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### What changes were proposed in this pull request? This patch is a follow-up of SPARK-26848 (#23747). In SPARK-26848, we decided to open possibility to let end users set individual timestamp per partition. But in many cases, specifying timestamp represents the intention that we would want to go back to specific timestamp and reprocess records, which should be applied to all topics and partitions. This patch proposes to provide a way to set a global timestamp across topic-partitions which the source is subscribing to, so that end users can set all offsets by specific timestamp easily. To provide the way to config the timestamp easier, the new options only receive "a" timestamp for start/end timestamp. New options introduced in this PR: * startingTimestamp * endingTimestamp All two options receive timestamp as string. There're priorities for options regarding starting/ending offset as we will have three options for start offsets and another three options for end offsets. Priorities are following: * starting offsets: startingTimestamp -> startingOffsetsByTimestamp -> startingOffsets * ending offsets: startingTimestamp -> startingOffsetsByTimestamp -> startingOffsets ### Why are the changes needed? Existing option to specify timestamp as offset is quite verbose if there're a lot of partitions across topics. Suppose there're 100s of partitions in a topic, the json should contain 100s of times of the same timestamp. Also, the number of partitions can also change, which requires either: * fixing the code if the json is statically created * introducing the dependencies on Kafka client and deal with Kafka API on crafting json programmatically Both approaches are even not "acceptable" if we're dealing with ad-hoc query; anyone doesn't want to write the code more complicated than the query itself. Flink [provides the option](https://ci.apache.org/projects/flink/flink-docs-release-1.13/docs/connectors/datastream/kafka/#kafka-consumers-start-position-configuration) to specify a timestamp for all topic-partitions like this PR, and even doesn't provide the option to specify the timestamp per topic-partition. With this PR, end users are only required to provide a single timestamp value. No more complicated JSON format end users need to know about the structure. ### Does this PR introduce _any_ user-facing change? Yes, this PR introduces two new options, described in above section. Doc changes are following: ![스크린샷 2021-05-21 오후 12 01 02](https://user-images.githubusercontent.com/1317309/119076244-3034e680-ba2d-11eb-8323-0e227932d2e5.png) ![스크린샷 2021-05-21 오후 12 01 12](https://user-images.githubusercontent.com/1317309/119076255-35923100-ba2d-11eb-9d79-538a7f9ee738.png) ![스크린샷 2021-05-21 오후 12 01 24](https://user-images.githubusercontent.com/1317309/119076264-39be4e80-ba2d-11eb-8265-ac158f55c360.png) ![스크린샷 2021-05-21 오후 12 06 01](https://user-images.githubusercontent.com/1317309/119076271-3d51d580-ba2d-11eb-98ea-35fd72b1bbfc.png) ### How was this patch tested? New UTs covering new functionalities. Also manually tested via simple batch & streaming queries. Closes #32609 from HeartSaVioR/SPARK-29223-v2. Authored-by: Jungtaek Lim <kabhwan.opensource@gmail.com> Signed-off-by: Jungtaek Lim <kabhwan.opensource@gmail.com> |
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Apache Spark
Spark is a unified analytics engine for large-scale data processing. It provides high-level APIs in Scala, Java, Python, and R, and an optimized engine that supports general computation graphs for data analysis. It also supports a rich set of higher-level tools including Spark SQL for SQL and DataFrames, MLlib for machine learning, GraphX for graph processing, and Structured Streaming for stream processing.
Online Documentation
You can find the latest Spark documentation, including a programming guide, on the project web page. This README file only contains basic setup instructions.
Building Spark
Spark is built using Apache Maven. To build Spark and its example programs, run:
./build/mvn -DskipTests clean package
(You do not need to do this if you downloaded a pre-built package.)
More detailed documentation is available from the project site, at "Building Spark".
For general development tips, including info on developing Spark using an IDE, see "Useful Developer Tools".
Interactive Scala Shell
The easiest way to start using Spark is through the Scala shell:
./bin/spark-shell
Try the following command, which should return 1,000,000,000:
scala> spark.range(1000 * 1000 * 1000).count()
Interactive Python Shell
Alternatively, if you prefer Python, you can use the Python shell:
./bin/pyspark
And run the following command, which should also return 1,000,000,000:
>>> spark.range(1000 * 1000 * 1000).count()
Example Programs
Spark also comes with several sample programs in the examples
directory.
To run one of them, use ./bin/run-example <class> [params]
. For example:
./bin/run-example SparkPi
will run the Pi example locally.
You can set the MASTER environment variable when running examples to submit
examples to a cluster. This can be a mesos:// or spark:// URL,
"yarn" to run on YARN, and "local" to run
locally with one thread, or "local[N]" to run locally with N threads. You
can also use an abbreviated class name if the class is in the examples
package. For instance:
MASTER=spark://host:7077 ./bin/run-example SparkPi
Many of the example programs print usage help if no params are given.
Running Tests
Testing first requires building Spark. Once Spark is built, tests can be run using:
./dev/run-tests
Please see the guidance on how to run tests for a module, or individual tests.
There is also a Kubernetes integration test, see resource-managers/kubernetes/integration-tests/README.md
A Note About Hadoop Versions
Spark uses the Hadoop core library to talk to HDFS and other Hadoop-supported storage systems. Because the protocols have changed in different versions of Hadoop, you must build Spark against the same version that your cluster runs.
Please refer to the build documentation at "Specifying the Hadoop Version and Enabling YARN" for detailed guidance on building for a particular distribution of Hadoop, including building for particular Hive and Hive Thriftserver distributions.
Configuration
Please refer to the Configuration Guide in the online documentation for an overview on how to configure Spark.
Contributing
Please review the Contribution to Spark guide for information on how to get started contributing to the project.