title: why does hdf5 file size increase dramatically when I segment the data to 30 smaller dataframes with 30 different keys
body: <p>I have a series of hdf5 files holding large panda data frames.
  a typical file is around 1000,000 rows. I use complib='blosc',complevel=9 for compression.
  original hdf5 files are saved as 1 flat file.</p>
  <p>I then tried segmenting the dataframes logically to 30 smaller dataframes and saved them in the same hdf5 file with 30 different keys and the same compression.</p>
  <p>The shocking issue is that the files with 30 smaller dataframes are 40X larger than the flat file.</p>
  <p>flat hdf5 file is saved as follows:</p>
  <p><code>dfx.to_hdf(file_name, key='opp',mode='a',complib='blosc',complevel=9, append=True)</code></p>
  <p>segemented hdf5 file is saved as follows:</p>
  <p><code>for i in range(30): dfx_small[i].to_hdf(file_name,key='d'+str(i), mode='a',complib='blosc',complevel=9 </code></p>
  <p>Am I doing something wrong or is this size increase expected?</p>
  <h2>Additonal Observation</h2>
  <p>I compared all the hdf5 files generated as 1)flat dataframe vs 2)30 chunk dataframe -  It seems largest files gain 8x to 10x in size when dataframe is saved as 30 smaller dataframe, meanwhile smaller files gain 100x to 1000x in size.
  I then experimented with saving the hdf5 files with 30 chunks with and without compression.  It seems that when multiple dataframe with unique keys are placed in the same hdf5 file, the compression is almost non functional.  I've tried all compression options with similar results.</p>
  <p>There is a bug with compression when saving hdf5 files with multiple datasets.</p>
  <p>I added Issue #45286</p>