7d0743b493
### Why is this change being proposed? This patch adds support for a new "product" aggregation function in `sql.functions` which multiplies-together all values in an aggregation group. This is likely to be useful in statistical applications which involve combining probabilities, or financial applications that involve combining cumulative interest rates, but is also a versatile mathematical operation of similar status to `sum` or `stddev`. Other users [have noted](https://stackoverflow.com/questions/52991640/cumulative-product-in-spark) the absence of such a function in current releases of Spark. This function is both much more concise than an expression of the form `exp(sum(log(...)))`, and avoids awkward edge-cases associated with some values being zero or negative, as well as being less computationally costly. ### Does this PR introduce _any_ user-facing change? No - only adds new function. ### How was this patch tested? Built-in tests have been added for the new `catalyst.expressions.aggregate.Product` class and its invocation via the (scala) `sql.functions.product` function. The latter, and the PySpark wrapper have also been manually tested in spark-shell and pyspark sessions. The SparkR wrapper is currently untested, and may need separate validation (I'm not an "R" user myself). An illustration of the new functionality, within PySpark is as follows: ``` import pyspark.sql.functions as pf, pyspark.sql.window as pw df = sqlContext.range(1, 17).toDF("x") win = pw.Window.partitionBy(pf.lit(1)).orderBy(pf.col("x")) df.withColumn("factorial", pf.product("x").over(win)).show(20, False) +---+---------------+ |x |factorial | +---+---------------+ |1 |1.0 | |2 |2.0 | |3 |6.0 | |4 |24.0 | |5 |120.0 | |6 |720.0 | |7 |5040.0 | |8 |40320.0 | |9 |362880.0 | |10 |3628800.0 | |11 |3.99168E7 | |12 |4.790016E8 | |13 |6.2270208E9 | |14 |8.71782912E10 | |15 |1.307674368E12 | |16 |2.0922789888E13| +---+---------------+ ``` Closes #30745 from rwpenney/feature/agg-product. Lead-authored-by: Richard Penney <rwp@rwpenney.uk> Co-authored-by: Richard Penney <rwpenney@users.noreply.github.com> Signed-off-by: HyukjinKwon <gurwls223@apache.org> |
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| .. | ||
| catalyst | ||
| core | ||
| hive | ||
| hive-thriftserver | ||
| create-docs.sh | ||
| gen-sql-api-docs.py | ||
| gen-sql-config-docs.py | ||
| gen-sql-functions-docs.py | ||
| mkdocs.yml | ||
| README.md | ||
Spark SQL
This module provides support for executing relational queries expressed in either SQL or the DataFrame/Dataset API.
Spark SQL is broken up into four subprojects:
- Catalyst (sql/catalyst) - An implementation-agnostic framework for manipulating trees of relational operators and expressions.
- Execution (sql/core) - A query planner / execution engine for translating Catalyst's logical query plans into Spark RDDs. This component also includes a new public interface, SQLContext, that allows users to execute SQL or LINQ statements against existing RDDs and Parquet files.
- Hive Support (sql/hive) - Includes extensions that allow users to write queries using a subset of HiveQL and access data from a Hive Metastore using Hive SerDes. There are also wrappers that allow users to run queries that include Hive UDFs, UDAFs, and UDTFs.
- HiveServer and CLI support (sql/hive-thriftserver) - Includes support for the SQL CLI (bin/spark-sql) and a HiveServer2 (for JDBC/ODBC) compatible server.
Running ./sql/create-docs.sh generates SQL documentation for built-in functions under sql/site, and SQL configuration documentation that gets included as part of configuration.md in the main docs directory.