Spark SQL Guide

JDBC To Other Databases

Spark SQL also includes a data source that can read data from other databases using JDBC. This functionality should be preferred over using JdbcRDD. This is because the results are returned as a DataFrame and they can easily be processed in Spark SQL or joined with other data sources. The JDBC data source is also easier to use from Java or Python as it does not require the user to provide a ClassTag. (Note that this is different than the Spark SQL JDBC server, which allows other applications to run queries using Spark SQL).

To get started you will need to include the JDBC driver for your particular database on the spark classpath. For example, to connect to postgres from the Spark Shell you would run the following command:

./bin/spark-shell --driver-class-path postgresql-9.4.1207.jar --jars postgresql-9.4.1207.jar

Data Source Option

Spark supports the following case-insensitive options for JDBC. The Data source options of JDBC can be set via:

For connection properties, users can specify the JDBC connection properties in the data source options. user and password are normally provided as connection properties for logging into the data sources.

Property NameDefaultMeaningScope
url (none) The JDBC URL of the form jdbc:subprotocol:subname to connect to. The source-specific connection properties may be specified in the URL. e.g., jdbc:postgresql://localhost/test?user=fred&password=secret read/write
dbtable (none) The JDBC table that should be read from or written into. Note that when using it in the read path anything that is valid in a FROM clause of a SQL query can be used. For example, instead of a full table you could also use a subquery in parentheses. It is not allowed to specify dbtable and query options at the same time. read/write
query (none) A query that will be used to read data into Spark. The specified query will be parenthesized and used as a subquery in the FROM clause. Spark will also assign an alias to the subquery clause. As an example, spark will issue a query of the following form to the JDBC Source.

SELECT <columns> FROM (<user_specified_query>) spark_gen_alias

Below are a couple of restrictions while using this option.
  1. It is not allowed to specify dbtable and query options at the same time.
  2. It is not allowed to specify query and partitionColumn options at the same time. When specifying partitionColumn option is required, the subquery can be specified using dbtable option instead and partition columns can be qualified using the subquery alias provided as part of dbtable.
    Example:
    spark.read.format("jdbc")
    .option("url", jdbcUrl)
    .option("query", "select c1, c2 from t1")
    .load()
read/write
prepareQuery (none) A prefix that will form the final query together with query. As the specified query will be parenthesized as a subquery in the FROM clause and some databases do not support all clauses in subqueries, the prepareQuery property offers a way to run such complex queries. As an example, spark will issue a query of the following form to the JDBC Source.

<prepareQuery> SELECT <columns> FROM (<user_specified_query>) spark_gen_alias

Below are a couple of examples.
  1. MSSQL Server does not accept WITH clauses in subqueries but it is possible to split such a query to prepareQuery and query:
    spark.read.format("jdbc")
    .option("url", jdbcUrl)
    .option("prepareQuery", "WITH t AS (SELECT x, y FROM tbl)")
    .option("query", "SELECT * FROM t WHERE x > 10")
    .load()
  2. MSSQL Server does not accept temp table clauses in subqueries but it is possible to split such a query to prepareQuery and query:
    spark.read.format("jdbc")
    .option("url", jdbcUrl)
    .option("prepareQuery", "(SELECT * INTO #TempTable FROM (SELECT * FROM tbl) t)")
    .option("query", "SELECT * FROM #TempTable")
    .load()
read/write
driver (none) The class name of the JDBC driver to use to connect to this URL. read/write
partitionColumn, lowerBound, upperBound (none) These options must all be specified if any of them is specified. In addition, numPartitions must be specified. They describe how to partition the table when reading in parallel from multiple workers. partitionColumn must be a numeric, date, or timestamp column from the table in question. Notice that lowerBound and upperBound are just used to decide the partition stride, not for filtering the rows in table. So all rows in the table will be partitioned and returned. This option applies only to reading.
Example:
spark.read.format("jdbc")
.option("url", jdbcUrl)
.option("dbtable", "(select c1, c2 from t1) as subq")
.option("partitionColumn", "c1")
.option("lowerBound", "1")
.option("upperBound", "100")
.option("numPartitions", "3")
.load() 		read
numPartitions (none) The maximum number of partitions that can be used for parallelism in table reading and writing. This also determines the maximum number of concurrent JDBC connections. If the number of partitions to write exceeds this limit, we decrease it to this limit by calling coalesce(numPartitions) before writing. read/write
queryTimeout 0 The number of seconds the driver will wait for a Statement object to execute to the given number of seconds. Zero means there is no limit. In the write path, this option depends on how JDBC drivers implement the API setQueryTimeout, e.g., the h2 JDBC driver checks the timeout of each query instead of an entire JDBC batch. read/write
fetchsize 0 The JDBC fetch size, which determines how many rows to fetch per round trip. This can help performance on JDBC drivers which default to low fetch size (e.g. Oracle with 10 rows). read
batchsize 1000 The JDBC batch size, which determines how many rows to insert per round trip. This can help performance on JDBC drivers. This option applies only to writing. write
isolationLevel READ_UNCOMMITTED The transaction isolation level, which applies to current connection. It can be one of NONE, READ_COMMITTED, READ_UNCOMMITTED, REPEATABLE_READ, or SERIALIZABLE, corresponding to standard transaction isolation levels defined by JDBC's Connection object, with default of READ_UNCOMMITTED. Please refer the documentation in java.sql.Connection. write
sessionInitStatement (none) After each database session is opened to the remote DB and before starting to read data, this option executes a custom SQL statement (or a PL/SQL block). Use this to implement session initialization code. Example: option("sessionInitStatement", """BEGIN execute immediate 'alter session set "_serial_direct_read"=true'; END;""") read
truncate false This is a JDBC writer related option. When SaveMode.Overwrite is enabled, this option causes Spark to truncate an existing table instead of dropping and recreating it. This can be more efficient, and prevents the table metadata (e.g., indices) from being removed. However, it will not work in some cases, such as when the new data has a different schema. In case of failures, users should turn off truncate option to use DROP TABLE again. Also, due to the different behavior of TRUNCATE TABLE among DBMS, it's not always safe to use this. MySQLDialect, DB2Dialect, MsSqlServerDialect, DerbyDialect, and OracleDialect supports this while PostgresDialect and default JDBCDirect doesn't. For unknown and unsupported JDBCDirect, the user option truncate is ignored. write
cascadeTruncate the default cascading truncate behaviour of the JDBC database in question, specified in the isCascadeTruncate in each JDBCDialect This is a JDBC writer related option. If enabled and supported by the JDBC database (PostgreSQL and Oracle at the moment), this options allows execution of a TRUNCATE TABLE t CASCADE (in the case of PostgreSQL a TRUNCATE TABLE ONLY t CASCADE is executed to prevent inadvertently truncating descendant tables). This will affect other tables, and thus should be used with care. write
createTableOptions This is a JDBC writer related option. If specified, this option allows setting of database-specific table and partition options when creating a table (e.g., CREATE TABLE t (name string) ENGINE=InnoDB.). write
createTableColumnTypes (none) The database column data types to use instead of the defaults, when creating the table. Data type information should be specified in the same format as CREATE TABLE columns syntax (e.g: "name CHAR(64), comments VARCHAR(1024)"). The specified types should be valid spark sql data types. write
customSchema (none) The custom schema to use for reading data from JDBC connectors. For example, "id DECIMAL(38, 0), name STRING". You can also specify partial fields, and the others use the default type mapping. For example, "id DECIMAL(38, 0)". The column names should be identical to the corresponding column names of JDBC table. Users can specify the corresponding data types of Spark SQL instead of using the defaults. read
pushDownPredicate true The option to enable or disable predicate push-down into the JDBC data source. The default value is true, in which case Spark will push down filters to the JDBC data source as much as possible. Otherwise, if set to false, no filter will be pushed down to the JDBC data source and thus all filters will be handled by Spark. Predicate push-down is usually turned off when the predicate filtering is performed faster by Spark than by the JDBC data source. read
pushDownAggregate true The option to enable or disable aggregate push-down in V2 JDBC data source. The default value is true, in which case Spark will push down aggregates to the JDBC data source. Otherwise, if sets to false, aggregates will not be pushed down to the JDBC data source. Aggregate push-down is usually turned off when the aggregate is performed faster by Spark than by the JDBC data source. Please note that aggregates can be pushed down if and only if all the aggregate functions and the related filters can be pushed down. If numPartitions equals to 1 or the group by key is the same as partitionColumn, Spark will push down aggregate to data source completely and not apply a final aggregate over the data source output. Otherwise, Spark will apply a final aggregate over the data source output. read
pushDownLimit true The option to enable or disable LIMIT push-down into V2 JDBC data source. The LIMIT push-down also includes LIMIT + SORT , a.k.a. the Top N operator. The default value is true, in which case Spark push down LIMIT or LIMIT with SORT to the JDBC data source. Otherwise, if sets to false, LIMIT or LIMIT with SORT is not pushed down to the JDBC data source. If numPartitions is greater than 1, Spark still applies LIMIT or LIMIT with SORT on the result from data source even if LIMIT or LIMIT with SORT is pushed down. Otherwise, if LIMIT or LIMIT with SORT is pushed down and numPartitions equals to 1, Spark will not apply LIMIT or LIMIT with SORT on the result from data source. read
pushDownOffset true The option to enable or disable OFFSET push-down into V2 JDBC data source. The default value is true, in which case Spark will push down OFFSET to the JDBC data source. Otherwise, if sets to false, Spark will not try to push down OFFSET to the JDBC data source. If pushDownOffset is true and numPartitions is equal to 1, OFFSET will be pushed down to the JDBC data source. Otherwise, OFFSET will not be pushed down and Spark still applies OFFSET on the result from data source. read
pushDownTableSample true The option to enable or disable TABLESAMPLE push-down into V2 JDBC data source. The default value is true, in which case Spark push down TABLESAMPLE to the JDBC data source. Otherwise, if value sets to false, TABLESAMPLE is not pushed down to the JDBC data source. read
keytab (none) Location of the kerberos keytab file (which must be pre-uploaded to all nodes either by --files option of spark-submit or manually) for the JDBC client. When path information found then Spark considers the keytab distributed manually, otherwise --files assumed. If both keytab and principal are defined then Spark tries to do kerberos authentication. read/write
principal (none) Specifies kerberos principal name for the JDBC client. If both keytab and principal are defined then Spark tries to do kerberos authentication. read/write
refreshKrb5Config false This option controls whether the kerberos configuration is to be refreshed or not for the JDBC client before establishing a new connection. Set to true if you want to refresh the configuration, otherwise set to false. The default value is false. Note that if you set this option to true and try to establish multiple connections, a race condition can occur. One possible situation would be like as follows.
  1. refreshKrb5Config flag is set with security context 1
  2. A JDBC connection provider is used for the corresponding DBMS
  3. The krb5.conf is modified but the JVM not yet realized that it must be reloaded
  4. Spark authenticates successfully for security context 1
  5. The JVM loads security context 2 from the modified krb5.conf
  6. Spark restores the previously saved security context 1
  7. The modified krb5.conf content just gone
 read/write
connectionProvider (none) The name of the JDBC connection provider to use to connect to this URL, e.g. db2, mssql. Must be one of the providers loaded with the JDBC data source. Used to disambiguate when more than one provider can handle the specified driver and options. The selected provider must not be disabled by spark.sql.sources.disabledJdbcConnProviderList. read/write
preferTimestampNTZ false When the option is set to true, TIMESTAMP WITHOUT TIME ZONE type is inferred as Spark's TimestampNTZ type. Otherwise, it is interpreted as Spark's Timestamp type(equivalent to TIMESTAMP WITH LOCAL TIME ZONE). This setting specifically affects only the inference of TIMESTAMP WITHOUT TIME ZONE data type. Both TIMESTAMP WITH LOCAL TIME ZONE and TIMESTAMP WITH TIME ZONE data types are consistently interpreted as Spark's Timestamp type regardless of this setting. read

Note that kerberos authentication with keytab is not always supported by the JDBC driver.
Before using keytab and principal configuration options, please make sure the following requirements are met:

There is a built-in connection providers for the following databases:

If the requirements are not met, please consider using the JdbcConnectionProvider developer API to handle custom authentication.

# Note: JDBC loading and saving can be achieved via either the load/save or jdbc methods
# Loading data from a JDBC source
jdbcDF = spark.read \
    .format("jdbc") \
    .option("url", "jdbc:postgresql:dbserver") \
    .option("dbtable", "schema.tablename") \
    .option("user", "username") \
    .option("password", "password") \
    .load()

jdbcDF2 = spark.read \
    .jdbc("jdbc:postgresql:dbserver", "schema.tablename",
          properties={"user": "username", "password": "password"})

# Specifying dataframe column data types on read
jdbcDF3 = spark.read \
    .format("jdbc") \
    .option("url", "jdbc:postgresql:dbserver") \
    .option("dbtable", "schema.tablename") \
    .option("user", "username") \
    .option("password", "password") \
    .option("customSchema", "id DECIMAL(38, 0), name STRING") \
    .load()

# Saving data to a JDBC source
jdbcDF.write \
    .format("jdbc") \
    .option("url", "jdbc:postgresql:dbserver") \
    .option("dbtable", "schema.tablename") \
    .option("user", "username") \
    .option("password", "password") \
    .save()

jdbcDF2.write \
    .jdbc("jdbc:postgresql:dbserver", "schema.tablename",
          properties={"user": "username", "password": "password"})

# Specifying create table column data types on write
jdbcDF.write \
    .option("createTableColumnTypes", "name CHAR(64), comments VARCHAR(1024)") \
    .jdbc("jdbc:postgresql:dbserver", "schema.tablename",
          properties={"user": "username", "password": "password"})
Find full example code at "examples/src/main/python/sql/datasource.py" in the Spark repo.
// Note: JDBC loading and saving can be achieved via either the load/save or jdbc methods
// Loading data from a JDBC source
val jdbcDF = spark.read
  .format("jdbc")
  .option("url", "jdbc:postgresql:dbserver")
  .option("dbtable", "schema.tablename")
  .option("user", "username")
  .option("password", "password")
  .load()

val connectionProperties = new Properties()
connectionProperties.put("user", "username")
connectionProperties.put("password", "password")
val jdbcDF2 = spark.read
  .jdbc("jdbc:postgresql:dbserver", "schema.tablename", connectionProperties)
// Specifying the custom data types of the read schema
connectionProperties.put("customSchema", "id DECIMAL(38, 0), name STRING")
val jdbcDF3 = spark.read
  .jdbc("jdbc:postgresql:dbserver", "schema.tablename", connectionProperties)

// Saving data to a JDBC source
jdbcDF.write
  .format("jdbc")
  .option("url", "jdbc:postgresql:dbserver")
  .option("dbtable", "schema.tablename")
  .option("user", "username")
  .option("password", "password")
  .save()

jdbcDF2.write
  .jdbc("jdbc:postgresql:dbserver", "schema.tablename", connectionProperties)

// Specifying create table column data types on write
jdbcDF.write
  .option("createTableColumnTypes", "name CHAR(64), comments VARCHAR(1024)")
  .jdbc("jdbc:postgresql:dbserver", "schema.tablename", connectionProperties)
Find full example code at "examples/src/main/scala/org/apache/spark/examples/sql/SQLDataSourceExample.scala" in the Spark repo.
// Note: JDBC loading and saving can be achieved via either the load/save or jdbc methods
// Loading data from a JDBC source
Dataset<Row> jdbcDF = spark.read()
  .format("jdbc")
  .option("url", "jdbc:postgresql:dbserver")
  .option("dbtable", "schema.tablename")
  .option("user", "username")
  .option("password", "password")
  .load();

Properties connectionProperties = new Properties();
connectionProperties.put("user", "username");
connectionProperties.put("password", "password");
Dataset<Row> jdbcDF2 = spark.read()
  .jdbc("jdbc:postgresql:dbserver", "schema.tablename", connectionProperties);

// Saving data to a JDBC source
jdbcDF.write()
  .format("jdbc")
  .option("url", "jdbc:postgresql:dbserver")
  .option("dbtable", "schema.tablename")
  .option("user", "username")
  .option("password", "password")
  .save();

jdbcDF2.write()
  .jdbc("jdbc:postgresql:dbserver", "schema.tablename", connectionProperties);

// Specifying create table column data types on write
jdbcDF.write()
  .option("createTableColumnTypes", "name CHAR(64), comments VARCHAR(1024)")
  .jdbc("jdbc:postgresql:dbserver", "schema.tablename", connectionProperties);
Find full example code at "examples/src/main/java/org/apache/spark/examples/sql/JavaSQLDataSourceExample.java" in the Spark repo.
# Loading data from a JDBC source
df <- read.jdbc("jdbc:postgresql:dbserver", "schema.tablename", user = "username", password = "password")

# Saving data to a JDBC source
write.jdbc(df, "jdbc:postgresql:dbserver", "schema.tablename", user = "username", password = "password")
Find full example code at "examples/src/main/r/RSparkSQLExample.R" in the Spark repo.
CREATE TEMPORARY VIEW jdbcTable
USING org.apache.spark.sql.jdbc
OPTIONS (
  url "jdbc:postgresql:dbserver",
  dbtable "schema.tablename",
  user 'username',
  password 'password'
)

INSERT INTO TABLE jdbcTable
SELECT * FROM resultTable

Data Type Mapping

Mapping Spark SQL Data Types from MySQL

The below table describes the data type conversions from MySQL data types to Spark SQL Data Types, when reading data from a MySQL table using the built-in jdbc data source with the MySQL Connector/J as the activated JDBC Driver. Note that, different JDBC drivers, such as Maria Connector/J, which are also available to connect MySQL, may have different mapping rules.

MySQL Data Type Spark SQL Data Type Remarks
BIT(1) BooleanType
BIT( >1 ) BinaryType (Default)
BIT( >1 ) LongType spark.sql.legacy.mysql.bitArrayMapping.enabled=true
TINYINT(1) BooleanType
TINYINT(1) ByteType tinyInt1isBit=false
BOOLEAN BooleanType
BOOLEAN ByteType tinyInt1isBit=false
TINYINT( >1 ) ByteType
TINYINT( any ) UNSIGNED ShortType
SMALLINT ShortType
SMALLINT UNSIGNED IntegerType
MEDIUMINT [UNSIGNED] IntegerType
INT IntegerType
INT UNSIGNED LongType
BIGINT LongType
BIGINT UNSIGNED DecimalType(20,0)
FLOAT FloatType
FLOAT UNSIGNED DoubleType
DOUBLE [UNSIGNED] DoubleType
DECIMAL(p,s) [UNSIGNED] DecimalType(min(38, p),(min(18,s))) The column type is bounded to DecimalType(38, 18), if 'p>38', the fraction part will be truncated if exceeded. And if any value of this column have an actual precision greater 38 will fail with NUMERIC_VALUE_OUT_OF_RANGE.WITHOUT_SUGGESTION error
DATE DateType
DATETIME TimestampType (Default)preferTimestampNTZ=false or spark.sql.timestampType=TIMESTAMP_LTZ
DATETIME TimestampNTZType preferTimestampNTZ=true or spark.sql.timestampType=TIMESTAMP_NTZ
TIMESTAMP TimestampType
TIME TimestampType (Default)preferTimestampNTZ=false or spark.sql.timestampType=TIMESTAMP_LTZ
TIME TimestampNTZType preferTimestampNTZ=true or spark.sql.timestampType=TIMESTAMP_NTZ
YEAR DateType yearIsDateType=true
YEAR IntegerType yearIsDateType=false
CHAR(n) CharType(n)
VARCHAR(n) VarcharType(n)
BINARY(n) BinaryType
VARBINARY(n) BinaryType
CHAR(n) BINARY BinaryType
VARCHAR(n) BINARY BinaryType
BLOB BinaryType
TINYBLOB BinaryType
MEDIUMBLOB BinaryType
LONGBLOB BinaryType
TEXT StringType
TINYTEXT StringType
MEDIUMTEXT StringType
LONGTEXT StringType
JSON StringType
GEOMETRY BinaryType
ENUM CharType(n)
SET CharType(n)

Mapping Spark SQL Data Types to MySQL

The below table describes the data type conversions from Spark SQL Data Types to MySQL data types, when creating, altering, or writing data to a MySQL table using the built-in jdbc data source with the MySQL Connector/J as the activated JDBC Driver.

Note that, different JDBC drivers, such as Maria Connector/J, which are also available to connect MySQL, may have different mapping rules.

Spark SQL Data Type MySQL Data Type Remarks
BooleanType BIT(1)
ByteType TINYINT
ShortType SMALLINT For Spark 3.5 and previous, it maps to INTEGER
IntegerType INTEGER
LongType BIGINT
FloatType FLOAT
DoubleType DOUBLE PRECISION
DecimalType(p, s) DECIMAL(p,s)
DateType DATE
TimestampType TIMESTAMP
TimestampNTZType DATETIME
StringType LONGTEXT
BinaryType BLOB
CharType(n) CHAR(n)
VarcharType(n) VARCHAR(n)

The Spark Catalyst data types below are not supported with suitable MYSQL types.

Mapping Spark SQL Data Types from PostgreSQL

The below table describes the data type conversions from PostgreSQL data types to Spark SQL Data Types, when reading data from a Postgres table using the built-in jdbc data source with the PostgreSQL JDBC Driver as the activated JDBC Driver. Note that, different JDBC drivers, or different versions might result slightly different.

PostgreSQL Data Type Spark SQL Data Type Remarks
boolean BooleanType
smallint, smallserial ShortType
integer, serial IntegerType
bigint, bigserial LongType
float, float(p), real FloatType 1 ≤ p ≤ 24
float(p) DoubleType 25 ≤ p ≤ 53
double precision DoubleType
numeric, decimal DecimalType
  • Since PostgreSQL 15, 's' can be negative. If 's<0' it'll be adjusted to DecimalType(min(p-s, 38), 0); Otherwise, DecimalType(p, s)
  • If 'p>38', the fraction part will be truncated if exceeded. And if any value of this column have an actual precision greater 38 will fail with NUMERIC_VALUE_OUT_OF_RANGE.WITHOUT_SUGGESTION error.
  • Special numeric values, 'NaN', 'infinity' and '-infinity' is not supported
character varying(n), varchar(n) VarcharType(n)
character(n), char(n), bpchar(n) CharType(n)
bpchar StringType
text StringType
bytea BinaryType
date DateType
timestamp [ (p) ] [ without time zone ] TimestampType (Default)preferTimestampNTZ=false or spark.sql.timestampType=TIMESTAMP_LTZ
timestamp [ (p) ] [ without time zone ] TimestampNTZType preferTimestampNTZ=true or spark.sql.timestampType=TIMESTAMP_NTZ
timestamp [ (p) ] with time zone TimestampType
time [ (p) ] [ without time zone ] TimestampType (Default)preferTimestampNTZ=false or spark.sql.timestampType=TIMESTAMP_LTZ
time [ (p) ] [ without time zone ] TimestampNTZType preferTimestampNTZ=true or spark.sql.timestampType=TIMESTAMP_NTZ
time [ (p) ] with time zone TimestampType
interval [ fields ] [ (p) ] StringType
ENUM StringType
money StringType Monetary Types
inet, cidr, macaddr, macaddr8 StringType Network Address Types
point, line, lseg, box, path, polygon, circle StringType Geometric Types
pg_lsn StringType Log Sequence Number
bit, bit(1) BooleanType
bit( >1 ) BinaryType
bit varying( any ) BinaryType
tsvector, tsquery StringType Text Search Types
uuid StringType Universally Unique Identifier Type
xml StringType XML Type
json, jsonb StringType JSON Types
array ArrayType
Composite Types StringType Types created by CREATE TYPE syntax.
int4range, int8range, numrange, tsrange, tstzrange, daterange, etc StringType Range Types
Domain Types (Decided by the underlying type)
oid DecimalType(20, 0) Object Identifier Types
regxxx StringType Object Identifier Types
void NullType void is a Postgres pseudo type, other pseudo types have not yet been verified

Mapping Spark SQL Data Types to PostgreSQL

The below table describes the data type conversions from Spark SQL Data Types to PostgreSQL data types, when creating, altering, or writing data to a PostgreSQL table using the built-in jdbc data source with the PostgreSQL JDBC Driver as the activated JDBC Driver.

Spark SQL Data Type PostgreSQL Data Type Remarks
BooleanType boolean
ByteType smallint
ShortType smallint
IntegerType integer
LongType bigint
FloatType float4
DoubleType float8
DecimalType(p, s) numeric(p,s)
DateType date
TimestampType timestamp with time zone Before Spark 4.0, it was mapped as timestamp. Please refer to the migration guide for more information
TimestampNTZType timestamp
StringType text
BinaryType bytea
CharType(n) CHAR(n)
VarcharType(n) VARCHAR(n)
ArrayType
Element type PG Array
BooleanType boolean[]
ByteType smallint[]
ShortType smallint[]
IntegerType integer[]
LongType bigint[]
FloatType float4[]
DoubleType float8[]
DecimalType(p, s) numeric(p,s)[]
DateType date[]
TimestampType timestamp[]
TimestampNTZType timestamp[]
StringType text[]
BinaryType bytea[]
CharType(n) char(n)[]
VarcharType(n) varchar(n)[]
If the element type is an ArrayType, it converts to Postgres multidimensional array.
For instance,
ArrayType(ArrayType(StringType)) converts to text[][],
ArrayType(ArrayType(ArrayType(LongType))) converts to bigint[][][]

The Spark Catalyst data types below are not supported with suitable PostgreSQL types.

Mapping Spark SQL Data Types from Oracle

The below table describes the data type conversions from Oracle data types to Spark SQL Data Types, when reading data from an Oracle table using the built-in jdbc data source with the Oracle JDBC as the activated JDBC Driver.

Oracle Data Type Spark SQL Data Type Remarks
BOOLEAN BooleanType Introduced since Oracle Release 23c
NUMBER[(p[,s])] DecimalType(p,s) 's' can be negative in Oracle. If 's<0' it'll be adjusted to DecimalType(min(p-s, 38), 0); Otherwise, DecimalType(p, s), and if 'p>38', the fraction part will be truncated if exceeded. And if any value of this column have an actual precision greater 38 will fail with NUMERIC_VALUE_OUT_OF_RANGE.WITHOUT_SUGGESTION error
FLOAT[(p)] DecimalType(38, 10)
BINARY_FLOAT FloatType
BINARY_DOUBLE DoubleType
LONG BinaryType
RAW(size) BinaryType
LONG RAW BinaryType
DATE TimestampType When oracle.jdbc.mapDateToTimestamp=true, it follows TIMESTAMP's behavior below
DATE DateType When oracle.jdbc.mapDateToTimestamp=false, it maps to DateType
TIMESTAMP TimestampType (Default)preferTimestampNTZ=false or spark.sql.timestampType=TIMESTAMP_LTZ
TIMESTAMP TimestampNTZType preferTimestampNTZ=true or spark.sql.timestampType=TIMESTAMP_NTZ
TIMESTAMP WITH TIME ZONE TimestampType
TIMESTAMP WITH LOCAL TIME ZONE TimestampType
INTERVAL YEAR TO MONTH YearMonthIntervalType
INTERVAL DAY TO SECOND DayTimeIntervalType
CHAR[(size [BYTE | CHAR])] CharType(size)
NCHAR[(size)] StringType
VARCHAR2(size [BYTE | CHAR]) VarcharType(size)
NVARCHAR2 StringType
ROWID/UROWID StringType
CLOB StringType
NCLOB StringType
BLOB BinaryType
BFILE UNRECOGNIZED_SQL_TYPE error raised

Mapping Spark SQL Data Types to Oracle

The below table describes the data type conversions from Spark SQL Data Types to Oracle data types, when creating, altering, or writing data to an Oracle table using the built-in jdbc data source with the Oracle JDBC as the activated JDBC Driver.

Spark SQL Data Type Oracle Data Type Remarks
BooleanType NUMBER(1, 0) BooleanType maps to NUMBER(1, 0) as BOOLEAN is introduced since Oracle Release 23c
ByteType NUMBER(3)
ShortType NUMBER(5)
IntegerType NUMBER(10)
LongType NUMBER(19)
FloatType NUMBER(19, 4)
DoubleType NUMBER(19, 4)
DecimalType(p, s) NUMBER(p,s)
DateType DATE
TimestampType TIMESTAMP WITH LOCAL TIME ZONE
TimestampNTZType TIMESTAMP
StringType VARCHAR2(255) For historical reason, a string value has maximum 255 characters
BinaryType BLOB
CharType(n) CHAR(n)
VarcharType(n) VARCHAR2(n)

The Spark Catalyst data types below are not supported with suitable Oracle types.

Mapping Spark SQL Data Types from Microsoft SQL Server

The below table describes the data type conversions from Microsoft SQL Server data types to Spark SQL Data Types, when reading data from a Microsoft SQL Server table using the built-in jdbc data source with the mssql-jdbc as the activated JDBC Driver.

SQL Server Data Type Spark SQL Data Type Remarks
bit BooleanType
tinyint ShortType
smallint ShortType
int IntegerType
bigint LongType
float(p), real FloatType 1 ≤ p ≤ 24
float[(p)] DoubleType 25 ≤ p ≤ 53
double precision DoubleType
smallmoney DecimalType(10, 4)
money DecimalType(19, 4)
decimal[(p[, s])], numeric[(p[, s])] DecimalType(p, s)
date DateType
datetime TimestampType (Default)preferTimestampNTZ=false or spark.sql.timestampType=TIMESTAMP_LTZ
datetime TimestampNTZType preferTimestampNTZ=true or spark.sql.timestampType=TIMESTAMP_NTZ
datetime2 [ (fractional seconds precision) ] TimestampType (Default)preferTimestampNTZ=false or spark.sql.timestampType=TIMESTAMP_LTZ
datetime2 [ (fractional seconds precision) ] TimestampNTZType preferTimestampNTZ=true or spark.sql.timestampType=TIMESTAMP_NTZ
datetimeoffset [ (fractional seconds precision) ] TimestampType
smalldatetime TimestampType (Default)preferTimestampNTZ=false or spark.sql.timestampType=TIMESTAMP_LTZ
smalldatetime TimestampNTZType preferTimestampNTZ=true or spark.sql.timestampType=TIMESTAMP_NTZ
time [ (fractional second scale) ] TimestampType (Default)preferTimestampNTZ=false or spark.sql.timestampType=TIMESTAMP_LTZ
time [ (fractional second scale) ] TimestampNTZType preferTimestampNTZ=true or spark.sql.timestampType=TIMESTAMP_NTZ
binary [ ( n ) ] BinaryType
varbinary [ ( n | max ) ] BinaryType
char [ ( n ) ] CharType(n)
varchar [ ( n | max ) ] VarcharType(n)
nchar [ ( n ) ] StringType
nvarchar [ ( n | max ) ] StringType
text StringType
ntext StringType
image StringType
geography BinaryType
geometry BinaryType
rowversion BinaryType
sql_variant UNRECOGNIZED_SQL_TYPE error raised

Mapping Spark SQL Data Types to Microsoft SQL Server

The below table describes the data type conversions from Spark SQL Data Types to Microsoft SQL Server data types, when creating, altering, or writing data to a Microsoft SQL Server table using the built-in jdbc data source with the mssql-jdbc as the activated JDBC Driver.

Spark SQL Data Type SQL Server Data Type Remarks
BooleanType bit
ByteType smallint Supported since Spark 4.0.0, previous versions throw errors
ShortType smallint
IntegerType int
LongType bigint
FloatType real
DoubleType double precision
DecimalType(p, s) number(p,s)
DateType date
TimestampType datetime
TimestampNTZType datetime
StringType nvarchar(max)
BinaryType varbinary(max)
CharType(n) char(n)
VarcharType(n) varchar(n)

The Spark Catalyst data types below are not supported with suitable SQL Server types.

Mapping Spark SQL Data Types from DB2

The below table describes the data type conversions from DB2 data types to Spark SQL Data Types, when reading data from a DB2 table using the built-in jdbc data source with the IBM Data Server Driver For JDBC and SQLJ as the activated JDBC Driver.

DB2 Data Type Spark SQL Data Type Remarks
BOOLEAN BinaryType
SMALLINT ShortType
INTEGER IntegerType
BIGINT LongType
REAL FloatType
DOUBLE, FLOAT DoubleType FLOAT is double precision floating-point in db2
DECIMAL, NUMERIC, DECFLOAT DecimalType
DATE DateType
TIMESTAMP, TIMESTAMP WITHOUT TIME ZONE TimestampType (Default)preferTimestampNTZ=false or spark.sql.timestampType=TIMESTAMP_LTZ
TIMESTAMP, TIMESTAMP WITHOUT TIME ZONE TimestampNTZType preferTimestampNTZ=true or spark.sql.timestampType=TIMESTAMP_NTZ
TIMESTAMP WITH TIME ZONE TimestampType
TIME TimestampType (Default)preferTimestampNTZ=false or spark.sql.timestampType=TIMESTAMP_LTZ
TIME TimestampNTZType preferTimestampNTZ=true or spark.sql.timestampType=TIMESTAMP_NTZ
CHAR(n) CharType(n)
VARCHAR(n) VarcharType(n)
CHAR(n) FOR BIT DATA BinaryType
VARCHAR(n) FOR BIT DATA BinaryType
BINARY(n) BinaryType
VARBINARY(n) BinaryType
CLOB(n) StringType
DBCLOB(n) StringType
BLOB(n) BinaryType
GRAPHIC(n) StringType
VARGRAPHIC(n) StringType
XML StringType
ROWID StringType

Mapping Spark SQL Data Types to DB2

The below table describes the data type conversions from Spark SQL Data Types to DB2 data types, when creating, altering, or writing data to a DB2 table using the built-in jdbc data source with the IBM Data Server Driver For JDBC and SQLJ as the activated JDBC Driver.

Spark SQL Data Type DB2 Data Type Remarks
BooleanType BOOLEAN
ByteType SMALLINT
ShortType SMALLINT
IntegerType INTEGER
LongType BIGINT
FloatType REAL
DoubleType DOUBLE PRECISION
DecimalType(p, s) DECIMAL(p,s) The maximum value for 'p' is 31 in DB2, while it is 38 in Spark. It might fail when storing DecimalType(p>=32, s) to DB2
DateType DATE
TimestampType TIMESTAMP
TimestampNTZType TIMESTAMP
StringType CLOB
BinaryType BLOB
CharType(n) CHAR(n) The maximum value for 'n' is 255 in DB2, while it is unlimited in Spark.
VarcharType(n) VARCHAR(n) The maximum value for 'n' is 255 in DB2, while it is unlimited in Spark.

The Spark Catalyst data types below are not supported with suitable DB2 types.

Mapping Spark SQL Data Types from Teradata

The below table describes the data type conversions from Teradata data types to Spark SQL Data Types, when reading data from a Teradata table using the built-in jdbc data source with the Teradata JDBC Driver as the activated JDBC Driver.

Teradata Data Type Spark SQL Data Type Remarks
BYTEINT ByteType
SMALLINT ShortType
INTEGER, INT IntegerType
BIGINT LongType
REAL, DOUBLE PRECISION, FLOAT DoubleType
DECIMAL, NUMERIC, NUMBER DecimalType
DATE DateType
TIMESTAMP, TIMESTAMP WITH TIME ZONE TimestampType (Default)preferTimestampNTZ=false or spark.sql.timestampType=TIMESTAMP_LTZ
TIMESTAMP, TIMESTAMP WITH TIME ZONE TimestampNTZType preferTimestampNTZ=true or spark.sql.timestampType=TIMESTAMP_NTZ
TIME, TIME WITH TIME ZONE TimestampType (Default)preferTimestampNTZ=false or spark.sql.timestampType=TIMESTAMP_LTZ
TIME, TIME WITH TIME ZONE TimestampNTZType preferTimestampNTZ=true or spark.sql.timestampType=TIMESTAMP_NTZ
CHARACTER(n), CHAR(n), GRAPHIC(n) CharType(n)
VARCHAR(n), VARGRAPHIC(n) VarcharType(n)
BYTE(n), VARBYTE(n) BinaryType
CLOB StringType
BLOB BinaryType
INTERVAL Data Types - The INTERVAL data types are unknown yet
Period Data Types, ARRAY, UDT - Not Supported

Mapping Spark SQL Data Types to Teradata

The below table describes the data type conversions from Spark SQL Data Types to Teradata data types, when creating, altering, or writing data to a Teradata table using the built-in jdbc data source with the Teradata JDBC Driver as the activated JDBC Driver.

Spark SQL Data Type Teradata Data Type Remarks
BooleanType CHAR(1)
ByteType BYTEINT
ShortType SMALLINT
IntegerType INTEGER
LongType BIGINT
FloatType REAL
DoubleType DOUBLE PRECISION
DecimalType(p, s) DECIMAL(p,s)
DateType DATE
TimestampType TIMESTAMP
TimestampNTZType TIMESTAMP
StringType VARCHAR(255)
BinaryType BLOB
CharType(n) CHAR(n)
VarcharType(n) VARCHAR(n)

The Spark Catalyst data types below are not supported with suitable Teradata types.