From e72cfcb744054fc63596a269a1f2065178593d0c Mon Sep 17 00:00:00 2001 From: Dmitry Chigarev Date: Thu, 11 Apr 2024 21:38:38 +0200 Subject: [PATCH] update docs Signed-off-by: Dmitry Chigarev --- .../range_partitioning_groupby.rst | 439 +++++++++++++++++- .../drop_duplicates_16cpus.jpg | Bin 0 -> 216221 bytes .../drop_duplicates_44cpus.jpg | Bin 0 -> 207763 bytes .../merge_h2o_500mb.jpg | Bin 0 -> 164869 bytes .../merge_h2o_5gb.png | Bin 0 -> 51939 bytes .../nunique_16cpus.jpg | Bin 0 -> 196913 bytes .../resample_16cpus.jpg | Bin 0 -> 104494 bytes .../unique_16cpus.jpg | Bin 0 -> 190613 bytes .../unique_44cpus.jpg | Bin 0 -> 201644 bytes 9 files changed, 418 insertions(+), 21 deletions(-) create mode 100644 docs/img/range_partitioning_measurements/drop_duplicates_16cpus.jpg create mode 100644 docs/img/range_partitioning_measurements/drop_duplicates_44cpus.jpg create mode 100644 docs/img/range_partitioning_measurements/merge_h2o_500mb.jpg create mode 100644 docs/img/range_partitioning_measurements/merge_h2o_5gb.png create mode 100644 docs/img/range_partitioning_measurements/nunique_16cpus.jpg create mode 100644 docs/img/range_partitioning_measurements/resample_16cpus.jpg create mode 100644 docs/img/range_partitioning_measurements/unique_16cpus.jpg create mode 100644 docs/img/range_partitioning_measurements/unique_44cpus.jpg diff --git a/docs/flow/modin/experimental/range_partitioning_groupby.rst b/docs/flow/modin/experimental/range_partitioning_groupby.rst index 38c10300aaf..799733ff5ec 100644 --- a/docs/flow/modin/experimental/range_partitioning_groupby.rst +++ b/docs/flow/modin/experimental/range_partitioning_groupby.rst @@ -1,10 +1,14 @@ +Range-partitioning in Modin +########################### Modin utilizes a range-partitioning approach for specific operations, significantly enhancing -parallelism and reducing memory consumption in certain scenarios. +parallelism and reducing memory consumption in certain scenarios. Range-partitioning is typically +engaged for operations that has key columns (to group on, to merge on, ...). -You can enable range-partitioning by specifying ``cfg.RangePartitioning`` :doc:`configuration variable: `: +You can enable `range-partitioning`_ by specifying ``cfg.RangePartitioning`` :doc:`configuration variable: ` .. code-block:: python + import modin.pandas as pd import modin.config as cfg @@ -17,15 +21,15 @@ You can enable range-partitioning by specifying ``cfg.RangePartitioning`` :doc:` pd.DataFrame(...).groupby(...).mean() # use MapReduce implementation for groupby.mean() -Building range-partitioning assumes data reshuffling, which may result into order of rows different from -pandas in certain operations. +Building range-partitioning assumes data reshuffling, which may result into breaking the original +order of rows, for some operation, it will mean that the result will be different from Pandas. Range-partitioning is not a silver bullet, meaning that enabling it is not always beneficial. Below you find a list of operations that have support for range-partitioning and practical advices on when one should enable it. -Range-partitioning GroupBy -"""""""""""""""""""""""""" +GroupBy +======= TODO: rewrite this section @@ -101,8 +105,8 @@ implementation with the respective warning if it meets an unsupported case: ... # https://github.com/modin-project/modin/issues/5926 ... # Falling back to a TreeReduce implementation. -Range-partitioning Merge -"""""""""""""""""""""""" +Merge +===== .. note:: Range-partitioning approach is implemented only for "left" and "inner" merge and only @@ -112,31 +116,342 @@ Range-partitioning merge replaces broadcast merge. It is recommended to use rang if the right dataframe in merge is as big as the left dataframe. In this case, range-partitioning implementation works faster and consumes less RAM. -TODO: add perf measurements from https://github.com/modin-project/modin/pull/6966 +Under the spoiler you can find performance comparison of range-partitioning and broadcast merge in different +scenarios: + +.. raw:: html + +
+ Performance measurements for merge + +The performance was measured on `h2o join queries`_ using Intel(R) Xeon(R) Gold 6238R CPU @ 2.20GHz (56 cores), +with the number of cores allocated for Modin limited by 44 (``MODIN_CPUS=44``). + +Measurements for small 500mb data: + +.. image:: /img/range_partitioning_measurements/merge_h2o_500mb.jpg + :align: center + +Measurements for medium 5gb data: + +.. image:: /img/range_partitioning_measurements/merge_h2o_5gb.png + :align: center + +.. raw:: html -'.unique()' and '.drop_duplicates()' -"""""""""""""""""""""""""""""""""""" +
-Range-partitioning implementation of '.unique()'/'.drop_duplicates()' works best when the input data size is big (more than + +``.unique()`` and ``.drop_duplicates()`` +======================================== + +.. note:: + When range-partitioning is enabled, both ``.unique()`` and ``.drop_duplicates()`` will + yield results that are sorted along rows. If range-partitioning is disabled, + the original order will be maintained. + +Range-partitioning implementation of ``.unique()`` / ``.drop_duplicates()`` works best when the input data size is big (more than 5_000_000 rows) and when the output size is also expected to be big (no more than 80% values are duplicates). -TODO: add perf measurements from https://github.com/modin-project/modin/pull/7091 +Under the spoiler you can find performance comparisons in different scenarios: + +.. raw:: html + +
+ Performance measurements for ``.unique()`` + +The performance was measured on randomly generated data using Intel(R) Xeon(R) Gold 6238R CPU @ 2.20GHz (56 cores). +The `duplicate rate` shows the procentage. You can learn more about this micro-benchmark by reading its source code: + +.. raw:: html + +
+ Micro-benchmark's source code + +.. code-block:: python + + import modin.pandas as pd + import numpy as np + import modin.config as cfg + + from modin.utils import execute + from timeit import default_timer as timer + import pandas + + cfg.CpuCount.put(16) + + def get_data(nrows, dtype): + if dtype == int: + return np.arange(nrows) + elif dtype == float: + return np.arange(nrows).astype(float) + elif dtype == str: + return np.array([f"value{i}" for i in range(nrows)]) + else: + raise NotImplementedError(dtype) + + pd.DataFrame(np.arange(cfg.NPartitions.get() * cfg.MinPartitionSize.get())).to_numpy() + + nrows = [1_000_000, 5_000_000, 10_000_000, 25_000_000, 50_000_000, 100_000_000] + duplicate_rate = [0, 0.1, 0.5, 0.95] + dtypes = [int, str] + use_range_part = [True, False] + + columns = pandas.MultiIndex.from_product([dtypes, duplicate_rate, use_range_part], names=["dtype", "duplicate rate", "use range-part"]) + result = pandas.DataFrame(index=nrows, columns=columns) + + i = 0 + total_its = len(nrows) * len(duplicate_rate) * len(dtypes) * len(use_range_part) + + for dt in dtypes: + for nrow in nrows: + data = get_data(nrow, dt) + np.random.shuffle(data) + for dpr in duplicate_rate: + data_c = data.copy() + dupl_val = data_c[0] + + num_duplicates = int(dpr * nrow) + dupl_indices = np.random.choice(np.arange(nrow), num_duplicates, replace=False) + data_c[dupl_indices] = dupl_val + + for impl in use_range_part: + print(f"{round((i / total_its) * 100, 2)}%") + i += 1 + cfg.RangePartitioning.put(impl) + + sr = pd.Series(data_c) + execute(sr) + + t1 = timer() + # returns a list, so no need for materialization + sr.unique() + tm = timer() - t1 + print(nrow, dpr, dt, impl, tm) + result.loc[nrow, (dt, dpr, impl)] = tm + result.to_excel("unique.xlsx") + +.. raw:: html + +
+ +Measurements with 16 cores being allocated for Modin (``MODIN_CPUS=16``): + +.. image:: /img/range_partitioning_measurements/unique_16cpus.jpg + :align: center + +Measurements with 44 cores being allocated for Modin (``MODIN_CPUS=4``): + +.. image:: /img/range_partitioning_measurements/unique_44cpus.jpg + :align: center + +.. raw:: html + +
+ + +.. raw:: html + +
+ Performance measurements for ``.drop_duplicates()`` + +The performance was measured on randomly generated data using Intel(R) Xeon(R) Gold 6238R CPU @ 2.20GHz (56 cores). +The `duplicate rate` shows the procentage. The `subset size` shows the number of columns being specified as a ``subset`` +parameter for ``df.drop_duplicates()``. You can learn more about this micro-benchmark by reading its source code: + +.. raw:: html + +
+ Micro-benchmark's source code + +.. code-block:: python + + import modin.pandas as pd + import numpy as np + import modin.config as cfg + + from modin.utils import execute + from timeit import default_timer as timer + import pandas + + cfg.CpuCount.put(16) + + pd.DataFrame(np.arange(cfg.NPartitions.get() * cfg.MinPartitionSize.get())).to_numpy() + + nrows = [1_000_000, 5_000_000, 10_000_000, 25_000_000] + duplicate_rate = [0, 0.1, 0.5, 0.95] + subset = [["col0"], ["col1", "col2", "col3", "col4"], None] + ncols = 15 + use_range_part = [True, False] + + columns = pandas.MultiIndex.from_product( + [ + [len(sbs) if sbs is not None else ncols for sbs in subset], + duplicate_rate, + use_range_part + ], + names=["subset size", "duplicate rate", "use range-part"] + ) + result = pandas.DataFrame(index=nrows, columns=columns) + + i = 0 + total_its = len(nrows) * len(duplicate_rate) * len(subset) * len(use_range_part) + + for sbs in subset: + for nrow in nrows: + data = {f"col{i}": np.arange(nrow) for i in range(ncols)} + pandas_df = pandas.DataFrame(data) + + for dpr in duplicate_rate: + pandas_df_c = pandas_df.copy() + dupl_val = pandas_df_c.iloc[0] + + num_duplicates = int(dpr * nrow) + dupl_indices = np.random.choice(np.arange(nrow), num_duplicates, replace=False) + pandas_df_c.iloc[dupl_indices] = dupl_val + + for impl in use_range_part: + print(f"{round((i / total_its) * 100, 2)}%") + i += 1 + cfg.RangePartitioning.put(impl) + + md_df = pd.DataFrame(pandas_df_c) + execute(md_df) + + t1 = timer() + res = md_df.drop_duplicates(subset=sbs) + execute(res) + tm = timer() - t1 + + sbs_s = len(sbs) if sbs is not None else ncols + print("len()", res.shape, nrow, dpr, sbs_s, impl, tm) + result.loc[nrow, (sbs_s, dpr, impl)] = tm + result.to_excel("drop_dupl.xlsx") + +.. raw:: html + +
+ +Measurements with 16 cores being allocated for Modin (``MODIN_CPUS=16``): + +.. image:: /img/range_partitioning_measurements/drop_duplicates_16cpus.jpg + :align: center + +Measurements with 44 cores being allocated for Modin (``MODIN_CPUS=44``): + +.. image:: /img/range_partitioning_measurements/drop_duplicates_44cpus.jpg + :align: center + +.. raw:: html + +
+ '.nunique()' -"""""""""""""""""""""""""""""""""""" +============ .. note:: - Range-partitioning approach is implemented only for 'pd.Series.nunique()' and 1-column dataframes. - For multi-column dataframes '.nunique()' can only use full-axis reduce implementation. + Range-partitioning approach is implemented only for ``pd.Series.nunique()`` and 1-column dataframes. + For multi-column dataframes ``.nunique()`` can only use full-axis reduce implementation. Range-partitioning implementation of '.nunique()'' works best when the input data size is big (more than 5_000_000 rows) and when the output size is also expected to be big (no more than 80% values are duplicates). -TODO: add perf measurements from https://github.com/modin-project/modin/pull/7101 +Under the spoiler you can find performance comparisons in different scenarios: + +.. raw:: html + +
+ Performance measurements for ``.nunique()`` + +The performance was measured on randomly generated data using Intel(R) Xeon(R) Gold 6238R CPU @ 2.20GHz (56 cores). +The `duplicate rate` shows the procentage. You can learn more about this micro-benchmark by reading its source code: + +.. raw:: html + +
+ Micro-benchmark's source code + +.. code-block:: python + + import modin.pandas as pd + import numpy as np + import modin.config as cfg + + from modin.utils import execute + from timeit import default_timer as timer + import pandas + + cfg.CpuCount.put(16) + + def get_data(nrows, dtype): + if dtype == int: + return np.arange(nrows) + elif dtype == float: + return np.arange(nrows).astype(float) + elif dtype == str: + return np.array([f"value{i}" for i in range(nrows)]) + else: + raise NotImplementedError(dtype) + + pd.DataFrame(np.arange(cfg.NPartitions.get() * cfg.MinPartitionSize.get())).to_numpy() + + nrows = [1_000_000, 5_000_000, 10_000_000, 25_000_000, 50_000_000, 100_000_000] + duplicate_rate = [0, 0.1, 0.5, 0.95] + dtypes = [int, str] + use_range_part = [True, False] + + columns = pandas.MultiIndex.from_product([dtypes, duplicate_rate, use_range_part], names=["dtype", "duplicate rate", "use range-part"]) + result = pandas.DataFrame(index=nrows, columns=columns) + + i = 0 + total_its = len(nrows) * len(duplicate_rate) * len(dtypes) * len(use_range_part) + + for dt in dtypes: + for nrow in nrows: + data = get_data(nrow, dt) + np.random.shuffle(data) + for dpr in duplicate_rate: + data_c = data.copy() + dupl_val = data_c[0] + + num_duplicates = int(dpr * nrow) + dupl_indices = np.random.choice(np.arange(nrow), num_duplicates, replace=False) + data_c[dupl_indices] = dupl_val + + for impl in use_range_part: + print(f"{round((i / total_its) * 100, 2)}%") + i += 1 + cfg.RangePartitioning.put(impl) + + sr = pd.Series(data_c) + execute(sr) + + t1 = timer() + # returns a scalar, so no need for materialization + res = sr.nunique() + tm = timer() - t1 + print(nrow, dpr, dt, impl, tm) + result.loc[nrow, (dt, dpr, impl)] = tm + result.to_excel("nunique.xlsx") + +.. raw:: html + +
+ +Measurements with 16 cores being allocated for Modin (``MODIN_CPUS=16``): + +.. image:: /img/range_partitioning_measurements/nunique_16cpus.jpg + :align: center + + +.. raw:: html + +
Resample -"""""""" +======== .. note:: @@ -145,10 +460,92 @@ Resample It is recommended to use range-partitioning for resampling if you're dealing with a dataframe that has more than 5_000_000 rows and the expected output is also expected to be big (more than 500_000 rows). -TODO: add perf measurements from https://github.com/modin-project/modin/pull/7140 +Under the spoiler you can find performance comparisons in different scenarios: + +.. raw:: html + +
+ Performance measurements for ``.resample()`` + +The script below measures performance of ``df.resample(rule).sum()`` using Intel(R) Xeon(R) Gold 6238R CPU @ 2.20GHz (56 cores). +You can learn more about this micro-benchmark by reading its source code: + +.. raw:: html + +
+ Micro-benchmark's source code + +.. code-block:: python + + import pandas + import numpy as np + import modin.pandas as pd + import modin.config as cfg + + from timeit import default_timer as timer + + from modin.utils import execute + + cfg.CpuCount.put(16) + + nrows = [1_000_000, 5_000_000, 10_000_000] + ncols = [5, 33] + rules = [ + "500ms", # doubles nrows + "30s", # decreases nrows in 30 times + "5min", # decreases nrows in 300 + ] + use_rparts = [True, False] + + cols = pandas.MultiIndex.from_product([rules, ncols, use_rparts], names=["rule", "ncols", "USE RANGE PART"]) + rres = pandas.DataFrame(index=nrows, columns=cols) + + total_nits = len(nrows) * len(ncols) * len(rules) * len(use_rparts) + i = 0 + + for nrow in nrows: + for ncol in ncols: + index = pandas.date_range("31/12/2000", periods=nrow, freq="s") + data = {f"col{i}": np.arange(nrow) for i in range(ncol)} + pd_df = pandas.DataFrame(data, index=index) + for rule in rules: + for rparts in use_rparts: + print(f"{round((i / total_nits) * 100, 2)}%") + i += 1 + cfg.RangePartitioning.put(rparts) + + df = pd.DataFrame(data, index=index) + execute(df) + + t1 = timer() + res = df.resample(rule).sum() + execute(res) + ts = timer() - t1 + print(nrow, ncol, rule, rparts, ts) + + rres.loc[nrow, (rule, ncol, rparts)] = ts + rres.to_excel("resample.xlsx") + +.. raw:: html + +
+ +Measurements with 16 cores being allocated for Modin (``MODIN_CPUS=16``): + +.. image:: /img/range_partitioning_measurements/resample_16cpus.jpg + :align: center + + +.. raw:: html + +
pivot_table -""""""""""" +=========== -Range-partitioning implementation is automatically applied for `df.pivot_table` +Range-partitioning implementation is automatically applied for ``df.pivot_table`` whenever possible, users can't control this. + + +.. _h2o join queries: https://h2oai.github.io/db-benchmark/ +.. _range-partitioning: https://www.techopedia.com/definition/31994/range-partitioning diff --git a/docs/img/range_partitioning_measurements/drop_duplicates_16cpus.jpg b/docs/img/range_partitioning_measurements/drop_duplicates_16cpus.jpg new file mode 100644 index 0000000000000000000000000000000000000000..5a090d79b0afdf2f465744bb88e08c746384c97e GIT binary patch literal 216221 zcmeEv2Ut_O#(t1(ryT+XH^uS$^1f~S_ZQnw?7KL2 zbArp@1`(mbLWQ}UN~A2zzldD@*%;n$}x_lR6BYvDH@ zpopHkbw7w}?-7Bc#{|X1CC;3cR8&${Ie$U*%2h3G9bLGdiK&^ng{76X{cVRkj!r1& z2OgeY-afv5kAg!&!@?g&#Kk8hCM7@nEhQr}D?2AQFTbF?qOz*GrnauWwXMCQv+GTF z&*0GT$mrOIaRQMv`)O|e^OuE1>e~9(4caEKwf${hY!LRpYzw^pa9;<(ef{+98E}5v 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