest._
+
+ val heuristicConfigurationData = newFakeHeuristicConfigurationData()
+ val unifiedMemoryHeuristic = new UnifiedMemoryHeuristic(heuristicConfigurationData)
+ val appConfigurationProperties = Map("spark.executor.memory"->"3147483647")
+ val appConfigurationProperties1 = Map("spark.executor.memory"->"214567874847")
+ val appConfigurationProperties2 = Map("spark.executor.memory"->"214567874847", "spark.memory.fraction"->"0.06")
+
+ val executorData = Seq(
+ newDummyExecutorData("1", 999999999, Map("executionMemory" -> 300000, "storageMemory" -> 94567)),
+ newDummyExecutorData("2", 400000, Map("executionMemory" -> 200000, "storageMemory" -> 34568)),
+ newDummyExecutorData("3", 400000, Map("executionMemory" -> 300000, "storageMemory" -> 34569)),
+ newDummyExecutorData("4", 400000, Map("executionMemory" -> 20000, "storageMemory" -> 3456)),
+ newDummyExecutorData("5", 400000, Map("executionMemory" -> 200000, "storageMemory" -> 34564)),
+ newDummyExecutorData("6", 400000, Map("executionMemory" -> 300000, "storageMemory" -> 94561))
+ )
+
+ val executorData1 = Seq(
+ newDummyExecutorData("driver", 400000, Map("executionMemory" -> 300000, "storageMemory" -> 94567)),
+ newDummyExecutorData("2", 999999999, Map("executionMemory" -> 200, "storageMemory" -> 200))
+ )
+
+ val executorData2 = Seq(
+ newDummyExecutorData("driver", 400000, Map("executionMemory" -> 300000, "storageMemory" -> 94567)),
+ newDummyExecutorData("2", 999999999, Map("executionMemory" -> 999999990, "storageMemory" -> 9))
+ )
+
+ val executorData3 = Seq(
+ newDummyExecutorData("1", 400000, Map("executionMemory" -> 300000, "storageMemory" -> 94567)),
+ newDummyExecutorData("2", 500000, Map("executionMemory" -> 5000, "storageMemory" -> 9))
+ )
+
+ val executorData4 = Seq(
+ newDummyExecutorData("1", 268435460L, Map("executionMemory" -> 300000, "storageMemory" -> 94567)),
+ newDummyExecutorData("2", 268435460L, Map("executionMemory" -> 900000, "storageMemory" -> 500000))
+ )
+
+ describe(".apply") {
+ val data = newFakeSparkApplicationData(appConfigurationProperties, executorData)
+ val data1 = newFakeSparkApplicationData(appConfigurationProperties1, executorData1)
+ val data2 = newFakeSparkApplicationData(appConfigurationProperties1, executorData2)
+ val data3 = newFakeSparkApplicationData(appConfigurationProperties1, executorData3)
+ val data4 = newFakeSparkApplicationData(appConfigurationProperties2, executorData4)
+ val heuristicResult = unifiedMemoryHeuristic.apply(data)
+ val heuristicResult1 = unifiedMemoryHeuristic.apply(data1)
+ val heuristicResult2 = unifiedMemoryHeuristic.apply(data2)
+ val heuristicResult3 = unifiedMemoryHeuristic.apply(data3)
+ val heuristicResult4 = unifiedMemoryHeuristic.apply(data4)
+ val evaluator = new Evaluator(unifiedMemoryHeuristic, data1)
+
+ it("has severity") {
+ heuristicResult.getSeverity should be(Severity.CRITICAL)
+ }
+
+ it("has max value") {
+ val details = heuristicResult.getHeuristicResultDetails.get(2)
+ details.getName should be("Max peak unified memory")
+ details.getValue should be("385.32 KB")
+ }
+
+ it("has mean value") {
+ val details = heuristicResult.getHeuristicResultDetails.get(1)
+ details.getName should be("Mean peak unified memory")
+ details.getValue should be("263.07 KB")
+ }
+
+ it("data1 has severity") {
+ heuristicResult1.getSeverity should be(Severity.CRITICAL)
+ }
+
+ it("data1 has maxMemory") {
+ evaluator.maxMemory should be(999999999)
+ }
+
+ it("data1 has max memory") {
+ evaluator.maxUnifiedMemory should be(400)
+ }
+
+ it("data1 has mean memory") {
+ evaluator.meanUnifiedMemory should be(400)
+ }
+
+ it("has no severity when max and allocated memory are the same") {
+ heuristicResult2.getSeverity should be(Severity.NONE)
+ }
+
+ it("has no severity when maxMemory is less than 256Mb") {
+ heuristicResult3.getSeverity should be(Severity.NONE)
+ }
+
+ it("has critical severity when maxMemory is greater than 256Mb and spark memory fraction is greater than 0.05") {
+ heuristicResult4.getSeverity should be(Severity.CRITICAL)
+ }
+ }
+}
+
+object UnifiedMemoryHeuristicTest {
+
+ import JavaConverters._
+
+ def newFakeHeuristicConfigurationData(params: Map[String, String] = Map.empty): HeuristicConfigurationData =
+ new HeuristicConfigurationData("heuristic", "class", "view", new ApplicationType("type"), params.asJava)
+
+ def newDummyExecutorData(
+ id: String,
+ maxMemory: Long,
+ peakUnifiedMemory: Map[String, Long]
+ ): ExecutorSummaryImpl = new ExecutorSummaryImpl(
+ id,
+ hostPort = "",
+ rddBlocks = 0,
+ memoryUsed = 0,
+ diskUsed = 0,
+ activeTasks = 0,
+ failedTasks = 0,
+ completedTasks = 0,
+ totalTasks = 0,
+ maxTasks = 0,
+ totalDuration = 0,
+ addTime= Calendar.getInstance().getTime,
+ endTime= None,
+ totalInputBytes = 0,
+ totalShuffleRead = 0,
+ totalShuffleWrite = 0,
+ maxMemory,
+ totalGCTime = 0,
+ totalMemoryBytesSpilled = 0,
+ executorLogs = Map.empty,
+ peakJvmUsedMemory = Map.empty,
+ peakUnifiedMemory
+ )
+
+ def newFakeSparkApplicationData(
+ appConfigurationProperties: Map[String, String],
+ executorSummaries: Seq[ExecutorSummaryImpl]): SparkApplicationData =
+ {
+ val appId = "application_1"
+ val restDerivedData = SparkRestDerivedData(
+ new ApplicationInfoImpl(appId, name = "app", Seq.empty),
+ jobDatas = Seq.empty,
+ stageDatas = Seq.empty,
+ executorSummaries = executorSummaries,
+ stagesWithFailedTasks = Seq.empty
+ )
+ val logDerivedData = SparkLogDerivedData(
+ SparkListenerEnvironmentUpdate(Map("Spark Properties" -> appConfigurationProperties.toSeq))
+ )
+ SparkApplicationData(appId, restDerivedData, Some(logDerivedData))
+ }
+}
diff --git a/test/com/linkedin/drelephant/tuning/PSOParamGeneratorTest.java b/test/com/linkedin/drelephant/tuning/PSOParamGeneratorTest.java
index 99aa1f0dd..255268255 100644
--- a/test/com/linkedin/drelephant/tuning/PSOParamGeneratorTest.java
+++ b/test/com/linkedin/drelephant/tuning/PSOParamGeneratorTest.java
@@ -25,10 +25,10 @@
import java.util.Map;
import models.JobDefinition;
import models.JobExecution;
+import models.JobSuggestedParamSet;
import models.JobSuggestedParamValue;
import models.TuningAlgorithm;
import models.TuningJobDefinition;
-import models.TuningJobExecution;
import models.TuningParameter;
import org.junit.Before;
import org.junit.Test;
@@ -203,19 +203,19 @@ public void run() {
PSOParamGenerator psoParamGenerator = new PSOParamGenerator();
psoParamGenerator.getParams();
- List tuningJobExecutionList = TuningJobExecution.find.where()
- .eq(TuningJobExecution.TABLE.paramSetState, TuningJobExecution.ParamSetStatus.CREATED)
+ List jobSuggestedParamSetList = JobSuggestedParamSet.find.where()
+ .eq(JobSuggestedParamSet.TABLE.paramSetState, JobSuggestedParamSet.ParamSetStatus.CREATED)
.findList();
- assertEquals("Swarm size did not match", SWARM_SIZE, tuningJobExecutionList.size());
+ assertEquals("Swarm size did not match", SWARM_SIZE, jobSuggestedParamSetList.size());
- TuningJobExecution tuningJobExecution = tuningJobExecutionList.get(0);
+ JobSuggestedParamSet jobSuggestedParamSet = jobSuggestedParamSetList.get(0);
List jobSuggestedParamValueList = JobSuggestedParamValue.find.where()
- .eq(JobSuggestedParamValue.TABLE.jobExecution + '.' + JobExecution.TABLE.id,
- tuningJobExecution.jobExecution.id)
+ .eq(JobSuggestedParamValue.TABLE.jobSuggestedParamSet + '.' + JobSuggestedParamSet.TABLE.id,
+ jobSuggestedParamSet.id)
.findList();
- TuningAlgorithm tuningAlgorithm = tuningJobExecution.tuningAlgorithm;
+ TuningAlgorithm tuningAlgorithm = jobSuggestedParamSet.tuningAlgorithm;
List tuningParameterList = TuningParameter.find.where()
.eq(TuningParameter.TABLE.tuningAlgorithm + "." + TuningAlgorithm.TABLE.id, tuningAlgorithm.id)
.findList();
diff --git a/test/com/linkedin/drelephant/util/InfoExtractorTest.java b/test/com/linkedin/drelephant/util/InfoExtractorTest.java
index b1c262dbf..4bed7006b 100644
--- a/test/com/linkedin/drelephant/util/InfoExtractorTest.java
+++ b/test/com/linkedin/drelephant/util/InfoExtractorTest.java
@@ -280,7 +280,8 @@ public void testLoadInfoSpark() {
new ApplicationInfoImpl("", "", new Vector(0,1,0)),
new Vector(0,1,0),
new Vector(0,1,0),
- new Vector(0,1,0));
+ new Vector(0,1,0),
+ new Vector(0,1,0));
InfoExtractor.loadInfo(result, data);
@@ -301,7 +302,8 @@ public void testLoadInfoSparkNoConfig() {
new ApplicationInfoImpl("", "", new Vector(0,1,0)),
new Vector(0,1,0),
new Vector(0,1,0),
- new Vector(0,1,0));
+ new Vector(0,1,0),
+ new Vector(0,1,0));
// test to make sure loadInfo does not throw exception if properties are not defined
InfoExtractor.loadInfo(result, data);
diff --git a/test/com/linkedin/drelephant/util/MemoryFormatUtilsTest.java b/test/com/linkedin/drelephant/util/MemoryFormatUtilsTest.java
index 0ae064ebc..067f7427d 100644
--- a/test/com/linkedin/drelephant/util/MemoryFormatUtilsTest.java
+++ b/test/com/linkedin/drelephant/util/MemoryFormatUtilsTest.java
@@ -105,4 +105,16 @@ public void testStringToBytes() {
}
}
}
+
+ public void testRoundOffMemoryStringToNextInteger() {
+ assertEquals("157 MB", MemoryFormatUtils.roundOffMemoryStringToNextInteger("156.1 MB"));
+ assertEquals("155 MB", MemoryFormatUtils.roundOffMemoryStringToNextInteger("155.0 MB"));
+ assertEquals("0 MB", MemoryFormatUtils.roundOffMemoryStringToNextInteger("0 MB"));
+ assertEquals("156 GB", MemoryFormatUtils.roundOffMemoryStringToNextInteger("155.1 G"));
+ assertEquals("", MemoryFormatUtils.roundOffMemoryStringToNextInteger(null));
+ assertEquals("500 MB", MemoryFormatUtils.roundOffMemoryStringToNextInteger("500M"));
+ assertEquals("600 GB", MemoryFormatUtils.roundOffMemoryStringToNextInteger("600 gb"));
+ assertEquals("600 GB", MemoryFormatUtils.roundOffMemoryStringToNextInteger("600 g"));
+ assertEquals("", MemoryFormatUtils.roundOffMemoryStringToNextInteger(""));
+ }
}
diff --git a/test/com/linkedin/drelephant/util/SparkUtilsTest.scala b/test/com/linkedin/drelephant/util/SparkUtilsTest.scala
index f8efab9d5..d2e5355d7 100644
--- a/test/com/linkedin/drelephant/util/SparkUtilsTest.scala
+++ b/test/com/linkedin/drelephant/util/SparkUtilsTest.scala
@@ -287,7 +287,7 @@ object SparkUtilsTest extends MockitoSugar {
BDDMockito.given(fileStatus.getPath()).willReturn(expectedPath)
fileStatus
}
- val expectedStatusArray = Array(expectedFileStatus)
+ val expectedStatusArray = Array(expectedFileStatus)
val filter = new PathFilter() {
override def accept(file: Path): Boolean = {
@@ -298,7 +298,7 @@ object SparkUtilsTest extends MockitoSugar {
BDDMockito.given(fs.getUri).willReturn(fileSystemUri)
BDDMockito.given(fs.exists(expectedPath)).willReturn(true)
BDDMockito.given(fs.getFileStatus(expectedPath)).willReturn(expectedFileStatus)
- BDDMockito.given(fs.listStatus(org.mockito.Matchers.refEq(new Path( new Path(fileSystemUri), basePath)),
+ BDDMockito.given(fs.listStatus(org.mockito.Matchers.refEq(new Path(new Path(fileSystemUri), basePath)),
org.mockito.Matchers.any(filter.getClass))).
willReturn(expectedStatusArray)
BDDMockito.given(fs.open(expectedPath)).willReturn(
diff --git a/test/resources/AutoTuningConf.xml b/test/resources/AutoTuningConf.xml
index b505e52ed..e2e6cacea 100644
--- a/test/resources/AutoTuningConf.xml
+++ b/test/resources/AutoTuningConf.xml
@@ -44,8 +44,8 @@
fitness.compute.wait_interval.ms
- 180
- Wait time after the job is completed for fitness computation
+ 0
+ Wait time after the job is completed till fitness is computer
dr.elephant.api.url
diff --git a/test/resources/test-init.sql b/test/resources/test-init.sql
index 024cd9155..522247546 100644
--- a/test/resources/test-init.sql
+++ b/test/resources/test-init.sql
@@ -11,27 +11,28 @@ INSERT INTO flow_definition(id, flow_def_id, flow_def_url) VALUES (10003,'https:
INSERT INTO job_definition(id, job_def_id, flow_definition_id, job_name, job_def_url, scheduler, username, created_ts, updated_ts) VALUES (100003,'https://ltx1-holdemaz01.grid.linkedin.com:8443/manager?project=AzkabanHelloPigTest&flow=countByCountryFlow&job=countByCountryFlow_countByCountry',10003,'countByCountryFlow_countByCountry','https://ltx1-holdemaz01.grid.linkedin.com:8443/manager?project=AzkabanHelloPigTest&flow=countByCountryFlow&job=countByCountryFlow_countByCountry','azkaban','mkumar1','2018-02-12 08:40:42','2018-02-12 08:40:43');
---INSERT INTO tuning_algorithm VALUES (1,'PIG','PSO',1,'RESOURCE','2018-02-06 17:03:23','2018-02-06 17:03:23');
-
--- INSERT INTO tuning_parameter VALUES (1,'mapreduce.task.io.sort.mb',1,100,50,1920,50,0,'2018-02-06 17:03:23','2018-02-06 17:03:23'),(2,'mapreduce.map.memory.mb',1,2048,1536,8192,128,0,'2018-02-06 17:03:23','2018-02-06 17:03:23'),(3,'mapreduce.task.io.sort.factor',1,10,10,150,10,0,'2018-02-06 17:03:23','2018-02-06 17:03:23'),(4,'mapreduce.map.sort.spill.percent',1,0.8,0.6,0.9,0.1,0,'2018-02-06 17:03:24','2018-02-06 17:03:24'),(5,'mapreduce.reduce.memory.mb',1,2048,1536,8192,128,0,'2018-02-06 17:03:24','2018-02-06 17:03:24'),(6,'pig.maxCombinedSplitSize',1,536870912,536870912,536870912,128,0,'2018-02-06 17:03:24','2018-02-06 17:03:24'),(7,'mapreduce.reduce.java.opts',1,1536,1152,6144,128,1,'2018-02-06 17:03:24','2018-02-06 17:03:24'),(8,'mapreduce.map.java.opts',1,1536,1152,6144,128,1,'2018-02-06 17:03:24','2018-02-06 17:03:24'),(9,'mapreduce.input.fileinputformat.split.maxsize',1,536870912,536870912,536870912,128,1,'2018-02-06 17:03:24','2018-02-06 17:03:24');
-
INSERT INTO tuning_job_definition(job_definition_id, client, tuning_algorithm_id, tuning_enabled, average_resource_usage, average_execution_time, average_input_size_in_bytes, allowed_max_resource_usage_percent, allowed_max_execution_time_percent, created_ts, updated_ts, tuning_disabled_reason)
VALUES (100003,'azkaban',1,1,40.29456456163195,5.178423333333334,324168876088,150,150,'2018-02-12 08:40:42','2018-02-12 08:40:43', NULL);
INSERT INTO flow_execution(id, flow_exec_id, flow_exec_url, flow_definition_id) VALUES (1496,'https://ltx1-holdemaz01.grid.linkedin.com:8443/executor?execid=5416293','https://ltx1-holdemaz01.grid.linkedin.com:8443/executor?execid=5416293',10003),(1497,'https://ltx1-holdemaz01.grid.linkedin.com:8443/executor?execid=5416389','https://ltx1-holdemaz01.grid.linkedin.com:8443/executor?execid=5416389',10003),(1498,'https://ltx1-holdemaz01.grid.linkedin.com:8443/executor?execid=5416495','https://ltx1-holdemaz01.grid.linkedin.com:8443/executor?execid=5416495',10003),(1499,'https://ltx1-holdemaz01.grid.linkedin.com:8443/executor?execid=5416589','https://ltx1-holdemaz01.grid.linkedin.com:8443/executor?execid=5416589',10003),(1500,'https://ltx1-holdemaz01.grid.linkedin.com:8443/executor?execid=5416680','https://ltx1-holdemaz01.grid.linkedin.com:8443/executor?execid=5416680',10003),(1501,'https://ltx1-holdemaz01.grid.linkedin.com:8443/executor?execid=5416818','https://ltx1-holdemaz01.grid.linkedin.com:8443/executor?execid=5416818',10003),(1502,'https://ltx1-holdemaz01.grid.linkedin.com:8443/executor?execid=5417057','https://ltx1-holdemaz01.grid.linkedin.com:8443/executor?execid=5417057',10003);
-INSERT INTO job_execution(id, job_exec_id, job_exec_url, job_definition_id, flow_execution_id, execution_state, resource_usage, execution_time, input_size_in_bytes, created_ts, updated_ts) VALUES (1541,'https://ltx1-holdemaz01.grid.linkedin.com:8443/executor?execid=5416293&job=countByCountryFlow_countByCountry&attempt=0','https://ltx1-holdemaz01.grid.linkedin.com:8443/executor?execid=5416293&job=countByCountryFlow_countByCountry&attempt=0',100003,1496,'SUCCEEDED',21.132545572916666,3.2694833333333335,324713861757,'2018-02-14 05:30:42','2018-02-14 05:30:42'),(1542,'https://ltx1-holdemaz01.grid.linkedin.com:8443/executor?execid=5416389&job=countByCountryFlow_countByCountry&attempt=0','https://ltx1-holdemaz01.grid.linkedin.com:8443/executor?execid=5416389&job=countByCountryFlow_countByCountry&attempt=0',100003,1497,'SUCCEEDED',23.334004991319443,3.6118166666666665,324713861757,'2018-02-14 06:29:45','2018-02-14 06:29:45'),(1543,'https://ltx1-holdemaz01.grid.linkedin.com:8443/executor?execid=5416495&job=countByCountryFlow_countByCountry&attempt=0','https://ltx1-holdemaz01.grid.linkedin.com:8443/executor?execid=5416495&job=countByCountryFlow_countByCountry&attempt=0',100003,1498,'SUCCEEDED',21.28552951388889,3.2940833333333335,324713861757,'2018-02-14 06:29:45','2018-02-14 06:29:45'),(1544,'https://ltx1-holdemaz01.grid.linkedin.com:8443/executor?execid=5416589&job=countByCountryFlow_countByCountry&attempt=0','https://ltx1-holdemaz01.grid.linkedin.com:8443/executor?execid=5416589&job=countByCountryFlow_countByCountry&attempt=0',100003,1499,'SUCCEEDED',21.630970052083335,3.9560833333333334,324713861757,'2018-02-14 06:29:45','2018-02-14 06:29:45'),(1545,'https://ltx1-holdemaz01.grid.linkedin.com:8443/executor?execid=5416680&job=countByCountryFlow_countByCountry&attempt=0','https://ltx1-holdemaz01.grid.linkedin.com:8443/executor?execid=5416680&job=countByCountryFlow_countByCountry&attempt=0',100003,1500,'SUCCEEDED',22.328486328125,3.7285166666666667,324713861757,'2018-02-14 07:29:47','2018-02-14 07:29:48'),(1546,'https://ltx1-holdemaz01.grid.linkedin.com:8443/executor?execid=5416818&job=countByCountryFlow_countByCountry&attempt=0','https://ltx1-holdemaz01.grid.linkedin.com:8443/executor?execid=5416818&job=countByCountryFlow_countByCountry&attempt=0',100003,1501,'SUCCEEDED',32.16945149739583,5.203783333333333,324713861757,'2018-02-14 07:29:48','2018-02-14 07:29:48'),(1547,'https://ltx1-holdemaz01.grid.linkedin.com:8443/executor?execid=5417057&job=countByCountryFlow_countByCountry&attempt=0','https://ltx1-holdemaz01.grid.linkedin.com:8443/executor?execid=5417057&job=countByCountryFlow_countByCountry&attempt=0',100003,1502,'SUCCEEDED', 27.2955078125, 4.047583333333334, 324713861757,'2018-02-14 07:29:48','2018-02-14 07:29:48');
-
-INSERT INTO tuning_job_execution(job_execution_id, tuning_algorithm_id, param_set_state, is_default_execution, fitness, is_param_set_best) VALUES (1541,1,'FITNESS_COMPUTED',0,0.06987967161749142,0),
-(1542,1,'FITNESS_COMPUTED',0,0.07715930864495756,0),
-(1543,1,'FITNESS_COMPUTED',0,0.07038554856075895,0),
-(1544,1,'FITNESS_COMPUTED',0,0.07152782795578526,0),
-(1545,1,'FITNESS_COMPUTED',0,0.07383432757503201,0),
-(1546,1,'FITNESS_COMPUTED',0,0.10637576523832741,0),
-(1547,1,'FITNESS_COMPUTED',0,0.09025893809095505,0);
-
--- INSERT INTO job_saved_state (job_definition_id, saved_state) VALUES (100003,'{\"current_population\":[{\"birthdate\":1.518593387909695E9,\"maximize\":false,\"candidate\":[159.88928672056016,1536.0,10.0,0.770164839202443,2863.372720073011,5.36870912E8],\"fitness\":10000.0,\"paramSetId\":1545,\"_candidate\":[159.88928672056016,1536.0,10.0,0.770164839202443,2863.372720073011,5.36870912E8]},{\"birthdate\":1.518593387909696E9,\"maximize\":false,\"candidate\":[201.64225529876035,1536.0,10.29839988592941,0.7635183100860585,2789.189282499988,5.36870912E8],\"fitness\":10000.0,\"paramSetId\":1546,\"_candidate\":[201.64225529876035,1536.0,10.29839988592941,0.7635183100860585,2789.189282499988,5.36870912E8]},{\"birthdate\":1.518593387909698E9,\"maximize\":false,\"candidate\":[149.52419594024295,1536.0,10.0,0.7630834894363029,2844.1716734703073,5.36870912E8],\"fitness\":10000.0,\"paramSetId\":1547,\"_candidate\":[149.52419594024295,1536.0,10.0,0.7630834894363029,2844.1716734703073,5.36870912E8]}],\"prev_population\":[{\"_candidate\":[162.7907384046847,1536.0,10.0,0.7632081784681852,2809.5806453243313,5.36870912E8],\"maximize\":false,\"birthdate\":1.518593387909403E9,\"fitness\":0.07715930864495756},{\"_candidate\":[124.05878355054111,1536.0,12.521341191290857,0.7622909149004323,2041.562366831904,5.36870912E8],\"maximize\":false,\"birthdate\":1.518593387909406E9,\"fitness\":0.07038554856075895},{\"_candidate\":[149.51252503919468,1536.0,10.0,0.7619961998308155,2844.326081249364,5.36870912E8],\"maximize\":false,\"birthdate\":1.518593387909407E9,\"fitness\":0.07152782795578526}],\"archive\":[{\"birthday\":1.518589785163133E9,\"_candidate\":[214.91248065459718,1536.0,10.0,0.7684337983774014,2810.072296417105,5.36870912E8],\"maximize\":false,\"birthdate\":1.518593387904912E9,\"fitness\":0.06798720858096566},{\"_candidate\":[124.05878355054111,1536.0,12.521341191290857,0.7622909149004323,2041.562366831904,5.36870912E8],\"maximize\":false,\"birthdate\":1.518593387909406E9,\"fitness\":0.07038554856075895},{\"birthday\":1.518589785163142E9,\"_candidate\":[149.5180195836781,1536.0,10.0,0.7624156183109899,2844.240241069899,5.36870912E8],\"maximize\":false,\"birthdate\":1.518593387904921E9,\"fitness\":0.06753575007445276}],\"rnd_state\":\"[3, 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-
--- INSERT INTO job_saved_state (job_definition_id, saved_state) VALUES (100003,'{}');
-
-INSERT INTO job_suggested_param_value(id, job_execution_id, tuning_parameter_id, param_value, created_ts, updated_ts) VALUES (3209,1541,1,149.5239493606563,'2018-02-14 05:30:42','2018-02-14 05:30:42'),(3210,1541,2,1536,'2018-02-14 05:30:42','2018-02-14 05:30:42'),(3211,1541,3,10,'2018-02-14 05:30:42','2018-02-14 05:30:42'),(3212,1541,4,0.761466551875019,'2018-02-14 05:30:42','2018-02-14 05:30:42'),(3213,1541,5,2844.365182469904,'2018-02-14 05:30:42','2018-02-14 05:30:42'),(3214,1541,6,536870912,'2018-02-14 05:30:42','2018-02-14 05:30:42'),(3215,1541,7,2133.273886852428,'2018-02-14 05:30:42','2018-02-14 05:30:42'),(3216,1541,8,1152,'2018-02-14 05:30:42','2018-02-14 05:30:42'),(3217,1541,9,536870912,'2018-02-14 05:30:42','2018-02-14 05:30:42'), (3218,1542,1,162.7907384046847,'2018-02-14 06:29:45','2018-02-14 06:29:45'),(3219,1542,2,1536,'2018-02-14 06:29:45','2018-02-14 06:29:45'),(3220,1542,3,10,'2018-02-14 06:29:45','2018-02-14 06:29:45'),(3221,1542,4,0.7632081784681852,'2018-02-14 06:29:45','2018-02-14 06:29:45'),(3222,1542,5,2809.5806453243313,'2018-02-14 06:29:45','2018-02-14 06:29:45'),(3223,1542,6,536870912,'2018-02-14 06:29:45','2018-02-14 06:29:45'),(3224,1542,7,2107.1854839932485,'2018-02-14 06:29:45','2018-02-14 06:29:45'),(3225,1542,8,1152,'2018-02-14 06:29:45','2018-02-14 06:29:45'),(3226,1542,9,536870912,'2018-02-14 06:29:45','2018-02-14 06:29:45'),(3227,1543,1,124.05878355054111,'2018-02-14 06:29:45','2018-02-14 06:29:45'),(3228,1543,2,1536,'2018-02-14 06:29:45','2018-02-14 06:29:45'),(3229,1543,3,12.521341191290857,'2018-02-14 06:29:45','2018-02-14 06:29:45'),(3230,1543,4,0.7622909149004323,'2018-02-14 06:29:45','2018-02-14 06:29:45'),(3231,1543,5,2041.562366831904,'2018-02-14 06:29:45','2018-02-14 06:29:45'),(3232,1543,6,536870912,'2018-02-14 06:29:45','2018-02-14 06:29:45'),(3233,1543,7,1531.171775123928,'2018-02-14 06:29:45','2018-02-14 06:29:45'),(3234,1543,8,1152,'2018-02-14 06:29:45','2018-02-14 06:29:45'),(3235,1543,9,536870912,'2018-02-14 06:29:45','2018-02-14 06:29:45'),(3236,1544,1,149.51252503919468,'2018-02-14 06:29:45','2018-02-14 06:29:45'),(3237,1544,2,1536,'2018-02-14 06:29:45','2018-02-14 06:29:45'),(3238,1544,3,10,'2018-02-14 06:29:45','2018-02-14 06:29:45'),(3239,1544,4,0.7619961998308155,'2018-02-14 06:29:45','2018-02-14 06:29:45'),(3240,1544,5,2844.326081249364,'2018-02-14 06:29:45','2018-02-14 06:29:45'),(3241,1544,6,536870912,'2018-02-14 06:29:45','2018-02-14 06:29:45'),(3242,1544,7,2133.244560937023,'2018-02-14 06:29:45','2018-02-14 06:29:45'),(3243,1544,8,1152,'2018-02-14 06:29:45','2018-02-14 06:29:45'),(3244,1544,9,536870912,'2018-02-14 06:29:45','2018-02-14 06:29:45'),(3245,1545,1,159.88928672056016,'2018-02-14 07:29:47','2018-02-14 07:29:47'),(3246,1545,2,1536,'2018-02-14 07:29:47','2018-02-14 07:29:47'),(3247,1545,3,10,'2018-02-14 07:29:47','2018-02-14 07:29:47'),(3248,1545,4,0.770164839202443,'2018-02-14 07:29:47','2018-02-14 07:29:47'),(3249,1545,5,2863.372720073011,'2018-02-14 07:29:47','2018-02-14 07:29:47'),(3250,1545,6,536870912,'2018-02-14 07:29:47','2018-02-14 07:29:47'),(3251,1545,7,2147.5295400547584,'2018-02-14 07:29:47','2018-02-14 07:29:47'),(3252,1545,8,1152,'2018-02-14 07:29:47','2018-02-14 07:29:47'),(3253,1545,9,536870912,'2018-02-14 07:29:48','2018-02-14 07:29:48'),(3254,1546,1,201.64225529876035,'2018-02-14 07:29:48','2018-02-14 07:29:48'),(3255,1546,2,1536,'2018-02-14 07:29:48','2018-02-14 07:29:48'),(3256,1546,3,10.29839988592941,'2018-02-14 07:29:48','2018-02-14 07:29:48'),(3257,1546,4,0.7635183100860585,'2018-02-14 07:29:48','2018-02-14 07:29:48'),(3258,1546,5,2789.189282499988,'2018-02-14 07:29:48','2018-02-14 07:29:48'),(3259,1546,6,536870912,'2018-02-14 07:29:48','2018-02-14 07:29:48'),(3260,1546,7,2091.891961874991,'2018-02-14 07:29:48','2018-02-14 07:29:48'),(3261,1546,8,1152,'2018-02-14 07:29:48','2018-02-14 07:29:48'),(3262,1546,9,536870912,'2018-02-14 07:29:48','2018-02-14 07:29:48'),(3263,1547,1,149.52419594024295,'2018-02-14 07:29:48','2018-02-14 07:29:48'),(3264,1547,2,1536,'2018-02-14 07:29:48','2018-02-14 07:29:48'),(3265,1547,3,10,'2018-02-14 07:29:48','2018-02-14 07:29:48'),(3266,1547,4,0.7630834894363029,'2018-02-14 07:29:48','2018-02-14 07:29:48'),(3267,1547,5,2844.1716734703073,'2018-02-14 07:29:48','2018-02-14 07:29:48'),(3268,1547,6,536870912,'2018-02-14 07:29:48','2018-02-14 07:29:48'),(3269,1547,7,2133.1287551027303,'2018-02-14 07:29:48','2018-02-14 07:29:48'),(3270,1547,8,1152,'2018-02-14 07:29:48','2018-02-14 07:29:48'),(3271,1547,9,536870912,'2018-02-14 07:29:48','2018-02-14 07:29:48');
+INSERT INTO job_execution(id, job_exec_id, job_exec_url, job_definition_id, flow_execution_id, execution_state, resource_usage, execution_time, input_size_in_bytes, created_ts, updated_ts) VALUES
+(1541,'https://ltx1-holdemaz01.grid.linkedin.com:8443/executor?execid=5416293&job=countByCountryFlow_countByCountry&attempt=0','https://ltx1-holdemaz01.grid.linkedin.com:8443/executor?execid=5416293&job=countByCountryFlow_countByCountry&attempt=0',100003,1496,'SUCCEEDED',21.132545572916666,3.2694833333333335,324713861757,'2018-02-14 05:30:42','2018-02-14 05:30:42'),
+(1542,'https://ltx1-holdemaz01.grid.linkedin.com:8443/executor?execid=5416389&job=countByCountryFlow_countByCountry&attempt=0','https://ltx1-holdemaz01.grid.linkedin.com:8443/executor?execid=5416389&job=countByCountryFlow_countByCountry&attempt=0',100003,1497,'SUCCEEDED',23.334004991319443,3.6118166666666665,324713861757,'2018-02-14 06:29:45','2018-02-14 06:29:45'),
+(1543,'https://ltx1-holdemaz01.grid.linkedin.com:8443/executor?execid=5416495&job=countByCountryFlow_countByCountry&attempt=0','https://ltx1-holdemaz01.grid.linkedin.com:8443/executor?execid=5416495&job=countByCountryFlow_countByCountry&attempt=0',100003,1498,'SUCCEEDED',21.28552951388889,3.2940833333333335,324713861757,'2018-02-14 06:29:45','2018-02-14 06:29:45'),
+(1544,'https://ltx1-holdemaz01.grid.linkedin.com:8443/executor?execid=5416589&job=countByCountryFlow_countByCountry&attempt=0','https://ltx1-holdemaz01.grid.linkedin.com:8443/executor?execid=5416589&job=countByCountryFlow_countByCountry&attempt=0',100003,1499,'SUCCEEDED',21.630970052083335,3.9560833333333334,324713861757,'2018-02-14 06:29:45','2018-02-14 06:29:45'),
+(1545,'https://ltx1-holdemaz01.grid.linkedin.com:8443/executor?execid=5416680&job=countByCountryFlow_countByCountry&attempt=0','https://ltx1-holdemaz01.grid.linkedin.com:8443/executor?execid=5416680&job=countByCountryFlow_countByCountry&attempt=0',100003,1500,'SUCCEEDED',22.328486328125,3.7285166666666667,324713861757,'2018-02-14 07:29:47','2018-02-14 07:29:48'),
+(1546,'https://ltx1-holdemaz01.grid.linkedin.com:8443/executor?execid=5416818&job=countByCountryFlow_countByCountry&attempt=0','https://ltx1-holdemaz01.grid.linkedin.com:8443/executor?execid=5416818&job=countByCountryFlow_countByCountry&attempt=0',100003,1501,'SUCCEEDED',32.16945149739583,5.203783333333333,324713861757,'2018-02-14 07:29:48','2018-02-14 07:29:48'),
+(1547,'https://ltx1-holdemaz01.grid.linkedin.com:8443/executor?execid=5417057&job=countByCountryFlow_countByCountry&attempt=0','https://ltx1-holdemaz01.grid.linkedin.com:8443/executor?execid=5417057&job=countByCountryFlow_countByCountry&attempt=0',100003,1502,'SUCCEEDED', 27.2955078125, 4.047583333333334, 324713861757,'2018-02-14 07:29:48','2018-02-14 07:29:48');
+
+INSERT INTO job_suggested_param_set(fitness_job_execution_id, tuning_algorithm_id, param_set_state, is_param_set_default, fitness, is_param_set_best, are_constraints_violated, job_definition_id,id) VALUES
+(1541,1,'FITNESS_COMPUTED',0,0.06987967161749142,0,0,100003,1541),
+(1542,1,'FITNESS_COMPUTED',0,0.07715930864495756,0,0,100003,1542),
+(1543,1,'FITNESS_COMPUTED',0,0.07038554856075895,0,0,100003,1543),
+(1544,1,'FITNESS_COMPUTED',0,0.07152782795578526,0,0,100003,1544),
+(1545,1,'FITNESS_COMPUTED',0,0.07383432757503201,0,0,100003,1545),
+(1546,1,'FITNESS_COMPUTED',0,0.10637576523832741,0,0,100003,1546),
+(1547,1,'FITNESS_COMPUTED',0,0.09025893809095505,0,0,100003,1547);
+
+INSERT INTO job_suggested_param_value(id, job_suggested_param_set_id, tuning_parameter_id, param_value, created_ts, updated_ts) VALUES
+(3209,1541,1,149.5239493606563,'2018-02-14 05:30:42','2018-02-14 05:30:42'),(3210,1541,2,1536,'2018-02-14 05:30:42','2018-02-14 05:30:42'),(3211,1541,3,10,'2018-02-14 05:30:42','2018-02-14 05:30:42'),(3212,1541,4,0.761466551875019,'2018-02-14 05:30:42','2018-02-14 05:30:42'),(3213,1541,5,2844.365182469904,'2018-02-14 05:30:42','2018-02-14 05:30:42'),(3214,1541,6,536870912,'2018-02-14 05:30:42','2018-02-14 05:30:42'),(3215,1541,7,2133.273886852428,'2018-02-14 05:30:42','2018-02-14 05:30:42'),(3216,1541,8,1152,'2018-02-14 05:30:42','2018-02-14 05:30:42'),(3217,1541,9,536870912,'2018-02-14 05:30:42','2018-02-14 05:30:42'), (3218,1542,1,162.7907384046847,'2018-02-14 06:29:45','2018-02-14 06:29:45'),(3219,1542,2,1536,'2018-02-14 06:29:45','2018-02-14 06:29:45'),(3220,1542,3,10,'2018-02-14 06:29:45','2018-02-14 06:29:45'),(3221,1542,4,0.7632081784681852,'2018-02-14 06:29:45','2018-02-14 06:29:45'),(3222,1542,5,2809.5806453243313,'2018-02-14 06:29:45','2018-02-14 06:29:45'),(3223,1542,6,536870912,'2018-02-14 06:29:45','2018-02-14 06:29:45'),(3224,1542,7,2107.1854839932485,'2018-02-14 06:29:45','2018-02-14 06:29:45'),(3225,1542,8,1152,'2018-02-14 06:29:45','2018-02-14 06:29:45'),(3226,1542,9,536870912,'2018-02-14 06:29:45','2018-02-14 06:29:45'),(3227,1543,1,124.05878355054111,'2018-02-14 06:29:45','2018-02-14 06:29:45'),(3228,1543,2,1536,'2018-02-14 06:29:45','2018-02-14 06:29:45'),(3229,1543,3,12.521341191290857,'2018-02-14 06:29:45','2018-02-14 06:29:45'),(3230,1543,4,0.7622909149004323,'2018-02-14 06:29:45','2018-02-14 06:29:45'),(3231,1543,5,2041.562366831904,'2018-02-14 06:29:45','2018-02-14 06:29:45'),(3232,1543,6,536870912,'2018-02-14 06:29:45','2018-02-14 06:29:45'),(3233,1543,7,1531.171775123928,'2018-02-14 06:29:45','2018-02-14 06:29:45'),(3234,1543,8,1152,'2018-02-14 06:29:45','2018-02-14 06:29:45'),(3235,1543,9,536870912,'2018-02-14 06:29:45','2018-02-14 06:29:45'),(3236,1544,1,149.51252503919468,'2018-02-14 06:29:45','2018-02-14 06:29:45'),(3237,1544,2,1536,'2018-02-14 06:29:45','2018-02-14 06:29:45'),(3238,1544,3,10,'2018-02-14 06:29:45','2018-02-14 06:29:45'),(3239,1544,4,0.7619961998308155,'2018-02-14 06:29:45','2018-02-14 06:29:45'),(3240,1544,5,2844.326081249364,'2018-02-14 06:29:45','2018-02-14 06:29:45'),(3241,1544,6,536870912,'2018-02-14 06:29:45','2018-02-14 06:29:45'),(3242,1544,7,2133.244560937023,'2018-02-14 06:29:45','2018-02-14 06:29:45'),(3243,1544,8,1152,'2018-02-14 06:29:45','2018-02-14 06:29:45'),(3244,1544,9,536870912,'2018-02-14 06:29:45','2018-02-14 06:29:45'),(3245,1545,1,159.88928672056016,'2018-02-14 07:29:47','2018-02-14 07:29:47'),(3246,1545,2,1536,'2018-02-14 07:29:47','2018-02-14 07:29:47'),(3247,1545,3,10,'2018-02-14 07:29:47','2018-02-14 07:29:47'),(3248,1545,4,0.770164839202443,'2018-02-14 07:29:47','2018-02-14 07:29:47'),(3249,1545,5,2863.372720073011,'2018-02-14 07:29:47','2018-02-14 07:29:47'),(3250,1545,6,536870912,'2018-02-14 07:29:47','2018-02-14 07:29:47'),(3251,1545,7,2147.5295400547584,'2018-02-14 07:29:47','2018-02-14 07:29:47'),(3252,1545,8,1152,'2018-02-14 07:29:47','2018-02-14 07:29:47'),(3253,1545,9,536870912,'2018-02-14 07:29:48','2018-02-14 07:29:48'),(3254,1546,1,201.64225529876035,'2018-02-14 07:29:48','2018-02-14 07:29:48'),(3255,1546,2,1536,'2018-02-14 07:29:48','2018-02-14 07:29:48'),(3256,1546,3,10.29839988592941,'2018-02-14 07:29:48','2018-02-14 07:29:48'),(3257,1546,4,0.7635183100860585,'2018-02-14 07:29:48','2018-02-14 07:29:48'),(3258,1546,5,2789.189282499988,'2018-02-14 07:29:48','2018-02-14 07:29:48'),(3259,1546,6,536870912,'2018-02-14 07:29:48','2018-02-14 07:29:48'),(3260,1546,7,2091.891961874991,'2018-02-14 07:29:48','2018-02-14 07:29:48'),(3261,1546,8,1152,'2018-02-14 07:29:48','2018-02-14 07:29:48'),(3262,1546,9,536870912,'2018-02-14 07:29:48','2018-02-14 07:29:48'),(3263,1547,1,149.52419594024295,'2018-02-14 07:29:48','2018-02-14 07:29:48'),(3264,1547,2,1536,'2018-02-14 07:29:48','2018-02-14 07:29:48'),(3265,1547,3,10,'2018-02-14 07:29:48','2018-02-14 07:29:48'),(3266,1547,4,0.7630834894363029,'2018-02-14 07:29:48','2018-02-14 07:29:48'),(3267,1547,5,2844.1716734703073,'2018-02-14 07:29:48','2018-02-14 07:29:48'),(3268,1547,6,536870912,'2018-02-14 07:29:48','2018-02-14 07:29:48'),(3269,1547,7,2133.1287551027303,'2018-02-14 07:29:48','2018-02-14 07:29:48'),(3270,1547,8,1152,'2018-02-14 07:29:48','2018-02-14 07:29:48'),(3271,1547,9,536870912,'2018-02-14 07:29:48','2018-02-14 07:29:48');
diff --git a/test/resources/tunein-test1.sql b/test/resources/tunein-test1.sql
index ebb33dd33..aee0f0d22 100644
--- a/test/resources/tunein-test1.sql
+++ b/test/resources/tunein-test1.sql
@@ -1,24 +1,44 @@
-INSERT INTO `flow_definition`(id, flow_def_id, flow_def_url) VALUES (57,'https://elephant.linkedin.com:8443/manager?project=nus-virals-autotuning-test&flow=score-all-images','https://elephant.linkedin.com:8443/manager?project=nus-virals-autotuning-test&flow=score-all-images'),(60,'https://elephant.linkedin.com:8443/manager?project=AzkabanHelloPigTest&flow=countByCountryFlowSmall','https://elephant.linkedin.com:8443/manager?project=AzkabanHelloPigTest&flow=countByCountryFlowSmall');
-INSERT INTO `job_definition`(id, job_def_id, flow_definition_id, job_name, job_def_url, scheduler, username, created_ts, updated_ts) VALUES (51,'https://elephant.linkedin.com:8443/manager?project=nus-virals-autotuning-test&flow=score-all-images&job=score-all-images_generate-impression-action',57,'score-all-images_generate-impression-action','https://elephant.linkedin.com:8443/manager?project=nus-virals-autotuning-test&flow=score-all-images&job=score-all-images_generate-impression-action','azkaban','dev_svc',parsedatetime('2018-01-13 17:27:09','dd-MM-yyyy hh:mm:ss'),parsedatetime('2018-01-13 17:27:09','dd-MM-yyyy hh:mm:ss')),(54,'https://elephant.linkedin.com:8443/manager?project=AzkabanHelloPigTest&flow=countByCountryFlowSmall&job=countByCountryFlowSmall_countByCountry',60,'countByCountryFlowSmall_countByCountry','https://elephant.linkedin.com:8443/manager?project=AzkabanHelloPigTest&flow=countByCountryFlowSmall&job=countByCountryFlowSmall_countByCountry','azkaban','mkumar1',parsedatetime('2018-01-22 11:41:12','dd-MM-yyyy hh:mm:ss'),parsedatetime('2018-01-22 11:41:12','dd-MM-yyyy hh:mm:ss'));
-INSERT INTO `tuning_job_definition`(job_definition_id, client, tuning_algorithm_id, tuning_enabled, average_resource_usage, average_execution_time, average_input_size_in_bytes, allowed_max_resource_usage_percent, allowed_max_execution_time_percent, created_ts, updated_ts, tuning_disabled_reason) VALUES
+INSERT INTO `flow_definition`(id, flow_def_id, flow_def_url) VALUES
+(57,'https://elephant.linkedin.com:8443/manager?project=nus-virals-autotuning-test&flow=score-all-images','https://elephant.linkedin.com:8443/manager?project=nus-virals-autotuning-test&flow=score-all-images'),
+(60,'https://elephant.linkedin.com:8443/manager?project=AzkabanHelloPigTest&flow=countByCountryFlowSmall','https://elephant.linkedin.com:8443/manager?project=AzkabanHelloPigTest&flow=countByCountryFlowSmall');
+
+
+INSERT INTO `job_definition`(id, job_def_id, flow_definition_id, job_name, job_def_url, scheduler, username, created_ts, updated_ts) VALUES
+(51,'https://elephant.linkedin.com:8443/manager?project=nus-virals-autotuning-test&flow=score-all-images&job=score-all-images_generate-impression-action',57,'score-all-images_generate-impression-action','https://elephant.linkedin.com:8443/manager?project=nus-virals-autotuning-test&flow=score-all-images&job=score-all-images_generate-impression-action','azkaban','dev_svc',parsedatetime('2018-01-13 17:27:09','dd-MM-yyyy hh:mm:ss'),parsedatetime('2018-01-13 17:27:09','dd-MM-yyyy hh:mm:ss')),
+(54,'https://elephant.linkedin.com:8443/manager?project=AzkabanHelloPigTest&flow=countByCountryFlowSmall&job=countByCountryFlowSmall_countByCountry',60,'countByCountryFlowSmall_countByCountry','https://elephant.linkedin.com:8443/manager?project=AzkabanHelloPigTest&flow=countByCountryFlowSmall&job=countByCountryFlowSmall_countByCountry','azkaban','mkumar1',parsedatetime('2018-01-22 11:41:12','dd-MM-yyyy hh:mm:ss'),parsedatetime('2018-01-22 11:41:12','dd-MM-yyyy hh:mm:ss'));
+
+INSERT INTO `tuning_job_definition`(job_definition_id, client, tuning_algorithm_id, tuning_enabled, average_resource_usage, average_execution_time,
+average_input_size_in_bytes, allowed_max_resource_usage_percent, allowed_max_execution_time_percent, created_ts, updated_ts, tuning_disabled_reason) VALUES
(51,'azkaban',1,1,458.4372180906033,61.92735384666667,5845242598595,200,200,parsedatetime('2018-01-13 17:27:09','dd-MM-yyyy hh:mm:ss'),parsedatetime('2018-01-13 17:28:34','dd-MM-yyyy hh:mm:ss'), NULL),
(54,'azkaban',1,1,0.09060929361979166,1.8950733333333332,532614133,200,200,parsedatetime('2018-01-22 11:41:12','dd-MM-yyyy hh:mm:ss'),parsedatetime('2018-01-22 15:49:33','dd-MM-yyyy hh:mm:ss'), NULL);
+
INSERT INTO `flow_execution`(id, flow_exec_id, flow_exec_url, flow_definition_id) VALUES (846,'https://elephant.linkedin.com:8443/executor?execid=5157830','https://elephant.linkedin.com:8443/executor?execid=5157830',57),(847,'https://elephant.linkedin.com:8443/executor?execid=5158194','https://elephant.linkedin.com:8443/executor?execid=5158194',57),(848,'https://elephant.linkedin.com:8443/executor?execid=5158533','https://elephant.linkedin.com:8443/executor?execid=5158533',57),(2035,'https://elephant.linkedin.com:8443/executor?execid=5356366','https://elephant.linkedin.com:8443/executor?execid=5356366',60),(2036,'https://elephant.linkedin.com:8443/executor?execid=5356377','https://elephant.linkedin.com:8443/executor?execid=5356377',60),(2047,'https://elephant.linkedin.com:8443/executor?execid=5356853','https://elephant.linkedin.com:8443/executor?execid=5356853',60);
-INSERT INTO `job_execution`(id, job_exec_id, job_exec_url, job_definition_id, flow_execution_id, execution_state, resource_usage, execution_time, input_size_in_bytes, created_ts, updated_ts) VALUES (1624,'https://elephant.linkedin.com:8443/executor?execid=5158194&job=score-all-images_generate-impression-action&attempt=0','https://elephant.linkedin.com:8443/executor?execid=5158194&job=score-all-images_generate-impression-action&attempt=0',51,847,'SUCCEEDED',196.53583333333333,43.996566666666666,3990998122525,parsedatetime('2018-01-15 03:03:05','dd-MM-yyyy hh:mm:ss'),parsedatetime('2018-01-15 05:08:44','dd-MM-yyyy hh:mm:ss')),(1625,'https://elephant.linkedin.com:8443/executor?execid=5157830&job=score-all-images_generate-impression-action&attempt=0','https://elephant.linkedin.com:8443/executor?execid=5157830&job=score-all-images_generate-impression-action&attempt=0',51,846,'SUCCEEDED',169.09791666666666,34.86125,3990827471029,parsedatetime('2018-01-15 03:03:05','dd-MM-yyyy hh:mm:ss'),parsedatetime('2018-01-15 03:58:41','dd-MM-yyyy hh:mm:ss')),(1626,'https://elephant.linkedin.com:8443/executor?execid=5158533&job=score-all-images_generate-impression-action&attempt=0','https://elephant.linkedin.com:8443/executor?execid=5158533&job=score-all-images_generate-impression-action&attempt=0',51,848,'SUCCEEDED',166.11400634765624,33.7234,3990138654674,parsedatetime('2018-01-15 03:03:05','dd-MM-yyyy hh:mm:ss'),parsedatetime('2018-01-15 05:59:21','dd-MM-yyyy hh:mm:ss')),(1627,NULL,NULL,51,NULL,NULL,NULL,NULL,NULL,parsedatetime('2018-01-15 05:59:21','dd-MM-yyyy hh:mm:ss'),parsedatetime('2018-01-15 05:59:21','dd-MM-yyyy hh:mm:ss')),(1628,NULL,NULL,51,NULL,NULL,NULL,NULL,NULL,parsedatetime('2018-01-15 05:59:21','dd-MM-yyyy hh:mm:ss'),parsedatetime('2018-01-15 05:59:21','dd-MM-yyyy hh:mm:ss')),(1629,NULL,NULL,51,NULL,NULL,NULL,NULL,NULL,parsedatetime('2018-01-15 05:59:21','dd-MM-yyyy hh:mm:ss'),parsedatetime('2018-01-15 05:59:21','dd-MM-yyyy hh:mm:ss')),(1719,'https://elephant.linkedin.com:8443/executor?execid=5356366&job=countByCountryFlowSmall_countByCountry&attempt=0','https://elephant.linkedin.com:8443/executor?execid=5356366&job=countByCountryFlowSmall_countByCountry&attempt=0',54,2035,'FAILED',0,0,0,parsedatetime('2018-01-23 04:06:41','dd-MM-yyyy hh:mm:ss'),parsedatetime('2018-02-09 05:52:42','dd-MM-yyyy hh:mm:ss')),(1720,'https://elephant.linkedin.com:8443/executor?execid=5356377&job=countByCountryFlowSmall_countByCountry&attempt=0','https://elephant.linkedin.com:8443/executor?execid=5356377&job=countByCountryFlowSmall_countByCountry&attempt=0',54,2036,'SUCCEEDED',0.07686197916666666,3.5777,540598828,parsedatetime('2018-01-23 04:06:41','dd-MM-yyyy hh:mm:ss'),parsedatetime('2018-02-09 05:52:42','dd-MM-yyyy hh:mm:ss')),(1721,'https://elephant.linkedin.com:8443/executor?execid=5356853&job=countByCountryFlowSmall_countByCountry&attempt=0','https://elephant.linkedin.com:8443/executor?execid=5356853&job=countByCountryFlowSmall_countByCountry&attempt=0',54,2047,'SUCCEEDED',0.21555555555555556,3.5456666666666665,540598828,parsedatetime('2018-01-23 04:06:41','dd-MM-yyyy hh:mm:ss'),parsedatetime('2018-02-13 18:02:55','dd-MM-yyyy hh:mm:ss')),(2833,NULL,NULL,54,NULL,NULL,NULL,NULL,NULL,parsedatetime('2018-02-13 18:02:55','dd-MM-yyyy hh:mm:ss'),parsedatetime('2018-02-13 18:02:55','dd-MM-yyyy hh:mm:ss')),(2834,NULL,NULL,54,NULL,NULL,NULL,NULL,NULL,parsedatetime('2018-02-13 18:02:55','dd-MM-yyyy hh:mm:ss'),parsedatetime('2018-02-13 18:02:55','dd-MM-yyyy hh:mm:ss')),(2835,NULL,NULL,54,NULL,NULL,NULL,NULL,NULL,parsedatetime('2018-02-13 18:02:55','dd-MM-yyyy hh:mm:ss'),parsedatetime('2018-02-13 18:02:56','dd-MM-yyyy hh:mm:ss'));
-INSERT INTO `tuning_job_execution`(job_execution_id, tuning_algorithm_id, param_set_state, is_default_execution, fitness, is_param_set_best) VALUES (1624,1,'FITNESS_COMPUTED',0,0.05287618226970775,0),
-(1625,1,'FITNESS_COMPUTED',0,0.04549620518409709,0),
-(1626,1,'FITNESS_COMPUTED',0,0.044701092268747876,0),
-(1627,1,'CREATED',0,NULL,0),
-(1628,1,'CREATED',0,NULL,0),
-(1629,1,'CREATED',0,NULL,0),
-(1719,1,'FITNESS_COMPUTED',0,1.0960015760903588,0),
-(1720,1,'FITNESS_COMPUTED',0,0.15266389313494158,0),
-(1721,1,'FITNESS_COMPUTED',0,1.079813530812908,0),
-(2833,1,'FITNESS_COMPUTED',0,0.54365576171875,0),
-(2834,1,'CREATED',0,NULL,0),
-(2835,1,'CREATED',0,NULL,0);
---INSERT INTO `job_saved_state` VALUES 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+
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+(NULL,1,'CREATED',0,NULL,0,0,54,2835);
+
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Spill',0,0),(1519961,'application_1506645932520_14829375','com.linkedin.drelephant.mapreduce.heuristics.MapperMemoryHeuristic','Mapper Memory',0,0),(1519962,'application_1506645932520_14829375','com.linkedin.drelephant.mapreduce.heuristics.ReducerDataSkewHeuristic','Reducer Data Skew',0,0),(1519963,'application_1506645932520_14829375','com.linkedin.drelephant.mapreduce.heuristics.ReducerGCHeuristic','Reducer GC',0,0),(1519964,'application_1506645932520_14829375','com.linkedin.drelephant.mapreduce.heuristics.ReducerTimeHeuristic','Reducer Time',3,1647),(1519965,'application_1506645932520_14829375','com.linkedin.drelephant.mapreduce.heuristics.ReducerMemoryHeuristic','Reducer Memory',0,0),(1519966,'application_1506645932520_14829375','com.linkedin.drelephant.mapreduce.heuristics.ShuffleSortHeuristic','Shuffle & Sort',0,0),(1519967,'application_1506645932520_14829375','com.linkedin.drelephant.mapreduce.heuristics.DistributedCacheLimitHeuristic','Distributed Cache Limit',0,0),(1519968,'application_1506645932520_14829662','com.linkedin.drelephant.mapreduce.heuristics.MapperDataSkewHeuristic','Mapper Data Skew',0,0),(1519969,'application_1506645932520_14829662','com.linkedin.drelephant.mapreduce.heuristics.MapperGCHeuristic','Mapper GC',0,0),(1519970,'application_1506645932520_14829662','com.linkedin.drelephant.mapreduce.heuristics.MapperTimeHeuristic','Mapper Time',0,0),(1519971,'application_1506645932520_14829662','com.linkedin.drelephant.mapreduce.heuristics.MapperSpeedHeuristic','Mapper Speed',0,0),(1519972,'application_1506645932520_14829662','com.linkedin.drelephant.mapreduce.heuristics.MapperSpillHeuristic','Mapper Spill',0,0),(1519973,'application_1506645932520_14829662','com.linkedin.drelephant.mapreduce.heuristics.MapperMemoryHeuristic','Mapper Memory',0,0),(1519974,'application_1506645932520_14829662','com.linkedin.drelephant.mapreduce.heuristics.ReducerDataSkewHeuristic','Reducer Data Skew',0,0),(1519975,'application_1506645932520_14829662','com.linkedin.drelephant.mapreduce.heuristics.ReducerGCHeuristic','Reducer GC',0,0),(1519976,'application_1506645932520_14829662','com.linkedin.drelephant.mapreduce.heuristics.ReducerTimeHeuristic','Reducer Time',0,0),(1519977,'application_1506645932520_14829662','com.linkedin.drelephant.mapreduce.heuristics.ReducerMemoryHeuristic','Reducer Memory',0,0),(1519978,'application_1506645932520_14829662','com.linkedin.drelephant.mapreduce.heuristics.ShuffleSortHeuristic','Shuffle & Sort',0,0),(1519979,'application_1506645932520_14829662','com.linkedin.drelephant.mapreduce.heuristics.DistributedCacheLimitHeuristic','Distributed Cache Limit',0,0),(1519980,'application_1506645932520_14829738','com.linkedin.drelephant.mapreduce.heuristics.MapperDataSkewHeuristic','Mapper Data Skew',0,0),(1519981,'application_1506645932520_14829738','com.linkedin.drelephant.mapreduce.heuristics.MapperGCHeuristic','Mapper GC',0,0),(1519982,'application_1506645932520_14829738','com.linkedin.drelephant.mapreduce.heuristics.MapperTimeHeuristic','Mapper Time',0,0),(1519983,'application_1506645932520_14829738','com.linkedin.drelephant.mapreduce.heuristics.MapperSpeedHeuristic','Mapper Speed',0,0),(1519984,'application_1506645932520_14829738','com.linkedin.drelephant.mapreduce.heuristics.MapperSpillHeuristic','Mapper Spill',0,0),(1519985,'application_1506645932520_14829738','com.linkedin.drelephant.mapreduce.heuristics.MapperMemoryHeuristic','Mapper Memory',0,0),(1519986,'application_1506645932520_14829738','com.linkedin.drelephant.mapreduce.heuristics.ReducerDataSkewHeuristic','Reducer Data Skew',0,0),(1519987,'application_1506645932520_14829738','com.linkedin.drelephant.mapreduce.heuristics.ReducerGCHeuristic','Reducer GC',0,0),(1519988,'application_1506645932520_14829738','com.linkedin.drelephant.mapreduce.heuristics.ReducerTimeHeuristic','Reducer Time',0,0),(1519989,'application_1506645932520_14829738','com.linkedin.drelephant.mapreduce.heuristics.ReducerMemoryHeuristic','Reducer Memory',0,0),(1519990,'application_1506645932520_14829738','com.linkedin.drelephant.mapreduce.heuristics.ShuffleSortHeuristic','Shuffle & Sort',0,0),(1519991,'application_1506645932520_14829738','com.linkedin.drelephant.mapreduce.heuristics.DistributedCacheLimitHeuristic','Distributed Cache Limit',0,0),(1519992,'application_1506645932520_14829819','com.linkedin.drelephant.mapreduce.heuristics.MapperDataSkewHeuristic','Mapper Data Skew',0,0),(1519993,'application_1506645932520_14829819','com.linkedin.drelephant.mapreduce.heuristics.MapperGCHeuristic','Mapper GC',0,0),(1519994,'application_1506645932520_14829819','com.linkedin.drelephant.mapreduce.heuristics.MapperTimeHeuristic','Mapper Time',0,0),(1519995,'application_1506645932520_14829819','com.linkedin.drelephant.mapreduce.heuristics.MapperSpeedHeuristic','Mapper Speed',0,0),(1519996,'application_1506645932520_14829819','com.linkedin.drelephant.mapreduce.heuristics.MapperSpillHeuristic','Mapper Spill',0,0),(1519997,'application_1506645932520_14829819','com.linkedin.drelephant.mapreduce.heuristics.MapperMemoryHeuristic','Mapper Memory',0,0),(1519998,'application_1506645932520_14829819','com.linkedin.drelephant.mapreduce.heuristics.ReducerDataSkewHeuristic','Reducer Data Skew',0,0),(1519999,'application_1506645932520_14829819','com.linkedin.drelephant.mapreduce.heuristics.ReducerGCHeuristic','Reducer GC',0,0),(1520000,'application_1506645932520_14829819','com.linkedin.drelephant.mapreduce.heuristics.ReducerTimeHeuristic','Reducer Time',0,0),(1520001,'application_1506645932520_14829819','com.linkedin.drelephant.mapreduce.heuristics.ReducerMemoryHeuristic','Reducer Memory',0,0),(1520002,'application_1506645932520_14829819','com.linkedin.drelephant.mapreduce.heuristics.ShuffleSortHeuristic','Shuffle & Sort',0,0),(1520003,'application_1506645932520_14829819','com.linkedin.drelephant.mapreduce.heuristics.DistributedCacheLimitHeuristic','Distributed Cache Limit',0,0),(1520004,'application_1506645932520_14829871','com.linkedin.drelephant.mapreduce.heuristics.MapperDataSkewHeuristic','Mapper Data Skew',0,0),(1520005,'application_1506645932520_14829871','com.linkedin.drelephant.mapreduce.heuristics.MapperGCHeuristic','Mapper GC',0,0),(1520006,'application_1506645932520_14829871','com.linkedin.drelephant.mapreduce.heuristics.MapperTimeHeuristic','Mapper Time',0,0),(1520007,'application_1506645932520_14829871','com.linkedin.drelephant.mapreduce.heuristics.MapperSpeedHeuristic','Mapper Speed',0,0),(1520008,'application_1506645932520_14829871','com.linkedin.drelephant.mapreduce.heuristics.MapperSpillHeuristic','Mapper Spill',0,0),(1520009,'application_1506645932520_14829871','com.linkedin.drelephant.mapreduce.heuristics.MapperMemoryHeuristic','Mapper Memory',0,0),(1520010,'application_1506645932520_14829871','com.linkedin.drelephant.mapreduce.heuristics.ReducerDataSkewHeuristic','Reducer Data Skew',1,0),(1520011,'application_1506645932520_14829871','com.linkedin.drelephant.mapreduce.heuristics.ReducerGCHeuristic','Reducer GC',0,0),(1520012,'application_1506645932520_14829871','com.linkedin.drelephant.mapreduce.heuristics.ReducerTimeHeuristic','Reducer Time',0,0),(1520013,'application_1506645932520_14829871','com.linkedin.drelephant.mapreduce.heuristics.ReducerMemoryHeuristic','Reducer Memory',0,0),(1520014,'application_1506645932520_14829871','com.linkedin.drelephant.mapreduce.heuristics.ShuffleSortHeuristic','Shuffle & Sort',0,0),(1520015,'application_1506645932520_14829871','com.linkedin.drelephant.mapreduce.heuristics.DistributedCacheLimitHeuristic','Distributed Cache Limit',0,0),(1520016,'application_1506645932520_14830035','com.linkedin.drelephant.mapreduce.heuristics.MapperDataSkewHeuristic','Mapper Data Skew',0,0),(1520017,'application_1506645932520_14830035','com.linkedin.drelephant.mapreduce.heuristics.MapperGCHeuristic','Mapper GC',0,0),(1520018,'application_1506645932520_14830035','com.linkedin.drelephant.mapreduce.heuristics.MapperTimeHeuristic','Mapper Time',0,0),(1520019,'application_1506645932520_14830035','com.linkedin.drelephant.mapreduce.heuristics.MapperSpeedHeuristic','Mapper Speed',0,0),(1520020,'application_1506645932520_14830035','com.linkedin.drelephant.mapreduce.heuristics.MapperSpillHeuristic','Mapper Spill',0,0),(1520021,'application_1506645932520_14830035','com.linkedin.drelephant.mapreduce.heuristics.MapperMemoryHeuristic','Mapper Memory',0,0),(1520022,'application_1506645932520_14830035','com.linkedin.drelephant.mapreduce.heuristics.ReducerDataSkewHeuristic','Reducer Data Skew',0,0),(1520023,'application_1506645932520_14830035','com.linkedin.drelephant.mapreduce.heuristics.ReducerGCHeuristic','Reducer GC',0,0),(1520024,'application_1506645932520_14830035','com.linkedin.drelephant.mapreduce.heuristics.ReducerTimeHeuristic','Reducer Time',0,0),(1520025,'application_1506645932520_14830035','com.linkedin.drelephant.mapreduce.heuristics.ReducerMemoryHeuristic','Reducer Memory',0,0),(1520026,'application_1506645932520_14830035','com.linkedin.drelephant.mapreduce.heuristics.ShuffleSortHeuristic','Shuffle & Sort',0,0),(1520027,'application_1506645932520_14830035','com.linkedin.drelephant.mapreduce.heuristics.DistributedCacheLimitHeuristic','Distributed Cache Limit',0,0),(1520028,'application_1506645932520_14830034','com.linkedin.drelephant.mapreduce.heuristics.MapperDataSkewHeuristic','Mapper Data Skew',0,0),(1520029,'application_1506645932520_14830034','com.linkedin.drelephant.mapreduce.heuristics.MapperGCHeuristic','Mapper GC',0,0),(1520030,'application_1506645932520_14830034','com.linkedin.drelephant.mapreduce.heuristics.MapperTimeHeuristic','Mapper Time',0,0),(1520031,'application_1506645932520_14830034','com.linkedin.drelephant.mapreduce.heuristics.MapperSpeedHeuristic','Mapper Speed',0,0),(1520032,'application_1506645932520_14830034','com.linkedin.drelephant.mapreduce.heuristics.MapperSpillHeuristic','Mapper Spill',0,0),(1520033,'application_1506645932520_14830034','com.linkedin.drelephant.mapreduce.heuristics.MapperMemoryHeuristic','Mapper Memory',0,0),(1520034,'application_1506645932520_14830034','com.linkedin.drelephant.mapreduce.heuristics.ReducerDataSkewHeuristic','Reducer Data Skew',0,0),(1520035,'application_1506645932520_14830034','com.linkedin.drelephant.mapreduce.heuristics.ReducerGCHeuristic','Reducer GC',0,0),(1520036,'application_1506645932520_14830034','com.linkedin.drelephant.mapreduce.heuristics.ReducerTimeHeuristic','Reducer Time',0,0),(1520037,'application_1506645932520_14830034','com.linkedin.drelephant.mapreduce.heuristics.ReducerMemoryHeuristic','Reducer Memory',0,0),(1520038,'application_1506645932520_14830034','com.linkedin.drelephant.mapreduce.heuristics.ShuffleSortHeuristic','Shuffle & Sort',0,0),(1520039,'application_1506645932520_14830034','com.linkedin.drelephant.mapreduce.heuristics.DistributedCacheLimitHeuristic','Distributed Cache Limit',0,0),(1520040,'application_1506645932520_14830183','com.linkedin.drelephant.mapreduce.heuristics.MapperDataSkewHeuristic','Mapper Data Skew',0,0),(1520041,'application_1506645932520_14830183','com.linkedin.drelephant.mapreduce.heuristics.MapperGCHeuristic','Mapper GC',0,0),(1520042,'application_1506645932520_14830183','com.linkedin.drelephant.mapreduce.heuristics.MapperTimeHeuristic','Mapper Time',0,0),(1520043,'application_1506645932520_14830183','com.linkedin.drelephant.mapreduce.heuristics.MapperSpeedHeuristic','Mapper Speed',0,0),(1520044,'application_1506645932520_14830183','com.linkedin.drelephant.mapreduce.heuristics.MapperSpillHeuristic','Mapper Spill',0,0),(1520045,'application_1506645932520_14830183','com.linkedin.drelephant.mapreduce.heuristics.MapperMemoryHeuristic','Mapper Memory',0,0),(1520046,'application_1506645932520_14830183','com.linkedin.drelephant.mapreduce.heuristics.ReducerDataSkewHeuristic','Reducer Data Skew',0,0),(1520047,'application_1506645932520_14830183','com.linkedin.drelephant.mapreduce.heuristics.ReducerGCHeuristic','Reducer GC',0,0),(1520048,'application_1506645932520_14830183','com.linkedin.drelephant.mapreduce.heuristics.ReducerTimeHeuristic','Reducer Time',0,0),(1520049,'application_1506645932520_14830183','com.linkedin.drelephant.mapreduce.heuristics.ReducerMemoryHeuristic','Reducer Memory',0,0),(1520050,'application_1506645932520_14830183','com.linkedin.drelephant.mapreduce.heuristics.ShuffleSortHeuristic','Shuffle & Sort',0,0),(1520051,'application_1506645932520_14830183','com.linkedin.drelephant.mapreduce.heuristics.DistributedCacheLimitHeuristic','Distributed Cache Limit',0,0),(1520052,'application_1506645932520_14830345','com.linkedin.drelephant.mapreduce.heuristics.MapperDataSkewHeuristic','Mapper Data Skew',0,0),(1520053,'application_1506645932520_14830345','com.linkedin.drelephant.mapreduce.heuristics.MapperGCHeuristic','Mapper GC',0,0),(1520054,'application_1506645932520_14830345','com.linkedin.drelephant.mapreduce.heuristics.MapperTimeHeuristic','Mapper Time',0,0),(1520055,'application_1506645932520_14830345','com.linkedin.drelephant.mapreduce.heuristics.MapperSpeedHeuristic','Mapper Speed',0,0),(1520056,'application_1506645932520_14830345','com.linkedin.drelephant.mapreduce.heuristics.MapperSpillHeuristic','Mapper Spill',0,0),(1520057,'application_1506645932520_14830345','com.linkedin.drelephant.mapreduce.heuristics.MapperMemoryHeuristic','Mapper Memory',0,0),(1520058,'application_1506645932520_14830345','com.linkedin.drelephant.mapreduce.heuristics.ReducerDataSkewHeuristic','Reducer Data Skew',0,0),(1520059,'application_1506645932520_14830345','com.linkedin.drelephant.mapreduce.heuristics.ReducerGCHeuristic','Reducer GC',0,0),(1520060,'application_1506645932520_14830345','com.linkedin.drelephant.mapreduce.heuristics.ReducerTimeHeuristic','Reducer Time',0,0),(1520061,'application_1506645932520_14830345','com.linkedin.drelephant.mapreduce.heuristics.ReducerMemoryHeuristic','Reducer Memory',0,0),(1520062,'application_1506645932520_14830345','com.linkedin.drelephant.mapreduce.heuristics.ShuffleSortHeuristic','Shuffle & Sort',0,0),(1520063,'application_1506645932520_14830345','com.linkedin.drelephant.mapreduce.heuristics.DistributedCacheLimitHeuristic','Distributed Cache Limit',0,0),(1520064,'application_1506645932520_14829374','com.linkedin.drelephant.mapreduce.heuristics.MapperDataSkewHeuristic','Mapper Data Skew',0,0),(1520065,'application_1506645932520_14829374','com.linkedin.drelephant.mapreduce.heuristics.MapperGCHeuristic','Mapper GC',0,0),(1520066,'application_1506645932520_14829374','com.linkedin.drelephant.mapreduce.heuristics.MapperTimeHeuristic','Mapper Time',3,1536),(1520067,'application_1506645932520_14829374','com.linkedin.drelephant.mapreduce.heuristics.MapperSpeedHeuristic','Mapper Speed',0,0),(1520068,'application_1506645932520_14829374','com.linkedin.drelephant.mapreduce.heuristics.MapperSpillHeuristic','Mapper Spill',0,0),(1520069,'application_1506645932520_14829374','com.linkedin.drelephant.mapreduce.heuristics.MapperMemoryHeuristic','Mapper Memory',0,0),(1520070,'application_1506645932520_14829374','com.linkedin.drelephant.mapreduce.heuristics.ReducerDataSkewHeuristic','Reducer Data Skew',0,0),(1520071,'application_1506645932520_14829374','com.linkedin.drelephant.mapreduce.heuristics.ReducerGCHeuristic','Reducer GC',0,0),(1520072,'application_1506645932520_14829374','com.linkedin.drelephant.mapreduce.heuristics.ReducerTimeHeuristic','Reducer Time',3,1698),(1520073,'application_1506645932520_14829374','com.linkedin.drelephant.mapreduce.heuristics.ReducerMemoryHeuristic','Reducer Memory',0,0),(1520074,'application_1506645932520_14829374','com.linkedin.drelephant.mapreduce.heuristics.ShuffleSortHeuristic','Shuffle & Sort',0,0),(1520075,'application_1506645932520_14829374','com.linkedin.drelephant.mapreduce.heuristics.DistributedCacheLimitHeuristic','Distributed Cache Limit',0,0),(1520076,'application_1506645932520_14829377','com.linkedin.drelephant.mapreduce.heuristics.MapperDataSkewHeuristic','Mapper Data Skew',0,0),(1520077,'application_1506645932520_14829377','com.linkedin.drelephant.mapreduce.heuristics.MapperGCHeuristic','Mapper GC',0,0),(1520078,'application_1506645932520_14829377','com.linkedin.drelephant.mapreduce.heuristics.MapperTimeHeuristic','Mapper Time',4,7096),(1520079,'application_1506645932520_14829377','com.linkedin.drelephant.mapreduce.heuristics.MapperSpeedHeuristic','Mapper Speed',0,0),(1520080,'application_1506645932520_14829377','com.linkedin.drelephant.mapreduce.heuristics.MapperSpillHeuristic','Mapper Spill',0,0),(1520081,'application_1506645932520_14829377','com.linkedin.drelephant.mapreduce.heuristics.MapperMemoryHeuristic','Mapper Memory',0,0),(1520082,'application_1506645932520_14829377','com.linkedin.drelephant.mapreduce.heuristics.ReducerDataSkewHeuristic','Reducer Data Skew',0,0),(1520083,'application_1506645932520_14829377','com.linkedin.drelephant.mapreduce.heuristics.ReducerGCHeuristic','Reducer GC',0,0),(1520084,'application_1506645932520_14829377','com.linkedin.drelephant.mapreduce.heuristics.ReducerTimeHeuristic','Reducer Time',3,2997),(1520085,'application_1506645932520_14829377','com.linkedin.drelephant.mapreduce.heuristics.ReducerMemoryHeuristic','Reducer Memory',0,0),(1520086,'application_1506645932520_14829377','com.linkedin.drelephant.mapreduce.heuristics.ShuffleSortHeuristic','Shuffle & Sort',0,0),(1520087,'application_1506645932520_14829377','com.linkedin.drelephant.mapreduce.heuristics.DistributedCacheLimitHeuristic','Distributed Cache Limit',0,0),(1536115,'application_1506645932520_18304591','com.linkedin.drelephant.mapreduce.heuristics.MapperDataSkewHeuristic','Mapper Data Skew',0,0),(1536116,'application_1506645932520_18304591','com.linkedin.drelephant.mapreduce.heuristics.MapperGCHeuristic','Mapper GC',0,0),(1536117,'application_1506645932520_18304591','com.linkedin.drelephant.mapreduce.heuristics.MapperTimeHeuristic','Mapper Time',0,0),(1536118,'application_1506645932520_18304591','com.linkedin.drelephant.mapreduce.heuristics.MapperSpeedHeuristic','Mapper Speed',0,0),(1536119,'application_1506645932520_18304591','com.linkedin.drelephant.mapreduce.heuristics.MapperSpillHeuristic','Mapper Spill',0,0),(1536120,'application_1506645932520_18304591','com.linkedin.drelephant.mapreduce.heuristics.MapperMemoryHeuristic','Mapper Memory',0,0),(1536121,'application_1506645932520_18304591','com.linkedin.drelephant.mapreduce.heuristics.ReducerDataSkewHeuristic','Reducer Data Skew',0,0),(1536122,'application_1506645932520_18304591','com.linkedin.drelephant.mapreduce.heuristics.ReducerGCHeuristic','Reducer GC',0,0),(1536123,'application_1506645932520_18304591','com.linkedin.drelephant.mapreduce.heuristics.ReducerTimeHeuristic','Reducer Time',0,0),(1536124,'application_1506645932520_18304591','com.linkedin.drelephant.mapreduce.heuristics.ReducerMemoryHeuristic','Reducer Memory',2,2),(1536125,'application_1506645932520_18304591','com.linkedin.drelephant.mapreduce.heuristics.ShuffleSortHeuristic','Shuffle & Sort',0,0),(1536126,'application_1506645932520_18304591','com.linkedin.drelephant.mapreduce.heuristics.DistributedCacheLimitHeuristic','Distributed Cache Limit',0,0),(1536163,'application_1506645932520_18311651','com.linkedin.drelephant.mapreduce.heuristics.MapperDataSkewHeuristic','Mapper Data Skew',0,0),(1536164,'application_1506645932520_18311651','com.linkedin.drelephant.mapreduce.heuristics.MapperGCHeuristic','Mapper GC',0,0),(1536165,'application_1506645932520_18311651','com.linkedin.drelephant.mapreduce.heuristics.MapperTimeHeuristic','Mapper Time',0,0),(1536166,'application_1506645932520_18311651','com.linkedin.drelephant.mapreduce.heuristics.MapperSpeedHeuristic','Mapper Speed',0,0),(1536167,'application_1506645932520_18311651','com.linkedin.drelephant.mapreduce.heuristics.MapperSpillHeuristic','Mapper Spill',0,0),(1536168,'application_1506645932520_18311651','com.linkedin.drelephant.mapreduce.heuristics.MapperMemoryHeuristic','Mapper Memory',3,3),(1536169,'application_1506645932520_18311651','com.linkedin.drelephant.mapreduce.heuristics.ReducerDataSkewHeuristic','Reducer Data Skew',0,0),(1536170,'application_1506645932520_18311651','com.linkedin.drelephant.mapreduce.heuristics.ReducerGCHeuristic','Reducer GC',0,0),(1536171,'application_1506645932520_18311651','com.linkedin.drelephant.mapreduce.heuristics.ReducerTimeHeuristic','Reducer Time',0,0),(1536172,'application_1506645932520_18311651','com.linkedin.drelephant.mapreduce.heuristics.ReducerMemoryHeuristic','Reducer Memory',3,3),(1536173,'application_1506645932520_18311651','com.linkedin.drelephant.mapreduce.heuristics.ShuffleSortHeuristic','Shuffle & Sort',0,0),(1536174,'application_1506645932520_18311651','com.linkedin.drelephant.mapreduce.heuristics.DistributedCacheLimitHeuristic','Distributed Cache Limit',0,0);
diff --git a/test/rest/RestAPITest.java b/test/rest/RestAPITest.java
index 0a4b44330..5ac9b93f6 100644
--- a/test/rest/RestAPITest.java
+++ b/test/rest/RestAPITest.java
@@ -19,19 +19,12 @@
import com.fasterxml.jackson.databind.JsonNode;
import com.fasterxml.jackson.databind.ObjectMapper;
import com.google.gson.Gson;
-import com.google.gson.JsonElement;
-import com.linkedin.drelephant.AutoTuner;
import com.linkedin.drelephant.DrElephant;
import com.linkedin.drelephant.ElephantContext;
import com.linkedin.drelephant.tuning.BaselineComputeUtil;
import com.linkedin.drelephant.tuning.FitnessComputeUtil;
-import com.linkedin.drelephant.tuning.JobCompleteDetector;
import com.linkedin.drelephant.util.Utils;
-import common.DBTestUtil;
-import controllers.AutoTuningMetricsController;
-
-import java.util.ArrayList;
import java.util.HashMap;
import java.util.Iterator;
import java.util.LinkedHashMap;
@@ -42,10 +35,11 @@
import models.JobDefinition;
import models.JobExecution;
import models.JobExecution.ExecutionState;
+import models.JobSuggestedParamSet;
import models.TuningJobDefinition;
-import models.TuningJobExecution;
-import models.TuningJobExecution.ParamSetStatus;
+import models.JobSuggestedParamSet.ParamSetStatus;
+import models.TuningJobExecutionParamSet;
import org.apache.hadoop.conf.Configuration;
import org.junit.Assert;
import org.junit.Before;
@@ -55,7 +49,6 @@
import play.Application;
import play.GlobalSettings;
-import play.libs.Json;
import play.libs.WS;
import play.test.FakeApplication;
@@ -154,31 +147,29 @@ public void run() {
jsonResponse.path("mapreduce.reduce.memory.mb").asDouble() > 0);
assertTrue("Get current run param output size did not match", jsonResponse.size() == 9);
- TuningJobExecution tuningJobExecution = TuningJobExecution.find.select("*")
- .fetch(TuningJobExecution.TABLE.jobExecution, "*")
- .fetch(TuningJobExecution.TABLE.jobExecution + "." + JobExecution.TABLE.job, "*")
+ TuningJobExecutionParamSet tuningJobExecutionParamSet = TuningJobExecutionParamSet.find.select("*")
+ .fetch(TuningJobExecutionParamSet.TABLE.jobExecution, "*")
+ .fetch(TuningJobExecutionParamSet.TABLE.jobSuggestedParamSet, "*")
.where()
- .eq(TuningJobExecution.TABLE.jobExecution + "." + JobExecution.TABLE.jobExecId,
+ .eq(TuningJobExecutionParamSet.TABLE.jobExecution + "." + JobExecution.TABLE.jobExecId,
"https://elephant.linkedin.com:8443/executor?execid=5221700&job=countByCountryFlowSmall_countByCountry&attempt=0")
.findUnique();
- tuningJobExecution.paramSetState = ParamSetStatus.EXECUTED;
- tuningJobExecution.jobExecution.executionState = ExecutionState.SUCCEEDED;
- tuningJobExecution.update();
+ JobSuggestedParamSet jobSuggestedParamSet = tuningJobExecutionParamSet.jobSuggestedParamSet;
+ JobExecution jobExecution = tuningJobExecutionParamSet.jobExecution;
+
+ jobExecution.executionState = ExecutionState.SUCCEEDED;
+ jobExecution.update();
+ jobSuggestedParamSet.paramSetState = ParamSetStatus.EXECUTED;
+ jobSuggestedParamSet.update();
FitnessComputeUtil fitnessComputeUtil = new FitnessComputeUtil();
fitnessComputeUtil.updateFitness();
- tuningJobExecution = TuningJobExecution.find.select("*")
- .fetch(TuningJobExecution.TABLE.jobExecution, "*")
- .fetch(TuningJobExecution.TABLE.jobExecution + "." + JobExecution.TABLE.job, "*")
- .where()
- .eq(TuningJobExecution.TABLE.jobExecution + "." + JobExecution.TABLE.jobExecId,
- "https://elephant.linkedin.com:8443/executor?execid=5221700&job=countByCountryFlowSmall_countByCountry&attempt=0")
- .findUnique();
+ jobSuggestedParamSet = JobSuggestedParamSet.find.byId(jobSuggestedParamSet.id);
- assertTrue("Fitness not computed", tuningJobExecution.paramSetState == ParamSetStatus.FITNESS_COMPUTED);
- assertTrue("Fitness not computed and have zero value", tuningJobExecution.fitness > 0);
+ assertTrue("Fitness not computed", jobSuggestedParamSet.paramSetState.equals(ParamSetStatus.FITNESS_COMPUTED));
+ assertTrue("Fitness is non-positive", jobSuggestedParamSet.fitness > 0);
}
});
}