771c538620
### What changes were proposed in this pull request? Fix UT according to https://github.com/apache/spark/pull/29966#issuecomment-752830046 Change StructType construct from ``` def inputSchema: StructType = StructType(StructField("inputColumn", LongType) :: Nil) ``` to ``` def inputSchema: StructType = new StructType().add("inputColumn", LongType) ``` The whole udf class is : ``` package org.apache.spark.examples.sql import org.apache.spark.sql.expressions.{MutableAggregationBuffer, UserDefinedAggregateFunction} import org.apache.spark.sql.types._ import org.apache.spark.sql.Row class Spark33084 extends UserDefinedAggregateFunction { // Data types of input arguments of this aggregate function def inputSchema: StructType = new StructType().add("inputColumn", LongType) // Data types of values in the aggregation buffer def bufferSchema: StructType = new StructType().add("sum", LongType).add("count", LongType) // The data type of the returned value def dataType: DataType = DoubleType // Whether this function always returns the same output on the identical input def deterministic: Boolean = true // Initializes the given aggregation buffer. The buffer itself is a `Row` that in addition to // standard methods like retrieving a value at an index (e.g., get(), getBoolean()), provides // the opportunity to update its values. Note that arrays and maps inside the buffer are still // immutable. def initialize(buffer: MutableAggregationBuffer): Unit = { buffer(0) = 0L buffer(1) = 0L } // Updates the given aggregation buffer `buffer` with new input data from `input` def update(buffer: MutableAggregationBuffer, input: Row): Unit = { if (!input.isNullAt(0)) { buffer(0) = buffer.getLong(0) + input.getLong(0) buffer(1) = buffer.getLong(1) + 1 } } // Merges two aggregation buffers and stores the updated buffer values back to `buffer1` def merge(buffer1: MutableAggregationBuffer, buffer2: Row): Unit = { buffer1(0) = buffer1.getLong(0) + buffer2.getLong(0) buffer1(1) = buffer1.getLong(1) + buffer2.getLong(1) } // Calculates the final result def evaluate(buffer: Row): Double = buffer.getLong(0).toDouble / buffer.getLong(1) } ``` ### Why are the changes needed? Fix UT for scala 2.13 ### Does this PR introduce _any_ user-facing change? No ### How was this patch tested? Existed UT Closes #30980 from AngersZhuuuu/spark-33084-followup. Authored-by: angerszhu <angers.zhu@gmail.com> Signed-off-by: Dongjoon Hyun <dhyun@apple.com> |
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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.