Put zarr, tileDB and hub benchmarks in one file

[DebadityaPal]

A preliminary attempt at standardizing the benchmarks. Also did some optimizations. Linked to #529

[github-actions]

Locust summary

Git references

Initial: 0128704
Terminal: d76b956

---

benchmarks/benchmark_tiledb_zarr_hub.py

Changes:

• Name: time_tiledb
  Type: function
  Changed lines: 33
  Total lines: 33
• Name: time_zarr
  Type: function
  Changed lines: 44
  Total lines: 44
• Name: time_hub
  Type: function
  Changed lines: 20
  Total lines: 20

[codecov]

Codecov Report

Merging #534 (f90f962) into master (4d40553) will not change coverage.
The diff coverage is n/a.

@@           Coverage Diff           @@
##           master     #534   +/-   ##
=======================================
  Coverage   88.46%   88.46%           
=======================================
  Files          52       52           
  Lines        3745     3745           
=======================================
  Hits         3313     3313           
  Misses        432      432           

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[haiyangdeperci]

Good job! Could you rename the file to benchmark_sequential_access.py?

[DebadityaPal]

Yeah, I changed it to the more appropriate name.

[haiyangdeperci]

@DebadityaPal Great! I'll review it soon.

[mynameisvinn]

@DebadityaPal I'll defer to @haiyangdeperci but I see duplicate code that can be factored out.

for example:

with Timer("Time"):
    ...

[DebadityaPal]

@mynameisvinn I have removed the duplicate code. Thanks for pointing it out.

[mynameisvinn]

@DebadityaPal much better! There is nothing better than concise, well organized code :-)

[haiyangdeperci]

Is it absolutely necessary to put the the entire dataset in memory as below:

ds["image"].compute().reshape(ds.shape[0], -1),

The machine we are using for benchmarks has 160 GB of memory. This step requires 330 GiB of memory.

MemoryError: Unable to allocate 330. GiB for an array with shape (1803460, 256, 256, 3) and data type uint8

I've noted some other inefficiencies and I am working on them as well but this one is a blocker.

[haiyangdeperci]

@DebadityaPal I am still having some memory issues after the patch. For instance:

Traceback (most recent call last):
  File "Hub/benchmarks/benchmark_sequential_access.py", line 165, in <module>
    time_tiledb(dataset, batch_size, split)
  File "Hub/benchmarks/benchmark_sequential_access.py", line 90, in time_tiledb
    ds_tldb[batch * batch_size : (batch + 1) * batch_size] = ds_numpy
  File "tiledb/libtiledb.pyx", line 4909, in tiledb.libtiledb.DenseArrayImpl.__setitem__
  File "tiledb/libtiledb.pyx", line 416, in tiledb.libtiledb._write_array
  File "tiledb/libtiledb.pyx", line 480, in tiledb.libtiledb._raise_ctx_err
  File "tiledb/libtiledb.pyx", line 465, in tiledb.libtiledb._raise_tiledb_error
tiledb.libtiledb.TileDBError: [TileDB::ChunkedBuffer] Error: Chunk write error; malloc() failed

[DebadityaPal]

The current changes resolve the memory crash issue. The whole thing should take less than 15GB of ram now.

[haiyangdeperci]

@DebadityaPal Great! Thanks for the update. I'll check it out right away