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Apache Hive compatibility

Applies to: check marked yes Databricks Runtime

Apache Spark SQL in Databricks is designed to be compatible with the Apache Hive, including metastore connectivity, SerDes, and UDFs.

SerDes and UDFs​

Hive SerDes and UDFs are based on Hive 1.2.1.

Metastore connectivity​

See External Apache Hive metastore (legacy) for information on how to connect Databricks to an externally hosted Hive metastore.

Supported Hive features​

Spark SQL supports the vast majority of Hive features, such as:

  • Hive query statements, including:
    • SELECT
    • GROUP BY
    • ORDER BY
    • CLUSTER BY
    • SORT BY
  • All Hive expressions, including:
    • Relational expressions (=, ⇔, ==, <>, <, >, >=, <=, etc)
    • Arithmetic expressions (+, -, *, /, %, etc)
    • Logical expressions (AND, &&, OR, ||, etc)
    • Complex type constructors
    • Mathematical expressions (sign, ln, cos, etc)
    • String expressions (instr, length, printf, etc)
  • User defined functions (UDF)
  • User defined aggregation functions (UDAF)
  • User defined serialization formats (SerDes)
  • Window functions
  • Joins
    • JOIN
    • {LEFT|RIGHT|FULL} OUTER JOIN
    • LEFT SEMI JOIN
    • CROSS JOIN
  • Unions
  • Sub-queries
    • SELECT col FROM ( SELECT a + b AS col from t1) t2
  • Sampling
  • Explain
  • Partitioned tables including dynamic partition insertion
  • View
  • Vast majority of DDL statements, including:
    • CREATE TABLE
    • CREATE TABLE AS SELECT
    • ALTER TABLE
  • Most Hive data types, including:
    • TINYINT
    • SMALLINT
    • INT
    • BIGINT
    • BOOLEAN
    • FLOAT
    • DOUBLE
    • STRING
    • BINARY
    • TIMESTAMP
    • DATE
    • ARRAY<>
    • MAP<>
    • STRUCT<>

Unsupported Hive functionality​

The following sections contain a list of Hive features that Spark SQL doesn't support. Most of these features are rarely used in Hive deployments.

Major Hive features​

  • Writing to bucketed table created by Hive
  • ACID fine-grained updates

Esoteric Hive features​

  • Union type
  • Unique join
  • Column statistics collecting: Spark SQL does not piggyback scans to collect column statistics at the moment and only supports populating the sizeInBytes field of the Hive metastore

Hive input and output formats​

  • File format for CLI: For results showing back to the CLI, Spark SQL supports only TextOutputFormat
  • Hadoop archive

Hive optimizations​

A handful of Hive optimizations are not included in Spark. Some of these (such as indexes) are less important due to Spark SQL's in-memory computational model.

  • Block level bitmap indexes and virtual columns (used to build indexes).
  • Automatically determine the number of reducers for joins and groupbys: In Spark SQL, you need to control the degree of parallelism post-shuffle using SET spark.sql.shuffle.partitions=[num_tasks];.
  • Skew data flag: Spark SQL does not follow the skew data flag in Hive.
  • STREAMTABLE hint in join: Spark SQL does not follow the STREAMTABLE hint.
  • Merge multiple small files for query results: if the result output contains multiple small files, Hive can optionally merge the small files into fewer large files to avoid overflowing the HDFS metadata. Spark SQL does not support that.
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