Parallelize Stateless (CSV/JSON) File Write Serialization #7452

devinjdangelo · 2023-08-30T19:40:32Z

Which issue does this PR close?

Part of #7079

Rationale for this change

Serialization of "stateless" file types (where the serialized bytes of each record batch has no dependency on the serialized bytes of any other record batch), can be parallelized efficiently across all available CPU cores for a significant decrease in the time needed to write out the file.

There is a tradeoff between write speed and memory utilization. If the ObjectStore writer cannot keep up with the data being serialized, bytes could accumulate in memory. ObjectStore puts are concurrent but not parallelized so the risk of higher memory usage increases as the number of cores in the system increases. We could potentially bound this increase in memory by throttling serialization tasks when more than X RecordBatches have been serialized but not yet written to the ObjectStore (this is done!).

What changes are included in this PR?

Spawn a tokio task to serialize each each record batch in parallel for "stateless" file types, concurrently write the serialized bytes to an ObjectStore.

Benchmarking

To benchmark the performance difference in this PR, the following script is used.

cd datafusion/benchmarks
./bench.sh data tpch10

use chrono;
use datafusion::prelude::*;
use datafusion_common::DataFusionError;
use object_store::local::LocalFileSystem;
use std::{io::Error, sync::Arc, time::Instant};
use url::Url;

const FILENAME: &str =
    "/home/dev/arrow-datafusion/benchmarks/data/tpch_sf10/lineitem/part-0.parquet";

#[tokio::main]
async fn main() -> Result<(), DataFusionError> {
    let _ctx = SessionContext::new();
    let local = Arc::new(LocalFileSystem::new());
    let local_url = Url::parse("file://local").unwrap();
    _ctx.runtime_env().register_object_store(&local_url, local);

    let _read_options = ParquetReadOptions::default();

    let start = Instant::now();
    let _df = _ctx
        .read_parquet(FILENAME, _read_options)
        .await
        .unwrap()
        //select a few columns with types compatible with write_json method
        .select_columns(&["l_orderkey", "l_partkey", "l_receiptdate"])?
        .repartition(Partitioning::Hash(vec![col("l_orderkey")], 4))?
        .cache()
        .await?;
    let elapsed = Instant::now() - start;
    println!("read parquet to memory took -> {elapsed:?}");

    let start3 = Instant::now();
    _df.clone()
        .write_csv("file://local/home/dev/arrow-datafusion/test_out/")
        .await?;
    let elapsed3 = Instant::now() - start3;
    println!("write as csv to disk took -> {elapsed3:?}");

    let start2 = Instant::now();
    _df.clone()
        .write_json("file://local/home/dev/arrow-datafusion/test_out/")
        .await?;
    let elapsed2 = Instant::now() - start2;
    println!("write as json to disk took -> {elapsed2:?}");

    Ok(())
}

Tables below report results on a 16c/32t development server with 128Gb of memory writing to fast local nvme storage. This is an ideal case, scaling will not be as good on systems with fewer threads, significantly less memory, or slower/remote storage.

Execution Time in Seconds

	main	This PR	% diff
1 JSON file	220.31	20.76	-91%
1 CSV file	144.31	11.82	-92%
4 JSON files	224.14	19.05	-92%
4 CSV files	147.11	11.46	-92%

Peak Memory Usage in GB

	main	This PR	% diff
1 JSON file	0.55	3.9	609%
1 CSV file	0.55	0.85	55%
4 JSON files	0.65	3.9	500%
4 CSV files	0.65	1.00	54%

After incorporating the channel method written by @metesynnada to introduce backpressure, memory usage increase is consistently ~200MB is all tests with no noticeable drop in performance.

Note: Peak memory usage figure subtracts the peak memory usage required to cache the DataFrame and not perform any writes.

On a laptop with 15t, I see 28.42s and 42.94s for the 4CSV and 4JSON file tests respectively.

Without the caching step, each write only takes about 1 extra second.

What about Parquet?

Serializing a parquet file is more complex (the parquet writer maintains internal state across record batches).. Serializing two different parquet files in parallel is trivial, but parallelizing serialization of a single large parquet file is more difficult. It may be possible by constructing multiple row groups in parallel. I intend to work on this in another PR.

Are these changes tested?

Yes by existing tests. More tests needed to verify abort behavior may be desired.

Are there any user-facing changes?

No, just faster writes!

devinjdangelo · 2023-08-31T16:39:33Z

datafusion/core/src/datasource/file_format/write.rs

+                    }
+                    false => {
+                        // TODO if we encounter an error during shutdown, delete previously written files?
+                        writer.shutdown()


I think this is the trickiest case to ensure the write is atomic. Suppose we have two writers A and B. Writer A could successfully commit and shutdown. Then, before Writer B can complete, a network or hardware fault could prevent Writer B from either finalizing or Writer A from Aborting.

For this to be atomic, we would need some way to simultaneously commit all or none of our multipart writers. I don't think ObjectStores (S3 ect) support a way to do that.

Downstream table providers could make this atomic in practice via an atomic metadata operation, which is I believe how DeltaLake and friends work.

Yes, I agree that if someone wants atomic commit/rollback they should build that in at a higher level than datafusion -- there isn't much we can do with just the object store API

devinjdangelo · 2023-08-31T16:42:05Z

datafusion/core/src/datasource/file_format/write.rs

+                        }
+                    },
+                    Err(_) => {
+                        // Don't panic, instead try to clean up as many writers as possible.


Join errors (perhaps because a thread was killed by the OS?) could also result in non atomic writes, since we would have no way to recover ownership of the writer and abort it.

You should return the error after aborting the writer, in case an execution error occurs within the plans.

devinjdangelo · 2023-08-31T16:43:53Z

datafusion/core/tests/fifo.rs

@@ -336,6 +336,7 @@ mod unix_test {

    /// It tests the INSERT INTO functionality.
    #[tokio::test]
+    #[ignore]


This test is deadlocking. I think this has to do with how this test is spawning threads.

Based on the other tests passing, I don't believe this PR has broken anything with FIFO tables.

@mustafasrepo Any idea how to fix this test for this PR?

I am currently checking for any potential problems.

It's not the way the threads are started that's the issue, but rather the assumption that if a FIFO file receives 10 rows and the batch size is also 10, it will create a record batch. The current implementation of serialize_rb_stream_to_object_store requires access to all data before it can proceed.

while let Some(maybe_batch) = data_stream.next().await { let mut serializer_clone = match serializer.duplicate() { Ok(s) => s, Err(_) => { return Err(( writer, DataFusionError::Internal( "Unknown error writing to object store".into(), ), )) } }; serialize_tasks.push(task::spawn(async move { let batch = maybe_batch?; let num_rows = batch.num_rows(); let bytes = serializer_clone.serialize(batch).await?; Ok((num_rows, bytes)) })); }

devinjdangelo · 2023-08-31T16:53:26Z

@alamb Any tips on how to benchmark this more scientifically? Would adding "insert into" and "copy" queries to our existing benchmarking framework be a reasonable idea?

metesynnada · 2023-09-04T11:50:25Z

datafusion/core/src/datasource/file_format/write.rs

+async fn serialize_rb_stream_to_object_store(
+    mut data_stream: Pin<Box<dyn RecordBatchStream + Send>>,
+    mut serializer: Box<dyn BatchSerializer>,
+    mut writer: AbortableWrite<Box<dyn AsyncWrite + Send + Unpin>>,
+) -> std::result::Result<
+    (
+        Box<dyn BatchSerializer>,
+        AbortableWrite<Box<dyn AsyncWrite + Send + Unpin>>,
+        u64,
+    ),
+    (
+        AbortableWrite<Box<dyn AsyncWrite + Send + Unpin>>,
+        DataFusionError,
+    ),
+> {
+    let mut row_count = 0;
+    // Not using JoinSet here since we want to ulimately write to ObjectStore preserving file order
+    let mut serialize_tasks: Vec<JoinHandle<Result<(usize, Bytes), DataFusionError>>> =
+        Vec::new();
+    while let Some(maybe_batch) = data_stream.next().await {
+        let mut serializer_clone = match serializer.duplicate() {
+            Ok(s) => s,
+            Err(_) => {
+                return Err((
+                    writer,
+                    DataFusionError::Internal(
+                        "Unknown error writing to object store".into(),
+                    ),
+                ))
+            }
+        };
+        serialize_tasks.push(task::spawn(async move {
+            let batch = maybe_batch?;
+            let num_rows = batch.num_rows();
+            let bytes = serializer_clone.serialize(batch).await?;
+            Ok((num_rows, bytes))
+        }));
+    }
+    for serialize_result in serialize_tasks {
+        let result = serialize_result.await;
+        match result {
+            Ok(res) => {
+                let (cnt, bytes) = match res {
+                    Ok(r) => r,
+                    Err(e) => return Err((writer, e)),
+                };
+                row_count += cnt;
+                match writer.write_all(&bytes).await {
+                    Ok(_) => (),
+                    Err(_) => {
+                        return Err((
+                            writer,
+                            DataFusionError::Internal(
+                                "Unknown error writing to object store".into(),
+                            ),
+                        ))
+                    }
+                };
+            }
+            Err(_) => {
+                return Err((
+                    writer,
+                    DataFusionError::Internal(
+                        "Unknown error writing to object store".into(),
+                    ),
+                ))
+            }
+        }
+    }
+
+    Ok((serializer, writer, row_count as u64))
+}


Suggested change

async fn serialize_rb_stream_to_object_store(

mut data_stream: Pin<Box<dyn RecordBatchStream + Send>>,

mut serializer: Box<dyn BatchSerializer>,

mut writer: AbortableWrite<Box<dyn AsyncWrite + Send + Unpin>>,

) -> std::result::Result<

(

Box<dyn BatchSerializer>,

AbortableWrite<Box<dyn AsyncWrite + Send + Unpin>>,

u64,

),

(

AbortableWrite<Box<dyn AsyncWrite + Send + Unpin>>,

DataFusionError,

),

> {

let mut row_count = 0;

// Not using JoinSet here since we want to ulimately write to ObjectStore preserving file order

let mut serialize_tasks: Vec<JoinHandle<Result<(usize, Bytes), DataFusionError>>> =

Vec::new();

while let Some(maybe_batch) = data_stream.next().await {

let mut serializer_clone = match serializer.duplicate() {

Ok(s) => s,

Err(_) => {

return Err((

writer,

DataFusionError::Internal(

"Unknown error writing to object store".into(),

),

))

}

};

serialize_tasks.push(task::spawn(async move {

let batch = maybe_batch?;

let num_rows = batch.num_rows();

let bytes = serializer_clone.serialize(batch).await?;

Ok((num_rows, bytes))

}));

}

for serialize_result in serialize_tasks {

let result = serialize_result.await;

match result {

Ok(res) => {

let (cnt, bytes) = match res {

Ok(r) => r,

Err(e) => return Err((writer, e)),

};

row_count += cnt;

match writer.write_all(&bytes).await {

Ok(_) => (),

Err(_) => {

return Err((

writer,

DataFusionError::Internal(

"Unknown error writing to object store".into(),

),

))

}

};

}

Err(_) => {

return Err((

writer,

DataFusionError::Internal(

"Unknown error writing to object store".into(),

),

))

}

}

}

Ok((serializer, writer, row_count as u64))

}

async fn serialize_rb_stream_to_object_store(

mut data_stream: Pin<Box<dyn RecordBatchStream + Send>>,

mut serializer: Box<dyn BatchSerializer>,

mut writer: AbortableWrite<Box<dyn AsyncWrite + Send + Unpin>>,

) -> std::result::Result<

(

Box<dyn BatchSerializer>,

AbortableWrite<Box<dyn AsyncWrite + Send + Unpin>>,

u64,

),

(

AbortableWrite<Box<dyn AsyncWrite + Send + Unpin>>,

DataFusionError,

),

> {

let (tx, mut rx) =

mpsc::channel::<JoinHandle<Result<(usize, Bytes), DataFusionError>>>(100); // buffer size of 100, adjust as needed

let serialize_task = tokio::spawn(async move {

while let Some(maybe_batch) = data_stream.next().await {

match serializer.duplicate() {

Ok(mut serializer_clone) => {

let handle = tokio::spawn(async move {

let batch = maybe_batch?;

let num_rows = batch.num_rows();

let bytes = serializer_clone.serialize(batch).await?;

Ok((num_rows, bytes))

});

tx.send(handle).await.map_err(|_| {

DataFusionError::Internal(

"Unknown error writing to object store".into(),

)

})?;

yield_now().await;

}

Err(_) => {

return Err(DataFusionError::Internal(

"Unknown error writing to object store".into(),

))

}

}

}

Ok(serializer)

});

let mut row_count = 0;

while let Some(handle) = rx.recv().await {

match handle.await {

Ok(Ok((cnt, bytes))) => {

match writer.write_all(&bytes).await {

Ok(_) => (),

Err(_) => {

return Err((

writer,

DataFusionError::Internal(

"Unknown error writing to object store".into(),

),

))

}

};

row_count += cnt;

}

Ok(Err(e)) => {

// Return the writer along with the error

return Err((writer, e));

}

Err(_) => {

// Handle task panic or cancellation

return Err((

writer,

DataFusionError::Internal(

"Serialization task panicked or was cancelled".into(),

),

));

}

}

}

let serializer = match serialize_task.await {

Ok(Ok(serializer)) => serializer,

Ok(Err(e)) => return Err((writer, e)),

Err(_) => {

return Err((

writer,

DataFusionError::Internal("Unknown error writing to object store".into()),

))

}

};

Ok((serializer, writer, row_count as u64))

}

}

How about this?

This is fantastic, thank you @metesynnada ! I tested this and with just a small change it resulted in identical performance with much lower memory usage due to the backpressure introduced by the channel. The FIFO tests is also passing now, so win win!

@metesynnada Actually, I just realized that the fifo test passing is dependent on leaving the yield_now().await call in. That call results in the serialize tasks being run in sequence rather than parallel though, so performance is poor. I'm not quite sure how to get the fifio test to pass without the yield_now().await call.

I believe we are facing the same issue at #5278. We could apply the same heuristic to #6290.

Also, is your profiling runtime multi-threaded?

Thanks for the issue links. I tried calling yield_now().await every N iterations, but the FIFO insert test only passes when N==1 (i.e. yield_now().await on every iteration).

I just pushed up a possible option of calling yield_now() if the input is unbounded, otherwise skipping it. That allows bounded tables to benefit fully from the parallelization but still allowing fifo tests to pass as they currently do.

The fifo test is not a multithreaded runtime, but when I benchmark performance I am running in a multithreaded runtime, yes.

I will check the entire pull request tomorrow. Thank you for your hard work. I am also pleased that the new mechanism has contributed to the performance.

Co-authored-by: Metehan Yıldırım <100111937+metesynnada@users.noreply.github.com>

alamb

Thank you @devinjdangelo -- I plan to study this PR carefully tomorrow

alamb · 2023-09-06T18:19:11Z

datafusion/core/src/datasource/file_format/write.rs

+                    }
+                    false => {
+                        // TODO if we encounter an error during shutdown, delete previously written files?
+                        writer.shutdown()


Yes, I agree that if someone wants atomic commit/rollback they should build that in at a higher level than datafusion -- there isn't much we can do with just the object store API

metesynnada

The PR looks great overall. Once the outstanding issues are resolved, I can give it a final review.

metesynnada · 2023-09-07T16:04:16Z

datafusion/core/src/datasource/file_format/write.rs

+    // tracks if any errors were encountered in the process of aborting writers.
+    // if true, we may not have a guarentee that all written data was cleaned up.
+    let mut any_abort_errors = false;
+    match single_file_output {


Can be if single_file_output { ... } else { ... }

metesynnada · 2023-09-07T16:04:47Z

datafusion/core/src/datasource/file_format/write.rs

+
+            // Finalize or abort writers as appropriate
+            for mut writer in finished_writers.into_iter() {
+                match any_errors {


Can be if any_errors { ... } else { ... }

metesynnada · 2023-09-07T16:11:12Z

datafusion/core/src/datasource/file_format/write.rs

+    };
+    Ok((serializer, writer, row_count as u64))
+}
+
 /// Contains the common logic for serializing RecordBatches and
 /// writing the resulting bytes to an ObjectStore.
 /// Serialization is assumed to be stateless, i.e.
 /// each RecordBatch can be serialized without any
 /// dependency on the RecordBatches before or after.
 pub(crate) async fn stateless_serialize_and_write_files(


The blocks inside the match serialize_rb_stream_to_object_store can be refactored into separate functions for better readability and maintainability.

pub(crate) async fn stateless_serialize_and_write_files( data: Vec<SendableRecordBatchStream>, mut serializers: Vec<Box<dyn BatchSerializer>>, mut writers: Vec<AbortableWrite<Box<dyn AsyncWrite + Send + Unpin>>>, single_file_output: bool, unbounded_input: bool, ) -> Result<u64> { if single_file_output { if serializers.len() != 1 || writers.len() != 1 { return internal_err!("single_file_output is true, but got more than 1 writer!"); } return serialize_single_file(data, serializers.remove(0), writers.remove(0), unbounded_input).await; } else { if data.len() != writers.len() { return internal_err!("Did not get 1 writer for each output partition!"); } return serialize_multiple_files(data, serializers, writers, unbounded_input).await; } }

metesynnada · 2023-09-07T16:22:56Z

datafusion/core/src/datasource/file_format/write.rs

+    mut writer: AbortableWrite<Box<dyn AsyncWrite + Send + Unpin>>,
+    unbounded_input: bool,
+) -> std::result::Result<
+    (


The function signature is quite complex with nested types. Using type aliases can simplify the signature and make it more readable. Like

type WriterType = AbortableWrite<Box<dyn AsyncWrite + Send + Unpin>>; type ResultTuple = (Box<dyn BatchSerializer>, WriterType, u64); type ErrorTuple = (WriterType, DataFusionError);

metesynnada · 2023-09-07T16:24:04Z

datafusion/core/src/datasource/file_format/write.rs

+                        Ok((num_rows, bytes))
+                    });
+                    tx.send(handle).await.map_err(|_| {
+                        DataFusionError::Internal(


Define the return error as a closure at the beginning of the method to make it common.

metesynnada · 2023-09-07T16:26:26Z

datafusion/core/src/datasource/file_format/write.rs

+                        }
+                    },
+                    Err(_) => {
+                        // Don't panic, instead try to clean up as many writers as possible.


You should return the error after aborting the writer, in case an execution error occurs within the plans.

metesynnada · 2023-09-07T16:29:55Z

datafusion/core/src/datasource/listing_table_factory.rs

@@ -137,8 +137,15 @@ impl TableProviderFactory for ListingTableFactory {
        let mut statement_options = StatementOptions::from(&cmd.options);

        // Extract ListingTable specific options if present or set default
-        // Discard unbounded option if present
-        statement_options.take_str_option("unbounded");
+        let unbounded = if infinite_source {


You can remove this logic since we support unbounded tables with CREATE UNBOUNDED EXTERNAL TABLE.

That syntax is supported, but the test for FIFO insert passes unbounded as a string option. The logic here was added to handle either case. Create unbounded syntax takes precedence, but if it is not present we check for OPTION (unbounded true).

https://github.com/apache/arrow-datafusion/blob/1dd0e988ceb6ecc1df4053fd7d065c15c6e8b431/datafusion/core/tests/fifo.rs#L399

I see, I think it is a leftover. I guess we can change the test into Create Unbounded External if I am not missing anything.

I think it is OK if both methods are supported. That would provide backwards compatibility for anyone who used this syntax in past versions.

metesynnada · 2023-09-07T16:30:51Z

datafusion/core/src/datasource/listing_table_factory.rs

@@ -213,7 +220,8 @@ impl TableProviderFactory for ListingTableFactory {
            .with_file_sort_order(cmd.order_exprs.clone())
            .with_insert_mode(insert_mode)
            .with_single_file(single_file)
-            .with_write_options(file_type_writer_options);
+            .with_write_options(file_type_writer_options)
+            .with_infinite_source(unbounded);


Please remove the line with .with_infinite_source(unbounded); as it duplicates the logic above.

I took the liberty of doing so in e6f1b2f

…writes

alamb

This is pretty compelling @devinjdangelo 👍 Thank you so much -- this is a great feature for DataFusion. I can't wait to see parallelized parquet writes :)

I ran some performance experiments locally and it shows the same performance you report in the PR description.

I think there are a few suggestions from @metesynnada that would make the code in this PR easier to read, but I think they could potentially be done as follow ons.

Performance Results (at least 4x faster)

This branch:

❯ copy 'traces' to 'foo.csv';
+---------+
| count   |
+---------+
| 5185717 |
+---------+
1 row in set. Query took 2.882 seconds.

Main

❯ copy 'traces' to 'foo.csv';
+---------+
| count   |
+---------+
| 5185717 |
+---------+
1 row in set. Query took 11.532 seconds.

datafusion/core/src/datasource/file_format/write.rs

alamb · 2023-09-08T17:35:23Z

datafusion/core/src/datasource/listing_table_factory.rs

@@ -213,7 +220,8 @@ impl TableProviderFactory for ListingTableFactory {
            .with_file_sort_order(cmd.order_exprs.clone())
            .with_insert_mode(insert_mode)
            .with_single_file(single_file)
-            .with_write_options(file_type_writer_options);
+            .with_write_options(file_type_writer_options)
+            .with_infinite_source(unbounded);


I took the liberty of doing so in e6f1b2f

alamb · 2023-09-08T17:36:31Z

datafusion/core/src/datasource/file_format/write.rs

+/// concurrently. Data order is preserved. In the event of an error,
+/// the ObjectStore writer is returned to the caller in addition to an error,
+/// so that the caller may handle aborting failed writes.
+async fn serialize_rb_stream_to_object_store(


I have some ideas how to simplify this code, which I will try out shortly, but I also think it can be merged like this too.

I tried to rewrite this as a futures::stream computation using buffered -- but I got stuck on some "higher-ranked lifetime error" so I think this is about as good as it is going to get

alamb · 2023-09-08T17:39:51Z

From my point of view this PR is ready to go -- There are still some suggestion from @metesynnada, but I am not sure if they feel any are required prior to merge.

alamb · 2023-09-08T18:48:16Z

datafusion/core/src/datasource/file_format/write.rs

+/// concurrently. Data order is preserved. In the event of an error,
+/// the ObjectStore writer is returned to the caller in addition to an error,
+/// so that the caller may handle aborting failed writes.
+async fn serialize_rb_stream_to_object_store(


I tried to rewrite this as a futures::stream computation using buffered -- but I got stuck on some "higher-ranked lifetime error" so I think this is about as good as it is going to get

datafusion/core/src/datasource/file_format/write.rs

Co-authored-by: Andrew Lamb <andrew@nerdnetworks.org>

alamb · 2023-09-09T10:47:11Z

@alamb Any tips on how to benchmark this more scientifically? Would adding "insert into" and "copy" queries to our existing benchmarking framework be a reasonable idea?

It would be great to add "write" / "copy" queries into our benchmarks. Since DataFusion only recently started supporting such statements we don't have same infrastructure for benchmarking that we do for queries

alamb · 2023-09-09T10:49:22Z

I think all comments have now been addressed. Thank you very much @devinjdangelo and @metesynnada -- this is really cool to see. 🚀

implement tokio task spawning

86aba96

github-actions bot added the core Core DataFusion crate label Aug 30, 2023

devinjdangelo added 3 commits August 31, 2023 09:43

improve error handling

d793ec5

parallelize single_file true

b0d5c1e

cargo fmt

1dd0e98

devinjdangelo commented Aug 31, 2023

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devinjdangelo marked this pull request as ready for review August 31, 2023 16:46

metesynnada reviewed Sep 4, 2023

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devinjdangelo and others added 3 commits September 5, 2023 11:31

Update datafusion/core/src/datasource/file_format/write.rs

bcb2baf

Co-authored-by: Metehan Yıldırım <100111937+metesynnada@users.noreply.github.com>

add channel

584d925

cargo fmt

6abbdfc

devinjdangelo mentioned this pull request Sep 5, 2023

Write Multiple Parquet Files in Parallel #7483

Merged

devinjdangelo added 3 commits September 6, 2023 10:47

yield_now if input is fifo unbounded

64d7692

fix unbounded option parse

cf1b1a5

cargo fmt

d8b84dd

alamb reviewed Sep 6, 2023

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metesynnada reviewed Sep 7, 2023

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alamb added 2 commits September 8, 2023 13:26

Remove redundant source

e6f1b2f

Merge remote-tracking branch 'apache/main' into parallelize_csv_json_…

ab1fe3e

…writes

alamb approved these changes Sep 8, 2023

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alamb reviewed Sep 8, 2023

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devinjdangelo and others added 3 commits September 8, 2023 19:50

Apply suggestions from code review

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Co-authored-by: Andrew Lamb <andrew@nerdnetworks.org>

address comments, add type aliases

0df35b1

cargo fmt

b8f2f0a

alamb merged commit abea893 into apache:main Sep 9, 2023
21 checks passed

devinjdangelo mentioned this pull request Sep 13, 2023

CSV/JSON Writes Not Guarenteed to Preserve Expected Ordering #7536

Closed

devinjdangelo mentioned this pull request Sep 15, 2023

Parallelize Parquet Serialization #7562

Merged

metesynnada mentioned this pull request Sep 20, 2023

[MINOR] Make the sink input aware of its plan #7610

Merged

andygrove added the enhancement New feature or request label Oct 7, 2023

Parallelize Stateless (CSV/JSON) File Write Serialization #7452

Parallelize Stateless (CSV/JSON) File Write Serialization #7452

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devinjdangelo commented Aug 30, 2023 • edited Loading

Which issue does this PR close?

Rationale for this change

What changes are included in this PR?

Benchmarking

Execution Time in Seconds

Peak Memory Usage in GB

What about Parquet?

Are these changes tested?

Are there any user-facing changes?

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metesynnada Sep 4, 2023 • edited Loading

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devinjdangelo commented Aug 31, 2023

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metesynnada Sep 6, 2023 • edited Loading

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alamb left a comment

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metesynnada left a comment

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metesynnada Sep 7, 2023 • edited Loading

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alamb left a comment

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Performance Results (at least 4x faster)

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alamb commented Sep 8, 2023

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alamb commented Sep 9, 2023

alamb commented Sep 9, 2023

devinjdangelo commented Aug 30, 2023 •

edited

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metesynnada Sep 4, 2023 •

edited

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metesynnada Sep 6, 2023 •

edited

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metesynnada Sep 7, 2023 •

edited

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