pandas pytables append: performance and increase in file size

I have more than 500 PyTables stores that contain about 300Mb of data each. I would like to merge these files into a big store, using pandas append as in the code below.

def merge_hdfs(file_list, merged_store):
    for file in file_list:
        store = HDFStore(file, mode='r')
        merged_store.append('data', store.data)
        store.close()

The append operation is very slow (it is taking up to 10 minutes to append a single store to merged_store), and strangely the file size of merged_store seems to be increasing by 1Gb for each appended store.

I have indicated the total number of expected rows which according to the documentation should improve performance, and having read Improve pandas (PyTables?) HDF5 table write performance I was expecting large write times, but almost 10 minutes for every 300Mb seems to be too slow, and I cannot understand why the increase in size.

I wonder if I am missing something?

For additional information, here is a description of one of the 500 PyTables.

/data/table (Table(272734,)) ''
  description := {
  "index": Int64Col(shape=(), dflt=0, pos=0),
  "values_block_0": Float64Col(shape=(6,), dflt=0.0, pos=1),
  "id": StringCol(itemsize=11, shape=(), dflt='', pos=2),
  "datetaken": Int64Col(shape=(), dflt=0, pos=3),
  "owner": StringCol(itemsize=15, shape=(), dflt='', pos=4),
  "machine_tags": StringCol(itemsize=100, shape=(), dflt='', pos=5),
  "title": StringCol(itemsize=200, shape=(), dflt='', pos=6),
  "country": StringCol(itemsize=3, shape=(), dflt='', pos=7),
  "place_id": StringCol(itemsize=18, shape=(), dflt='', pos=8),
  "url_s": StringCol(itemsize=80, shape=(), dflt='', pos=9),
  "url_o": StringCol(itemsize=80, shape=(), dflt='', pos=10),
  "ownername": StringCol(itemsize=50, shape=(), dflt='', pos=11),
  "tags": StringCol(itemsize=505, shape=(), dflt='', pos=12)}
  byteorder := 'little'
  chunkshape := (232,)

Answers


This is basically the answer here, which I recently answered.

Bottom line is this, you need to turn off indexing store.append('df',df,index=False). When creating the store, then index it at the end.

Furthermore turn off compression when merging the tables as well.

Indexing is a fairly expensive operation and if I recall correctly, only uses a single processor.

Finally, make sure that you create the merged with with mode='w' as all of the subsequent operations are appends and you want to start with a clean new file.

I also would NOT specify the chunksize upfront. Rather, after you have created the final index, perform the compression using ptrepack and specify chunksize=auto which will compute it for you. I don't think this will affect write performance but will optimize query performance.

You might try tweaking the chunksize parameter to append (this is the writing chunksize) to a larger number as well.

Obviously make sure that each of the appending tables has exactly the same structure (will raise if this is not the case).

I created this issue for an enhancement to do this 'internally': https://github.com/pydata/pandas/issues/6837


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