| commit | 4abab8b83085d39674ce790fae02ab806cbda60c | [log] [tgz] |
|---|---|---|
| author | srielau <serge@rielau.com> | Fri Oct 20 21:02:51 2023 +0800 |
| committer | Wenchen Fan <wenchen@databricks.com> | Fri Oct 20 21:02:51 2023 +0800 |
| tree | 2aa68fe1027cb2e548a3e516154ab49808af6be8 | |
| parent | fa8f4b1edd6715cbfcc075178ba0a673c3d919f0 [diff] |
[SPARK-45595] Expose SQLSTATE in error message
### What changes were proposed in this pull request?
In this PR we include the SQLSTATE as part of the error message when
spark.sql.error.messageFormat = PRETTY (default)
```
[DIVIDE_BY_ZERO] Division by zero. Use `try_divide` to tolerate divisor being 0 and return NULL instead. If necessary set "spark.sql.ansi.enabled" to "false" to bypass this error. SQLSTATE: 22012
== SQL(line 1, position 8) ==
SELECT 1/0
^^^
```
### Alternatives
Aside from minor changes (like colon vs equal etc) there more options on where to place the information.
- Between error class and message
```
The state is added right before the message text, using a set of brackets (e.g. round):
[DIVIDE_BY_ZERO](22013) Division by zero. Use `try_divide` to tolerate divisor being 0 and return NULL instead. If necessary set "spark.sql.ansi.enabled" to "false" to bypass this error.
== SQL(line 1, position 8) ==
SELECT 1/0
^^^
```
- At the very end after the context
```
[DIVIDE_BY_ZERO] Division by zero. Use `try_divide` to tolerate divisor being 0 and return NULL instead. If necessary set "spark.sql.ansi.enabled" to "false" to bypass this error.
== SQL(line 1, position 8) ==
SELECT 1/0
^^^
SQLSTATE: 22013
```
Both of these alternatives have issues:
- The SQLSTATE itself is not interesting to a human. It is 5 alphanumerics that are hard to remember and have less information than the human-readable error class. As such it contributes nothing to anyone who wants to read the message.
- The "context" which is currently optionally tailing the message can be length and complex. We may also decide to refine it in the futire. So adding SQLSTATE after it makes it hard to find. What, for example, if we want to nest error messages and the "stack-trace" should show multiple error messages. In that case SQLSTATE would be like a closing bracket, and hard to match.
### Why are the changes needed?
To provide useful information for users to e.g. catch exceptions based on SQLSTATE or look up information.
### Does this PR introduce _any_ user-facing change?
Yes, error messages change
### How was this patch tested?
Existing QA
### Was this patch authored or co-authored using generative AI tooling?
No
Closes #43438 from srielau/SPARK-45595-Expose-SQLSTATE-in-error-message.
Lead-authored-by: srielau <serge@rielau.com>
Co-authored-by: Serge Rielau <srielau@users.noreply.github.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
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, pandas API on Spark for pandas workloads, MLlib for machine learning, GraphX for graph processing, and Structured Streaming for stream processing.
You can find the latest Spark documentation, including a programming guide, on the project web page. This README file only contains basic setup instructions.
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”.
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()
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()
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 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.
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
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.
Please refer to the Configuration Guide in the online documentation for an overview on how to configure Spark.
Please review the Contribution to Spark guide for information on how to get started contributing to the project.
