d0f635e3bc
### What changes were proposed in this pull request? There are scenarios where Spark History Server is located behind the VPC. So whenever api calls hit to get the executor Summary(allexecutors). There can be delay in getting the response of executor summary and in mean time "stage-page-template.html" is loaded and the response of executor Summary is not added to the stage-page-template.html. As the result of which Aggregated Metrics by Executor in stage page is showing blank. This scenario can be easily found in the cases when there is some proxy-server which is responsible for sending the request and response to spark History server. This can be reproduced in Knox/In-house proxy servers which are used to send and receive response to Spark History Server. Alternative scenario to test this case, Open the spark UI in developer mode in browser add some breakpoint in stagepage.js, this will add some delay in getting the response and now if we check the spark UI for stage Aggregated Metrics by Executor in stage page is showing blank. So In-order to fix this there is a need to add the change in stagepage.js . There is a need to add the api call to get the html page(stage-page-template.html) first and after that other api calls to get the data that needs to attached in the stagepage (like executor Summary, stageExecutorSummaryInfoKeys exc) ### Why are the changes needed? Since stage page is useful for debugging purpose, This helps in understanding how many task ran on the particular executor and information related to shuffle read and write on that executor. ### Does this PR introduce any user-facing change? No ### How was this patch tested? Manually tested. Testing this in a reproducible way requires a running browser or HTML rendering engine that executes the JavaScript.Open the spark UI in developer mode in browser add some breakpoint in stagepage.js, this will add some delay in getting the response and now if we check the spark UI for stage Aggregated Metrics by Executor in stage page is showing blank. Before fix <img width="1529" alt="Screenshot 2020-01-20 at 3 21 55 PM" src="https://user-images.githubusercontent.com/34540906/72716739-bcfd3500-3b98-11ea-8dbe-90a135822f92.png"> After fix <img width="1540" alt="Screenshot 2020-01-20 at 3 23 12 PM" src="https://user-images.githubusercontent.com/34540906/72716782-d30af580-3b98-11ea-8764-2bde77764604.png"> Closes #27292 from SaurabhChawla100/SPARK-30582. Authored-by: Saurabh Chawla <saurabhc@qubole.com> Signed-off-by: Sean Owen <srowen@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.