Improve DataFusion scalability as more cores are added

[andygrove]

Is your feature request related to a problem or challenge?

I ran some benchmarks in constrained Docker containers and found that DataFusion is pretty close to DuckDB speed when running on a single core but does not scale as well as DuckDB when more cores are added.

At 16 cores, DuckDB was ~2.9x faster than DataFusion for this particular test.

Cores	DuckDB 0.7.1	DataFusion Python 21.0.0	DuckDB x Faster
1	315,630.9	318,037.8	1.0
2	155,034.5	197,257.3	1.3
4	75,110.0	111,243.1	1.5
8	37,584.9	73,142.3	1.9
16	18,880.3	55,071.8	2.9

Describe the solution you'd like

I would like DataFusion to scale as well as DuckDB as more cores are added.

Describe alternatives you've considered

No response

Additional context

Instructions for creating the Docker images can be found at https://github.com/sql-benchmarks/sqlbench-runners

[alamb]

Possibly related: #5995

[Dandandan]

Suggestion I posted in slack:

I wonder whether there might be some regressions here wrt scalability with the nr of cores - #4219 comes to mind which makes building the hashmap for the build-side done in a single thread for smaller tables
might be checked by changing datafusion.optimizer.hash_join_single_partition_threshold

[Dandandan]

For tpch-h q1 (SF=1) the metrics are as follows for the complete plan with metrics is as follows:

SortExec: [l_returnflag@0 ASC NULLS LAST,l_linestatus@1 ASC NULLS LAST], metrics=[output_rows=4, elapsed_compute=25.042µs, spill_count=0, spilled_bytes=0]
  CoalescePartitionsExec, metrics=[output_rows=4, elapsed_compute=3.167µs, spill_count=0, spilled_bytes=0, mem_used=0]
    ProjectionExec: expr=[l_returnflag@0 as l_returnflag, l_linestatus@1 as l_linestatus, SUM(lineitem.l_quantity)@2 as sum_qty, SUM(lineitem.l_extendedprice)@3 as sum_base_price, SUM(lineitem.l_extendedprice * Int64(1) - lineitem.l_discount)@4 as sum_disc_price, SUM(lineitem.l_extendedprice * Int64(1) - lineitem.l_discount * Int64(1) + lineitem.l_tax)@5 as sum_charge, AVG(lineitem.l_quantity)@6 as avg_qty, AVG(lineitem.l_extendedprice)@7 as avg_price, AVG(lineitem.l_discount)@8 as avg_disc, COUNT(UInt8(1))@9 as count_order], metrics=[output_rows=4, elapsed_compute=8.376µs, spill_count=0, spilled_bytes=0, mem_used=0]
      AggregateExec: mode=FinalPartitioned, gby=[l_returnflag@0 as l_returnflag, l_linestatus@1 as l_linestatus], aggr=[SUM(lineitem.l_quantity), SUM(lineitem.l_extendedprice), SUM(lineitem.l_extendedprice * Int64(1) - lineitem.l_discount), SUM(lineitem.l_extendedprice * Int64(1) - lineitem.l_discount * Int64(1) + lineitem.l_tax), AVG(lineitem.l_quantity), AVG(lineitem.l_extendedprice), AVG(lineitem.l_discount), COUNT(UInt8(1))], metrics=[output_rows=4, elapsed_compute=181.588µs, spill_count=0, spilled_bytes=0, mem_used=0]
        CoalesceBatchesExec: target_batch_size=8192, metrics=[output_rows=64, elapsed_compute=350.731µs, spill_count=0, spilled_bytes=0, mem_used=0]
          RepartitionExec: partitioning=Hash([Column { name: "l_returnflag", index: 0 }, Column { name: "l_linestatus", index: 1 }], 16), input_partitions=16, metrics=[repart_time=296.707µs, fetch_time=1.652179502s, send_time=110.333µs]
            AggregateExec: mode=Partial, gby=[l_returnflag@5 as l_returnflag, l_linestatus@6 as l_linestatus], aggr=[SUM(lineitem.l_quantity), SUM(lineitem.l_extendedprice), SUM(lineitem.l_extendedprice * Int64(1) - lineitem.l_discount), SUM(lineitem.l_extendedprice * Int64(1) - lineitem.l_discount * Int64(1) + lineitem.l_tax), AVG(lineitem.l_quantity), AVG(lineitem.l_extendedprice), AVG(lineitem.l_discount), COUNT(UInt8(1))], metrics=[output_rows=64, elapsed_compute=670.144742ms, spill_count=0, spilled_bytes=0, mem_used=0]
              ProjectionExec: expr=[CAST(l_extendedprice@1 AS Decimal128(38, 4)) * CAST(Some(100),23,2 - CAST(l_discount@2 AS Decimal128(23, 2)) AS Decimal128(38, 4)) as CAST(lineitem.l_extendedprice AS Decimal128(38, 4)) * CAST(Decimal128(Some(100),23,2) - CAST(lineitem.l_discount AS Decimal128(23, 2)) AS Decimal128(38, 4))CAST(Decimal128(Some(100),23,2) - CAST(lineitem.l_discount AS Decimal128(23, 2)) AS Decimal128(38, 4))Decimal128(Some(100),23,2) - CAST(lineitem.l_discount AS Decimal128(23, 2))CAST(lineitem.l_discount AS Decimal128(23, 2))lineitem.l_discountDecimal128(Some(100),23,2)CAST(lineitem.l_extendedprice AS Decimal128(38, 4))lineitem.l_extendedprice, l_quantity@0 as l_quantity, l_extendedprice@1 as l_extendedprice, l_discount@2 as l_discount, l_tax@3 as l_tax, l_returnflag@4 as l_returnflag, l_linestatus@5 as l_linestatus], metrics=[output_rows=5916591, elapsed_compute=162.828798ms, spill_count=0, spilled_bytes=0, mem_used=0]
                CoalesceBatchesExec: target_batch_size=8192, metrics=[output_rows=5916591, elapsed_compute=29.656446ms, spill_count=0, spilled_bytes=0, mem_used=0]
                  FilterExec: l_shipdate@6 <= 10471, metrics=[output_rows=5916591, elapsed_compute=54.707373ms, spill_count=0, spilled_bytes=0, mem_used=0]
                    ParquetExec: limit=None, partitions={16 groups: [[...]]}, predicate=l_shipdate <= Date32("10471"), pruning_predicate=l_shipdate_min@0 <= 10471, projection=[l_quantity, l_extendedprice, l_discount, l_tax, l_returnflag, l_linestatus, l_shipdate], metrics=[output_rows=6001215, elapsed_compute=16ns, spill_count=0, spilled_bytes=0, mem_used=0, row_groups_pruned=0, predicate_evaluation_errors=0, bytes_scanned=44132502, num_predicate_creation_errors=0, pushdown_rows_filtered=0, page_index_rows_filtered=0, pushdown_eval_time=32ns, time_elapsed_scanning_until_data=493.033123ms, time_elapsed_scanning_total=1.643740915s, time_elapsed_processing=290.136543ms, time_elapsed_opening=6.494334ms, page_index_eval_time=32ns]

Relevant metrics:

• ParquetExec time_elapsed_scanning_until_data=493.033123ms
• ParquetExec time_elapsed_scanning_total=1.643740915s
• ParquetExec time_elapsed_processing=290.136543ms
• ParquetExec time_elapsed_opening=6.494334ms
• FilterExec elapsed_compute=54.707373ms
• CoalesceBatchesExec elapsed_compute=29.656446ms
• ProjectionExec elapsed_compute=162.828798ms
• AggregateExec elapsed_compute=670.144742ms

[andygrove]

I ran the benchmarks with a simpler version of q1 with just the table scan and filter:

select
    *
from
	lineitem
where
	l_shipdate <= date '1998-12-01' - interval '113 days';

Here are the results:

Cores	DataFusion Python 22.0.0	DuckDB	DuckDB x Times Faster
1	14376	15000.9	0.96
2	7547.9	7915.7	0.95
4	4243.9	3850.7	1.10
8	2655.8	1869.3	1.42
16	2096.6	954.9	2.20
32	2252.2	531.7	4.24

[andygrove]

Here are the results for just the table scan:

select
    *
from
	lineitem;

Cores	DataFusion Python 22.0.0	DuckDB	DuckDB x Times Faster
1	14501.3	14751	0.98
2	7562.5	8135.8	0.93
4	4508.7	3618.6	1.25
8	2363.2	1837.5	1.29
16	2135.4	888	2.40
32	2355	511.4	4.61

This screenshot shows CPU usage with the DataFusion run on the left and the DuckDB run on the right, both using 32 cores.

Here is the directory listing of my lineitem directory:

$ ls -l /mnt/bigdata/tpch/sf10-parquet/lineitem.parquet/
total 2134532
-rw-rw-r-- 1 andy andy 91100218 Feb  3 08:45 part-0.parquet
-rw-rw-r-- 1 andy andy 90998179 Feb  3 08:46 part-10.parquet
-rw-rw-r-- 1 andy andy 91113498 Feb  3 08:46 part-11.parquet
-rw-rw-r-- 1 andy andy 90979631 Feb  3 08:46 part-12.parquet
-rw-rw-r-- 1 andy andy 91042031 Feb  3 08:46 part-13.parquet
-rw-rw-r-- 1 andy andy 91013004 Feb  3 08:46 part-14.parquet
-rw-rw-r-- 1 andy andy 91102199 Feb  3 08:46 part-15.parquet
-rw-rw-r-- 1 andy andy 91113429 Feb  3 08:46 part-16.parquet
-rw-rw-r-- 1 andy andy 91057291 Feb  3 08:46 part-17.parquet
-rw-rw-r-- 1 andy andy 90968073 Feb  3 08:46 part-18.parquet
-rw-rw-r-- 1 andy andy 91085729 Feb  3 08:47 part-19.parquet
-rw-rw-r-- 1 andy andy 91065849 Feb  3 08:45 part-1.parquet
-rw-rw-r-- 1 andy andy 90977725 Feb  3 08:47 part-20.parquet
-rw-rw-r-- 1 andy andy 91092444 Feb  3 08:47 part-21.parquet
-rw-rw-r-- 1 andy andy 91114853 Feb  3 08:47 part-22.parquet
-rw-rw-r-- 1 andy andy 91029929 Feb  3 08:47 part-23.parquet
-rw-rw-r-- 1 andy andy 91139350 Feb  3 08:46 part-2.parquet
-rw-rw-r-- 1 andy andy 91167180 Feb  3 08:46 part-3.parquet
-rw-rw-r-- 1 andy andy 91083996 Feb  3 08:46 part-4.parquet
-rw-rw-r-- 1 andy andy 91024700 Feb  3 08:46 part-5.parquet
-rw-rw-r-- 1 andy andy 91022201 Feb  3 08:46 part-6.parquet
-rw-rw-r-- 1 andy andy 91101885 Feb  3 08:46 part-7.parquet
-rw-rw-r-- 1 andy andy 91198702 Feb  3 08:46 part-8.parquet
-rw-rw-r-- 1 andy andy 91120030 Feb  3 08:46 part-9.parquet

[andygrove]

@Dandandan I added more benchmark results. The issues appear to be related to the ParquetExec.

[Dandandan]

I could also replicate the issue of non-perfect scaling with loading the tables in memory.

One thing I noticed is that the current round-robin RepartitionExec doesn't spread the batches evenly over the number of output channels, which can already be seen in MemoryExec itself:

MemoryExec: partitions=32, partition_sizes=[32, 32, 32, 32, 32, 32, 32, 32, 26, 26, 26, 25, 25, 25, 25, 25, 25, 25, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16], metrics=[]

It has a bias for the starting partitions as the outputs always go first to those channels.

[Dandandan]

Also, I wonder if DistributionSender / DistributionReceiver didn't introduce any regressions here FYI @crepererum

[crepererum]

Also, I wonder if DistributionSender / DistributionReceiver didn't introduce any regressions here FYI @crepererum

Definitely possible, even if it's only due to changes it triggers in tokio's scheduler. On the other hand, tokio's scheduler isn't really optimized for the DataFusion use case in the first place (as @tustvold probably pointed out before).

[cristian-ilies-vasile]

There is a small application written in Rust called odbc2parquet that reads data from databases and exports parquet files.
For testing on dummy data I think it is useful.

https://github.com/pacman82/odbc2parquet

[andygrove]

Here are latest results with DataFusion Python 23, which fixes a bug around how the Tokio runtime was being managed.

Cores	DuckDB 0.7.1	DataFusion Python 21.0.0	DataFusion Python 23.0.0	DuckDB x Faster
1	315,631	318,038	218,604	0.7
2	155,035	197,257	142,480	0.9
4	75,110	111,243	93,804	1.2
8	37,585	73,142	58,083	1.5
16	18,880	55,072	45,016	2.4

[Dandandan]

Interestingly, the speedup seems bigger for fewer cores.

There have been quite a few improvement for Q1 between 21 and 23 for aggregates and casts, that might explain most of the difference.

[alamb]

I believe #6929 from @YjyJeff will perhaps help with this