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new 17d4f3c [HUDI-2309] Blog post for new S3 deltastreamer source (#3526)
17d4f3c is described below
commit 17d4f3ca0b8aade75e524f27c9d36be802e854e9
Author: Sagar Sumit <[email protected]>
AuthorDate: Fri Aug 27 20:40:34 2021 +0530
[HUDI-2309] Blog post for new S3 deltastreamer source (#3526)
* [HUDI-2309] Blog post for new S3 deltastreamer source
* Address review comments and new design pic
* Code Review edits
* Add permalinks
Co-authored-by: Vinoth Chandar <[email protected]>
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+---
+title: "Reliable ingestion from AWS S3 using Hudi"
+excerpt: "From listing to log-based approach, a reliable way of ingesting data
from AWS S3 into Hudi."
+author: codope
+category: blog
+---
+
+In this post we will talk about a new deltastreamer source which reliably and
efficiently processes new data files as they arrive in AWS S3.
+As of today, to ingest data from S3 into Hudi, users leverage DFS source whose
[path
selector](https://github.com/apache/hudi/blob/178767948e906f673d6d4a357c65c11bc574f619/hudi-utilities/src/main/java/org/apache/hudi/utilities/sources/helpers/DFSPathSelector.java)
would identify the source files modified since the last checkpoint based on
max modification time.
+The problem with this approach is that modification time precision is upto
seconds in S3. It maybe possible that there were many files (beyond what the
configurable source limit allows) modifed in that second and some files might
be skipped.
+For more details, please refer to
[HUDI-1723](https://issues.apache.org/jira/browse/HUDI-1723).
+While the workaround is to ignore the source limit and keep reading, the
problem motivated us to redesign so that users can reliably ingest from S3.
+
+<!--truncate-->
+
+## Design
+
+For use-cases where seconds granularity does not suffice, we have a new source
in deltastreamer using log-based approach.
+The new [S3 events
source](https://github.com/apache/hudi/blob/178767948e906f673d6d4a357c65c11bc574f619/hudi-utilities/src/main/java/org/apache/hudi/utilities/sources/S3EventsSource.java)
relies on change notification and incremental processing to ingest from S3.
+The architecture is as shown in the figure below.
+
+
+
+In this approach, users need to [enable S3 event
notifications](https://docs.aws.amazon.com/AmazonS3/latest/userguide/NotificationHowTo.html).
+There will be two types of deltastreamers as detailed below.
+
+1.
[S3EventsSource](https://github.com/apache/hudi/blob/178767948e906f673d6d4a357c65c11bc574f619/hudi-utilities/src/main/java/org/apache/hudi/utilities/sources/S3EventsSource.java):
Create Hudi S3 metadata table.
+This source leverages AWS [SNS](https://aws.amazon.com/sns) and
[SQS](https://aws.amazon.com/sqs/) services that subscribe to file events from
the source bucket.
+ - Events from SQS will be written to this table, which serves as a
changelog for the subsequent incremental puller.
+ - When the events are committed to the S3 metadata table they will be
deleted from SQS.
+3.
[S3EventsHoodieIncrSource](https://github.com/apache/hudi/blob/178767948e906f673d6d4a357c65c11bc574f619/hudi-utilities/src/main/java/org/apache/hudi/utilities/sources/S3EventsHoodieIncrSource.java)
and uses the metadata table written by S3EventsSource.
+ - Read the S3 metadata table and get the objects that were added or
modified. These objects contain the S3 path for the source files that were
added or modified.
+ - Write to Hudi table with source data corresponding to the source files
in the S3 bucket.
+
+## Advantages
+
+- **Decoupling**: Every step in the pipeline is decoupled. The two sources can
be started independent of each other. We imagine most users run a single
deltastreamer to get all changes for a given bucket and can fan-out multiple
tables off that.
+- **Performance and Scale**: The previous approach used to list all files,
sort by modification time and then filter based on checkpoint. While it did
prune partition paths, the directory listing could still become a bottleneck.
By relying on change notification and native cloud APIs, the new approach
avoids directory listing and scales with the number of files being ingested.
+- **Reliability**: Since there is no longer any dependency on the max
modification time and the fact that S3 events are being recorded in the
metadata table, users can rest assured that all the events will be processed
eventually.
+- **Fault Tolerance**: There are two levels of fault toerance in this design.
Firstly, if some of the messages are not committed to the S3 metadata table,
then those messages will remain in the queue so that they can be reprocessed in
the next round. Secondly, if the incremental puller fails, then users can query
the S3 metadata table for the last commit point and resume the incremental
puller from that point onwards (kinda like how Kafka consumers can reset
offset).
+- **Asynchronous backfills**: With the log-based approach, it becomes much
easier to trigger backfills. See the "Conclusion and Future Work" section for
more details.
+
+## Configuration and Setup
+
+Users only need to specify the SQS queue url and region name to start the
S3EventsSource (metadata source).
+
+```
+hoodie.deltastreamer.s3.source.queue.url=https://sqs.us-west-2.amazonaws.com/queue/url
+hoodie.deltastreamer.s3.source.queue.region=us-west-2
+```
+
+There are a few other configurations for the metadata source which can be
tuned to suit specific requirements:
+
+- *`hoodie.deltastreamer.s3.source.queue.long.poll.wait`*: Value can range in
[0, 20] seconds. If set to 0 then metadata source will consume messages from
SQS using short polling. It is recommended to use long polling because it will
reduce false empty responses and reduce the cost of using SQS. By default, this
value is set to 20 seconds.
+- *`hoodie.deltastreamer.s3.source.queue.visibility.timeout`*: Value can range
in [0, 43200] seconds (i.e. max 12 hours). SQS does not automatically delete
the messages once consumed. It is the responsibility of metadata source to
delete the message after committing. SQS will move the consumed message to
in-flight state during which it becomes invisible for the configured timeout
period. By default, this value is set to 30 seconds.
+- *`hoodie.deltastreamer.s3.source.queue.max.messages.per.batch`*: Maximum
number of messages in a batch of one round of metadata source run. By default,
this value is set to 5.
+
+To setup the pipeline, first [enable S3 event
notifications](https://docs.aws.amazon.com/AmazonS3/latest/userguide/NotificationHowTo.html).
+Download the
[aws-java-sdk-sqs](https://mvnrepository.com/artifact/com.amazonaws/aws-java-sdk-sqs)
jar.
+Then start the S3EventsSource and S3EventsHoodieIncrSource using the
HoodieDeltaStreamer utility as shown in sample commands below.
+
+```bash
+# To start S3EventsSource
+spark-submit \
+--jars
"/home/hadoop/hudi-utilities-bundle_2.11-0.9.0.jar,/usr/lib/spark/external/lib/spark-avro.jar,/home/hadoop/aws-java-sdk-sqs-1.12.22.jar"
\
+--master yarn --deploy-mode client \
+--class org.apache.hudi.utilities.deltastreamer.HoodieDeltaStreamer
/home/hadoop/hudi-packages/hudi-utilities-bundle_2.11-0.9.0-SNAPSHOT.jar \
+--table-type COPY_ON_WRITE --source-ordering-field eventTime \
+--target-base-path s3://bucket_name/path/for/s3_meta_table \
+--target-table s3_meta_table --continuous \
+--min-sync-interval-seconds 10 \
+--hoodie-conf
hoodie.datasource.write.recordkey.field="s3.object.key,eventName" \
+--hoodie-conf
hoodie.datasource.write.keygenerator.class=org.apache.hudi.keygen.ComplexKeyGenerator
\
+--hoodie-conf hoodie.datasource.write.partitionpath.field=s3.bucket.name
--enable-hive-sync \
+--hoodie-conf
hoodie.datasource.hive_sync.partition_extractor_class=org.apache.hudi.hive.MultiPartKeysValueExtractor
\
+--hoodie-conf hoodie.datasource.write.hive_style_partitioning=true \
+--hoodie-conf hoodie.datasource.hive_sync.database=default \
+--hoodie-conf hoodie.datasource.hive_sync.table=s3_meta_table \
+--hoodie-conf hoodie.datasource.hive_sync.partition_fields=bucket \
+--source-class org.apache.hudi.utilities.sources.S3EventsSource \
+--hoodie-conf
hoodie.deltastreamer.source.queue.url=https://sqs.us-west-2.amazonaws.com/queue/url
+--hoodie-conf hoodie.deltastreamer.s3.source.queue.region=us-west-2
+
+# To start S3EventsHoodieIncrSource
+spark-submit \
+--jars
"/home/hadoop/hudi-utilities-bundle_2.11-0.9.0.jar,/usr/lib/spark/external/lib/spark-avro.jar,/home/hadoop/aws-java-sdk-sqs-1.12.22.jar"
\
+--master yarn --deploy-mode client \
+--class org.apache.hudi.utilities.deltastreamer.HoodieDeltaStreamer
/home/hadoop/hudi-packages/hudi-utilities-bundle_2.11-0.9.0-SNAPSHOT.jar \
+--table-type COPY_ON_WRITE \
+--source-ordering-field eventTime --target-base-path
s3://bucket_name/path/for/s3_hudi_table \
+--target-table s3_hudi_table --continuous --min-sync-interval-seconds 10 \
+--hoodie-conf hoodie.datasource.write.recordkey.field="pull_request_id" \
+--hoodie-conf
hoodie.datasource.write.keygenerator.class=org.apache.hudi.keygen.SimpleKeyGenerator
\
+--hoodie-conf hoodie.datasource.write.partitionpath.field=s3.bucket.name
--enable-hive-sync \
+--hoodie-conf
hoodie.datasource.hive_sync.partition_extractor_class=org.apache.hudi.hive.MultiPartKeysValueExtractor
\
+--hoodie-conf hoodie.datasource.write.hive_style_partitioning=true \
+--hoodie-conf hoodie.datasource.hive_sync.database=default \
+--hoodie-conf hoodie.datasource.hive_sync.table=s3_hudi_v6 \
+--hoodie-conf hoodie.datasource.hive_sync.partition_fields=bucket \
+--source-class org.apache.hudi.utilities.sources.S3EventsHoodieIncrSource \
+--hoodie-conf
hoodie.deltastreamer.source.hoodieincr.path=s3://bucket_name/path/for/s3_meta_table
\
+--hoodie-conf
hoodie.deltastreamer.source.hoodieincr.read_latest_on_missing_ckpt=true
+```
+
+## Conclusion and Future Work
+
+This post introduced a log-based approach to ingest data from S3 into Hudi
tables reliably and efficiently. We are actively improving this along the
following directions.
+
+- One stream of work is to add support for other cloud-based object storage
like Google Cloud Storage, Azure Blob Storage, etc. with this revamped design.
+- Another stream of work is to add resource manager that allows users to setup
notifications and delete resources when no longer needed.
+- Another interesting piece of work is to support **asynchronous backfills**.
Notification systems are evntually consistent and typically do not guarantee
perfect delivery of all files right away. The log-based approach provides
enough flexibility to trigger automatic backfills at a configurable interval
e.g. once a day or once a week.
+
+Please follow this [JIRA](https://issues.apache.org/jira/browse/HUDI-1896) to
learn more about active development on this issue.
+We look forward to contributions from the community. Hope you enjoyed this
post.
+
+Put your Hudi on and keep streaming!
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