diff --git a/doc/source/whatsnew/v3.1.0.rst b/doc/source/whatsnew/v3.1.0.rst
index ba68d142e1eda..fbfb07d2a0f02 100644
--- a/doc/source/whatsnew/v3.1.0.rst
+++ b/doc/source/whatsnew/v3.1.0.rst
@@ -152,6 +152,7 @@ Performance improvements
 - Performance improvement in :func:`merge` with ``how="left"`` and ``sort=False`` when joining on a right index with unique keys (:issue:`65160`)
 - Performance improvement in :func:`merge` with ``sort=False`` for single-key ``how="left"``/``how="right"`` joins when the opposite join key is sorted, unique, and range-like (:issue:`64146`)
 - Performance improvement in :func:`read_csv` with ``engine="c"`` when reading from binary file-like objects (e.g. PyArrow S3 file handles) by avoiding unnecessary ``TextIOWrapper`` wrapping (:issue:`46823`)
+- Performance improvement in :func:`read_hdf` for the fixed (default) format, especially on large frames (:issue:`47726`)
 - Performance improvement in :func:`read_html` and the Python CSV parser when ``thousands`` is set, fixing catastrophic regex backtracking on cells with many comma-separated digit groups followed by non-numeric text (:issue:`52619`)
 - Performance improvement in :func:`read_sas` by reading page header fields directly in Cython instead of falling back to Python (:issue:`47339`)
 - Performance improvement in :func:`read_sas` for SAS7BDAT files by pre-computing date/datetime column classification once during metadata parsing instead of per chunk (:issue:`47339`)
@@ -172,6 +173,7 @@ Performance improvements
 - Performance improvement in :meth:`DataFrame.sort_values` with multiple numeric columns by avoiding unnecessary :class:`Categorical` conversion (:issue:`15389`)
 - Performance improvement in :meth:`DataFrame.sum`, :meth:`DataFrame.prod`, :meth:`DataFrame.min`, :meth:`DataFrame.max`, :meth:`DataFrame.mean`, :meth:`DataFrame.any`, and :meth:`DataFrame.all` with ``axis=1`` for multi-block DataFrames by avoiding a transpose (:issue:`51474`)
 - Performance improvement in :meth:`DataFrame.to_excel` with the ``openpyxl`` engine when using ``engine_kwargs={"write_only": True}``, reducing memory consumption (:issue:`41681`)
+- Performance improvement in :meth:`DataFrame.to_hdf` and :meth:`HDFStore.append` for table format when appending to an existing wide table with many ``data_columns`` (:issue:`25839`)
 - Performance improvement in :meth:`DataFrame.to_stata` when writing object-dtype datetime columns with date formats that require year/month extraction (:issue:`64555`)
 - Performance improvement in :meth:`DataFrame.unstack` and :meth:`Series.unstack` when the :class:`MultiIndex` is already sorted and the unstacked level is the last level (:issue:`65107`)
 - Performance improvement in :meth:`DatetimeIndex.month_name` and :meth:`DatetimeIndex.day_name` when using the default string dtype by using PyArrow compute instead of going through an intermediate object array (:issue:`65104`)
@@ -179,6 +181,7 @@ Performance improvements
 - Performance improvement in :meth:`GroupBy.any` and :meth:`GroupBy.all` for boolean-dtype columns (:issue:`37850`)
 - Performance improvement in :meth:`GroupBy.first` and :meth:`GroupBy.last` for Extension Array dtypes, which no longer fall back to a slow ``apply``-based implementation (:issue:`57591`)
 - Performance improvement in :meth:`GroupBy.quantile` (:issue:`64330`)
+- Performance improvement in :meth:`HDFStore.select_as_multiple` when no ``where`` clause is given, by avoiding a coordinate-based read (:issue:`26771`)
 - Performance improvement in :meth:`Index.get_indexer` for large monotonic indexes, which now uses binary search instead of building a hash table when the number of targets is small (:issue:`14273`)
 - Performance improvement in :meth:`Index.join` and :meth:`Index.union` for :class:`RangeIndex` by avoiding unnecessary memory allocation in the libjoin fastpath (:issue:`54646`)
 - Performance improvement in :meth:`IntervalIndex.get_indexer` for monotonic non-overlapping indexes, which now uses binary search instead of the interval tree (:issue:`47614`)
diff --git a/pandas/io/pytables.py b/pandas/io/pytables.py
index 03da842157e1e..4870f9627460f 100644
--- a/pandas/io/pytables.py
+++ b/pandas/io/pytables.py
@@ -255,6 +255,29 @@ def _ensure_term(where, scope_level: int):
 _table_file_open_policy_is_strict = False
 
 
+_MISSING = object()
+
+
+def _set_attr_if_changed(attrs, name: str, value) -> None:
+    """
+    setattr on a PyTables AttributeSet only if the on-disk value differs.
+
+    Re-writing an HDF5 attribute to the same value is expensive — pytables
+    deletes and re-creates it, hitting the disk per attribute. On wide-table
+    appends this dominates runtime (GH#25839).
+    """
+    current = getattr(attrs, name, _MISSING)
+    if current is _MISSING:
+        setattr(attrs, name, value)
+        return
+    try:
+        equal = bool(current == value)
+    except (ValueError, TypeError):
+        equal = False
+    if not equal:
+        setattr(attrs, name, value)
+
+
 def _tables():
     global _table_mod
     global _table_file_open_policy_is_strict
@@ -2228,7 +2251,10 @@ def get_result(self, coordinates: bool = False):
             return self
 
         # if specified read via coordinates (necessary for multiple selections
-        if coordinates:
+        # so each table reads the same row set). Skip when self.where is None
+        # since every row would be selected anyway, and a coordinate-based read
+        # is much slower than a sequential read (GH#26771).
+        if coordinates and self.where is not None:
             if not isinstance(self.s, Table):
                 raise TypeError("can only read_coordinates on a table")
             where = self.s.read_coordinates(
@@ -2533,7 +2559,7 @@ def set_info(self, info) -> None:
 
     def set_attr(self) -> None:
         """set the kind for this column"""
-        setattr(self.attrs, self.kind_attr, self.kind)
+        _set_attr_if_changed(self.attrs, self.kind_attr, self.kind)
 
     def validate_metadata(self, handler: AppendableTable) -> None:
         """validate that kind=category does not change the categories"""
@@ -2888,10 +2914,10 @@ def convert(self, values: np.ndarray, nan_rep, encoding: str, errors: str):
 
     def set_attr(self) -> None:
         """set the data for this column"""
-        setattr(self.attrs, self.kind_attr, self.values)
-        setattr(self.attrs, self.meta_attr, self.meta)
+        _set_attr_if_changed(self.attrs, self.kind_attr, self.values)
+        _set_attr_if_changed(self.attrs, self.meta_attr, self.meta)
         assert self.dtype is not None
-        setattr(self.attrs, self.dtype_attr, self.dtype)
+        _set_attr_if_changed(self.attrs, self.dtype_attr, self.dtype)
 
 
 class DataIndexableCol(DataCol):
@@ -3633,7 +3659,7 @@ def read(
             dfs.append(df)
 
         if len(dfs) > 0:
-            out = concat(dfs, axis=1).copy()
+            out = concat(dfs, axis=1)
             return out.reindex(columns=items)
 
         return DataFrame(columns=axes[0], index=axes[1])