Migration Guide: SQL, Datasets and DataFrame

• Upgrading from Spark SQL 2.4 to 3.0
  • Dataset/DataFrame APIs
  • DDL Statements
  • UDFs and Built-in Functions
  • Query Engine
  • Data Sources
  • Others
• Upgrading from Spark SQL 2.4.5 to 2.4.6
• Upgrading from Spark SQL 2.4.4 to 2.4.5
• Upgrading from Spark SQL 2.4.3 to 2.4.4
• Upgrading from Spark SQL 2.4 to 2.4.1
• Upgrading from Spark SQL 2.3 to 2.4
• Upgrading from Spark SQL 2.2 to 2.3
• Upgrading from Spark SQL 2.1 to 2.2
• Upgrading from Spark SQL 2.0 to 2.1
• Upgrading from Spark SQL 1.6 to 2.0
• Upgrading from Spark SQL 1.5 to 1.6
• Upgrading from Spark SQL 1.4 to 1.5
• Upgrading from Spark SQL 1.3 to 1.4
• Upgrading from Spark SQL 1.0-1.2 to 1.3
• Compatibility with Apache Hive

Upgrading from Spark SQL 2.4 to 3.0

Dataset/DataFrame APIs

• In Spark 3.0, the Dataset and DataFrame API unionAll is no longer deprecated. It is an alias for union.

• In Spark 2.4 and below, Dataset.groupByKey results to a grouped dataset with key attribute is wrongly named as “value”, if the key is non-struct type, for example, int, string, array, etc. This is counterintuitive and makes the schema of aggregation queries unexpected. For example, the schema of ds.groupByKey(...).count() is (value, count). Since Spark 3.0, we name the grouping attribute to “key”. The old behavior is preserved under a newly added configuration spark.sql.legacy.dataset.nameNonStructGroupingKeyAsValue with a default value of false.

• In Spark 3.0, the column metadata will always be propagated in the API Column.name and Column.as. In Spark version 2.4 and earlier, the metadata of NamedExpression is set as the explicitMetadata for the new column at the time the API is called, it won’t change even if the underlying NamedExpression changes metadata. To restore the behavior before Spark 2.4, you can use the API as(alias: String, metadata: Metadata) with explicit metadata.

DDL Statements

• In Spark 3.0, when inserting a value into a table column with a different data type, the type coercion is performed as per ANSI SQL standard. Certain unreasonable type conversions such as converting string to int and double to boolean are disallowed. A runtime exception is thrown if the value is out-of-range for the data type of the column. In Spark version 2.4 and below, type conversions during table insertion are allowed as long as they are valid Cast. When inserting an out-of-range value to an integral field, the low-order bits of the value is inserted(the same as Java/Scala numeric type casting). For example, if 257 is inserted to a field of byte type, the result is 1. The behavior is controlled by the option spark.sql.storeAssignmentPolicy, with a default value as “ANSI”. Setting the option as “Legacy” restores the previous behavior.

• The ADD JAR command previously returned a result set with the single value 0. It now returns an empty result set.

• Spark 2.4 and below: the SET command works without any warnings even if the specified key is for SparkConf entries and it has no effect because the command does not update SparkConf, but the behavior might confuse users. In 3.0, the command fails if a SparkConf key is used. You can disable such a check by setting spark.sql.legacy.setCommandRejectsSparkCoreConfs to false.

• Refreshing a cached table would trigger a table uncache operation and then a table cache (lazily) operation. In Spark version 2.4 and below, the cache name and storage level are not preserved before the uncache operation. Therefore, the cache name and storage level could be changed unexpectedly. In Spark 3.0, cache name and storage level are first preserved for cache recreation. It helps to maintain a consistent cache behavior upon table refreshing.

• In Spark 3.0, the properties listing below become reserved; commands fail if you specify reserved properties in places like CREATE DATABASE ... WITH DBPROPERTIES and ALTER TABLE ... SET TBLPROPERTIES. You need their specific clauses to specify them, for example, CREATE DATABASE test COMMENT 'any comment' LOCATION 'some path'. You can set spark.sql.legacy.notReserveProperties to true to ignore the ParseException, in this case, these properties will be silently removed, for example: SET DBPROPERTIES('location'='/tmp') will have no effect. In Spark version 2.4 and below, these properties are neither reserved nor have side effects, for example, SET DBPROPERTIES('location'='/tmp') do not change the location of the database but only create a headless property just like 'a'='b'.

  Property (case sensitive)	Database Reserved	Table Reserved	Remarks
  provider	no	yes	For tables, use the USING clause to specify it. Once set, it can’t be changed.
  location	yes	yes	For databases and tables, use the LOCATION clause to specify it.
  owner	yes	yes	For databases and tables, it is determined by the user who runs spark and create the table.

• In Spark 3.0, you can use ADD FILE to add file directories as well. Earlier you could add only single files using this command. To restore the behavior of earlier versions, set spark.sql.legacy.addSingleFileInAddFile to true.

• In Spark 3.0, SHOW TBLPROPERTIES throws AnalysisException if the table does not exist. In Spark version 2.4 and below, this scenario caused NoSuchTableException.

• In Spark 3.0, SHOW CREATE TABLE always returns Spark DDL, even when the given table is a Hive SerDe table. For generating Hive DDL, use SHOW CREATE TABLE AS SERDE command instead.

• In Spark 3.0, column of CHAR type is not allowed in non-Hive-Serde tables, and CREATE/ALTER TABLE commands will fail if CHAR type is detected. Please use STRING type instead. In Spark version 2.4 and below, CHAR type is treated as STRING type and the length parameter is simply ignored.

UDFs and Built-in Functions

• In Spark 3.0, the date_add and date_sub functions accepts only int, smallint, tinyint as the 2nd argument; fractional and non-literal strings are not valid anymore, for example: date_add(cast('1964-05-23' as date), '12.34') causes AnalysisException. Note that, string literals are still allowed, but Spark will throw AnalysisException if the string content is not a valid integer. In Spark version 2.4 and below, if the 2nd argument is fractional or string value, it is coerced to int value, and the result is a date value of 1964-06-04.

• In Spark 3.0, the function percentile_approx and its alias approx_percentile only accept integral value with range in [1, 2147483647] as its 3rd argument accuracy, fractional and string types are disallowed, for example, percentile_approx(10.0, 0.2, 1.8D) causes AnalysisException. In Spark version 2.4 and below, if accuracy is fractional or string value, it is coerced to an int value, percentile_approx(10.0, 0.2, 1.8D) is operated as percentile_approx(10.0, 0.2, 1) which results in 10.0.

• In Spark 3.0, an analysis exception is thrown when hash expressions are applied on elements of MapType. To restore the behavior before Spark 3.0, set spark.sql.legacy.allowHashOnMapType to true.

• In Spark 3.0, when the array/map function is called without any parameters, it returns an empty collection with NullType as element type. In Spark version 2.4 and below, it returns an empty collection with StringType as element type. To restore the behavior before Spark 3.0, you can set spark.sql.legacy.createEmptyCollectionUsingStringType to true.

• In Spark 3.0, the from_json functions supports two modes - PERMISSIVE and FAILFAST. The modes can be set via the mode option. The default mode became PERMISSIVE. In previous versions, behavior of from_json did not conform to either PERMISSIVE nor FAILFAST, especially in processing of malformed JSON records. For example, the JSON string {"a" 1} with the schema a INT is converted to null by previous versions but Spark 3.0 converts it to Row(null).

• In Spark version 2.4 and below, you can create map values with map type key via built-in function such as CreateMap, MapFromArrays, etc. In Spark 3.0, it’s not allowed to create map values with map type key with these built-in functions. Users can use map_entries function to convert map to array<struct<key, value» as a workaround. In addition, users can still read map values with map type key from data source or Java/Scala collections, though it is discouraged.

• In Spark version 2.4 and below, you can create a map with duplicated keys via built-in functions like CreateMap, StringToMap, etc. The behavior of map with duplicated keys is undefined, for example, map look up respects the duplicated key appears first, Dataset.collect only keeps the duplicated key appears last, MapKeys returns duplicated keys, etc. In Spark 3.0, Spark throws RuntimeException when duplicated keys are found. You can set spark.sql.mapKeyDedupPolicy to LAST_WIN to deduplicate map keys with last wins policy. Users may still read map values with duplicated keys from data sources which do not enforce it (for example, Parquet), the behavior is undefined.

• In Spark 3.0, using org.apache.spark.sql.functions.udf(AnyRef, DataType) is not allowed by default. Remove the return type parameter to automatically switch to typed Scala udf is recommended, or set spark.sql.legacy.allowUntypedScalaUDF to true to keep using it. In Spark version 2.4 and below, if org.apache.spark.sql.functions.udf(AnyRef, DataType) gets a Scala closure with primitive-type argument, the returned UDF returns null if the input values is null. However, in Spark 3.0, the UDF returns the default value of the Java type if the input value is null. For example, val f = udf((x: Int) => x, IntegerType), f($"x") returns null in Spark 2.4 and below if column x is null, and return 0 in Spark 3.0. This behavior change is introduced because Spark 3.0 is built with Scala 2.12 by default.

• In Spark 3.0, a higher-order function exists follows the three-valued boolean logic, that is, if the predicate returns any nulls and no true is obtained, then exists returns null instead of false. For example, exists(array(1, null, 3), x -> x % 2 == 0) is null. The previous behaviorcan be restored by setting spark.sql.legacy.followThreeValuedLogicInArrayExists to false.

• In Spark 3.0, the add_months function does not adjust the resulting date to a last day of month if the original date is a last day of months. For example, select add_months(DATE'2019-02-28', 1) results 2019-03-28. In Spark version 2.4 and below, the resulting date is adjusted when the original date is a last day of months. For example, adding a month to 2019-02-28 results in 2019-03-31.

• In Spark version 2.4 and below, the current_timestamp function returns a timestamp with millisecond resolution only. In Spark 3.0, the function can return the result with microsecond resolution if the underlying clock available on the system offers such resolution.

• In Spark 3.0, a 0-argument Java UDF is executed in the executor side identically with other UDFs. In Spark version 2.4 and below, the 0-argument Java UDF alone was executed in the driver side, and the result was propagated to executors, which might be more performant in some cases but caused inconsistency with a correctness issue in some cases.

• The result of java.lang.Math’s log, log1p, exp, expm1, and pow may vary across platforms. In Spark 3.0, the result of the equivalent SQL functions (including related SQL functions like LOG10) return values consistent with java.lang.StrictMath. In virtually all cases this makes no difference in the return value, and the difference is very small, but may not exactly match java.lang.Math on x86 platforms in cases like, for example, log(3.0), whose value varies between Math.log() and StrictMath.log().

• In Spark 3.0, the Cast function processes string literals such as ‘Infinity’, ‘+Infinity’, ‘-Infinity’, ‘NaN’, ‘Inf’, ‘+Inf’, ‘-Inf’ in a case-insensitive manner when casting the literals to Double or Float type to ensure greater compatibility with other database systems. This behavior change is illustrated in the table below:

  Operation	Result before Spark 3.0	Result in Spark 3.0
  CAST(‘infinity’ AS DOUBLE)	NULL	Double.PositiveInfinity
  CAST(‘+infinity’ AS DOUBLE)	NULL	Double.PositiveInfinity
  CAST(‘inf’ AS DOUBLE)	NULL	Double.PositiveInfinity
  CAST(‘inf’ AS DOUBLE)	NULL	Double.PositiveInfinity
  CAST(‘-infinity’ AS DOUBLE)	NULL	Double.NegativeInfinity
  CAST(‘-inf’ AS DOUBLE)	NULL	Double.NegativeInfinity
  CAST(‘infinity’ AS FLOAT)	NULL	Float.PositiveInfinity
  CAST(‘+infinity’ AS FLOAT)	NULL	Float.PositiveInfinity
  CAST(‘inf’ AS FLOAT)	NULL	Float.PositiveInfinity
  CAST(‘+inf’ AS FLOAT)	NULL	Float.PositiveInfinity
  CAST(‘-infinity’ AS FLOAT)	NULL	Float.NegativeInfinity
  CAST(‘-inf’ AS FLOAT)	NULL	Float.NegativeInfinity
  CAST(‘nan’ AS DOUBLE)	NULL	Double.Nan
  CAST(‘nan’ AS FLOAT)	NULL	Float.NaN

• In Spark 3.0, when casting interval values to string type, there is no “interval” prefix, for example, 1 days 2 hours. In Spark version 2.4 and below, the string contains the “interval” prefix like interval 1 days 2 hours.

• In Spark 3.0, when casting string value to integral types(tinyint, smallint, int and bigint), datetime types(date, timestamp and interval) and boolean type, the leading and trailing whitespaces (<= ASCII 32) will be trimmed before converted to these type values, for example, cast(' 1\t' as int) results 1, cast(' 1\t' as boolean) results true, cast('2019-10-10\t as date) results the date value 2019-10-10. In Spark version 2.4 and below, when casting string to integrals and booleans, it does not trim the whitespaces from both ends; the foregoing results is null, while to datetimes, only the trailing spaces (= ASCII 32) are removed.

Query Engine

• In Spark version 2.4 and below, SQL queries such as FROM <table> or FROM <table> UNION ALL FROM <table> are supported by accident. In hive-style FROM <table> SELECT <expr>, the SELECT clause is not negligible. Neither Hive nor Presto support this syntax. These queries are treated as invalid in Spark 3.0.

• In Spark 3.0, the interval literal syntax does not allow multiple from-to units anymore. For example, SELECT INTERVAL '1-1' YEAR TO MONTH '2-2' YEAR TO MONTH' throws parser exception.

• In Spark 3.0, numbers written in scientific notation(for example, 1E2) would be parsed as Double. In Spark version 2.4 and below, they’re parsed as Decimal. To restore the behavior before Spark 3.0, you can set spark.sql.legacy.exponentLiteralAsDecimal.enabled to true.

• In Spark 3.0, day-time interval strings are converted to intervals with respect to the from and to bounds. If an input string does not match to the pattern defined by specified bounds, the ParseException exception is thrown. For example, interval '2 10:20' hour to minute raises the exception because the expected format is [+|-]h[h]:[m]m. In Spark version 2.4, the from bound was not taken into account, and the to bound was used to truncate the resulted interval. For instance, the day-time interval string from the showed example is converted to interval 10 hours 20 minutes. To restore the behavior before Spark 3.0, you can set spark.sql.legacy.fromDayTimeString.enabled to true.

• In Spark 3.0, negative scale of decimal is not allowed by default, for example, data type of literal like 1E10BD is DecimalType(11, 0). In Spark version 2.4 and below, it was DecimalType(2, -9). To restore the behavior before Spark 3.0, you can set spark.sql.legacy.allowNegativeScaleOfDecimal to true.

• In Spark 3.0, the unary arithmetic operator plus(+) only accepts string, numeric and interval type values as inputs. Besides, + with an integral string representation is coerced to a double value, for example, +'1' returns 1.0. In Spark version 2.4 and below, this operator is ignored. There is no type checking for it, thus, all type values with a + prefix are valid, for example, + array(1, 2) is valid and results [1, 2]. Besides, there is no type coercion for it at all, for example, in Spark 2.4, the result of +'1' is string 1.

• In Spark 3.0, Dataset query fails if it contains ambiguous column reference that is caused by self join. A typical example: val df1 = ...; val df2 = df1.filter(...);, then df1.join(df2, df1("a") > df2("a")) returns an empty result which is quite confusing. This is because Spark cannot resolve Dataset column references that point to tables being self joined, and df1("a") is exactly the same as df2("a") in Spark. To restore the behavior before Spark 3.0, you can set spark.sql.analyzer.failAmbiguousSelfJoin to false.

• In Spark 3.0, spark.sql.legacy.ctePrecedencePolicy is introduced to control the behavior for name conflicting in the nested WITH clause. By default value EXCEPTION, Spark throws an AnalysisException, it forces users to choose the specific substitution order they wanted. If set to CORRECTED (which is recommended), inner CTE definitions take precedence over outer definitions. For example, set the config to false, WITH t AS (SELECT 1), t2 AS (WITH t AS (SELECT 2) SELECT * FROM t) SELECT * FROM t2 returns 2, while setting it to LEGACY, the result is 1 which is the behavior in version 2.4 and below.

• In Spark 3.0, configuration spark.sql.crossJoin.enabled become internal configuration, and is true by default, so by default spark won’t raise exception on sql with implicit cross join.

• In Spark version 2.4 and below, float/double -0.0 is semantically equal to 0.0, but -0.0 and 0.0 are considered as different values when used in aggregate grouping keys, window partition keys, and join keys. In Spark 3.0, this bug is fixed. For example, Seq(-0.0, 0.0).toDF("d").groupBy("d").count() returns [(0.0, 2)] in Spark 3.0, and [(0.0, 1), (-0.0, 1)] in Spark 2.4 and below.

• In Spark version 2.4 and below, invalid time zone ids are silently ignored and replaced by GMT time zone, for example, in the from_utc_timestamp function. In Spark 3.0, such time zone ids are rejected, and Spark throws java.time.DateTimeException.

• In Spark 3.0, Proleptic Gregorian calendar is used in parsing, formatting, and converting dates and timestamps as well as in extracting sub-components like years, days and so on. Spark 3.0 uses Java 8 API classes from the java.time packages that are based on ISO chronology. In Spark version 2.4 and below, those operations are performed using the hybrid calendar (Julian + Gregorian. The changes impact on the results for dates before October 15, 1582 (Gregorian) and affect on the following Spark 3.0 API:

  • Parsing/formatting of timestamp/date strings. This effects on CSV/JSON datasources and on the unix_timestamp, date_format, to_unix_timestamp, from_unixtime, to_date, to_timestamp functions when patterns specified by users is used for parsing and formatting. In Spark 3.0, we define our own pattern strings in sql-ref-datetime-pattern.md, which is implemented via java.time.format.DateTimeFormatter under the hood. New implementation performs strict checking of its input. For example, the 2015-07-22 10:00:00 timestamp cannot be parse if pattern is yyyy-MM-dd because the parser does not consume whole input. Another example is the 31/01/2015 00:00 input cannot be parsed by the dd/MM/yyyy hh:mm pattern because hh supposes hours in the range 1-12. In Spark version 2.4 and below, java.text.SimpleDateFormat is used for timestamp/date string conversions, and the supported patterns are described in simpleDateFormat. The old behavior can be restored by setting spark.sql.legacy.timeParserPolicy to LEGACY.

  • The weekofyear, weekday, dayofweek, date_trunc, from_utc_timestamp, to_utc_timestamp, and unix_timestamp functions use java.time API for calculation week number of year, day number of week as well for conversion from/to TimestampType values in UTC time zone.

  • The JDBC options lowerBound and upperBound are converted to TimestampType/DateType values in the same way as casting strings to TimestampType/DateType values. The conversion is based on Proleptic Gregorian calendar, and time zone defined by the SQL config spark.sql.session.timeZone. In Spark version 2.4 and below, the conversion is based on the hybrid calendar (Julian + Gregorian) and on default system time zone.

  • Formatting TIMESTAMP and DATE literals.

  • Creating typed TIMESTAMP and DATE literals from strings. In Spark 3.0, string conversion to typed TIMESTAMP/DATE literals is performed via casting to TIMESTAMP/DATE values. For example, TIMESTAMP '2019-12-23 12:59:30' is semantically equal to CAST('2019-12-23 12:59:30' AS TIMESTAMP). When the input string does not contain information about time zone, the time zone from the SQL config spark.sql.session.timeZone is used in that case. In Spark version 2.4 and below, the conversion is based on JVM system time zone. The different sources of the default time zone may change the behavior of typed TIMESTAMP and DATE literals.

• In Spark 3.0, TIMESTAMP literals are converted to strings using the SQL config spark.sql.session.timeZone. In Spark version 2.4 and below, the conversion uses the default time zone of the Java virtual machine.

• In Spark 3.0, Spark casts String to Date/Timestamp in binary comparisons with dates/timestamps. The previous behavior of casting Date/Timestamp to String can be restored by setting spark.sql.legacy.typeCoercion.datetimeToString.enabled to true.

• In Spark 3.0, special values are supported in conversion from strings to dates and timestamps. Those values are simply notational shorthands that are converted to ordinary date or timestamp values when read. The following string values are supported for dates:
  • epoch [zoneId] - 1970-01-01
  • today [zoneId] - the current date in the time zone specified by spark.sql.session.timeZone
  • yesterday [zoneId] - the current date - 1
  • tomorrow [zoneId] - the current date + 1
  • now - the date of running the current query. It has the same notion as today

  For example SELECT date 'tomorrow' - date 'yesterday'; should output 2. Here are special timestamp values:

  • epoch [zoneId] - 1970-01-01 00:00:00+00 (Unix system time zero)
  • today [zoneId] - midnight today
  • yesterday [zoneId] - midnight yesterday
  • tomorrow [zoneId] - midnight tomorrow
  • now - current query start time

  For example SELECT timestamp 'tomorrow';.

• Since Spark 3.0, when using EXTRACT expression to extract the second field from date/timestamp values, the result will be a DecimalType(8, 6) value with 2 digits for second part, and 6 digits for the fractional part with microsecond precision. e.g. extract(second from to_timestamp('2019-09-20 10:10:10.1')) results 10.100000. In Spark version 2.4 and earlier, it returns an IntegerType value and the result for the former example is 10.

Data Sources

• In Spark version 2.4 and below, when reading a Hive SerDe table with Spark native data sources(parquet/orc), Spark infers the actual file schema and update the table schema in metastore. In Spark 3.0, Spark doesn’t infer the schema anymore. This should not cause any problems to end users, but if it does, set spark.sql.hive.caseSensitiveInferenceMode to INFER_AND_SAVE.

• In Spark version 2.4 and below, partition column value is converted as null if it can’t be casted to corresponding user provided schema. In 3.0, partition column value is validated with user provided schema. An exception is thrown if the validation fails. You can disable such validation by setting spark.sql.sources.validatePartitionColumns to false.

• In Spark 3.0, if files or subdirectories disappear during recursive directory listing (that is, they appear in an intermediate listing but then cannot be read or listed during later phases of the recursive directory listing, due to either concurrent file deletions or object store consistency issues) then the listing will fail with an exception unless spark.sql.files.ignoreMissingFiles is true (default false). In previous versions, these missing files or subdirectories would be ignored. Note that this change of behavior only applies during initial table file listing (or during REFRESH TABLE), not during query execution: the net change is that spark.sql.files.ignoreMissingFiles is now obeyed during table file listing / query planning, not only at query execution time.

• In Spark version 2.4 and below, the parser of JSON data source treats empty strings as null for some data types such as IntegerType. For FloatType, DoubleType, DateType and TimestampType, it fails on empty strings and throws exceptions. Spark 3.0 disallows empty strings and will throw an exception for data types except for StringType and BinaryType. The previous behavior of allowing an empty string can be restored by setting spark.sql.legacy.json.allowEmptyString.enabled to true.

• In Spark version 2.4 and below, JSON datasource and JSON functions like from_json convert a bad JSON record to a row with all nulls in the PERMISSIVE mode when specified schema is StructType. In Spark 3.0, the returned row can contain non-null fields if some of JSON column values were parsed and converted to desired types successfully.

• In Spark 3.0, JSON datasource and JSON function schema_of_json infer TimestampType from string values if they match to the pattern defined by the JSON option timestampFormat. Set JSON option inferTimestamp to false to disable such type inference.

• In Spark version 2.4 and below, CSV datasource converts a malformed CSV string to a row with all nulls in the PERMISSIVE mode. In Spark 3.0, the returned row can contain non-null fields if some of CSV column values were parsed and converted to desired types successfully.

• In Spark 3.0, when Avro files are written with user provided schema, the fields are matched by field names between catalyst schema and Avro schema instead of positions.

• In Spark 3.0, when Avro files are written with user provided non-nullable schema, even the catalyst schema is nullable, Spark is still able to write the files. However, Spark throws runtime NullPointerException if any of the records contains null.

Others

• In Spark version 2.4, when a Spark session is created via cloneSession(), the newly created Spark session inherits its configuration from its parent SparkContext even though the same configuration may exist with a different value in its parent Spark session. In Spark 3.0, the configurations of a parent SparkSession have a higher precedence over the parent SparkContext. You can restore the old behavior by setting spark.sql.legacy.sessionInitWithConfigDefaults to true.

• In Spark 3.0, if hive.default.fileformat is not found in Spark SQL configuration then it falls back to the hive-site.xml file present in the Hadoop configuration of SparkContext.

• In Spark 3.0, we pad decimal numbers with trailing zeros to the scale of the column for spark-sql interface, for example:

  Query	Spark 2.4	Spark 3.0
  SELECT CAST(1 AS decimal(38, 18));	1	1.000000000000000000

• In Spark 3.0, we upgraded the built-in Hive from 1.2 to 2.3 and it brings following impacts:

  • You may need to set spark.sql.hive.metastore.version and spark.sql.hive.metastore.jars according to the version of the Hive metastore you want to connect to. For example: set spark.sql.hive.metastore.version to 1.2.1 and spark.sql.hive.metastore.jars to maven if your Hive metastore version is 1.2.1.

  • You need to migrate your custom SerDes to Hive 2.3 or build your own Spark with hive-1.2 profile. See HIVE-15167 for more details.

  • The decimal string representation can be different between Hive 1.2 and Hive 2.3 when using TRANSFORM operator in SQL for script transformation, which depends on hive’s behavior. In Hive 1.2, the string representation omits trailing zeroes. But in Hive 2.3, it is always padded to 18 digits with trailing zeroes if necessary.

Upgrading from Spark SQL 2.4.5 to 2.4.6

• In Spark 2.4.6, the RESET command does not reset the static SQL configuration values to the default. It only clears the runtime SQL configuration values.

Upgrading from Spark SQL 2.4.4 to 2.4.5

• Since Spark 2.4.5, TRUNCATE TABLE command tries to set back original permission and ACLs during re-creating the table/partition paths. To restore the behaviour of earlier versions, set spark.sql.truncateTable.ignorePermissionAcl.enabled to true.

• Since Spark 2.4.5, spark.sql.legacy.mssqlserver.numericMapping.enabled configuration is added in order to support the legacy MsSQLServer dialect mapping behavior using IntegerType and DoubleType for SMALLINT and REAL JDBC types, respectively. To restore the behaviour of 2.4.3 and earlier versions, set spark.sql.legacy.mssqlserver.numericMapping.enabled to true.

Upgrading from Spark SQL 2.4.3 to 2.4.4

• Since Spark 2.4.4, according to MsSqlServer Guide, MsSQLServer JDBC Dialect uses ShortType and FloatType for SMALLINT and REAL, respectively. Previously, IntegerType and DoubleType is used.

Upgrading from Spark SQL 2.4 to 2.4.1

• The value of spark.executor.heartbeatInterval, when specified without units like “30” rather than “30s”, was inconsistently interpreted as both seconds and milliseconds in Spark 2.4.0 in different parts of the code. Unitless values are now consistently interpreted as milliseconds. Applications that set values like “30” need to specify a value with units like “30s” now, to avoid being interpreted as milliseconds; otherwise, the extremely short interval that results will likely cause applications to fail.

• When turning a Dataset to another Dataset, Spark will up cast the fields in the original Dataset to the type of corresponding fields in the target DataSet. In version 2.4 and earlier, this up cast is not very strict, e.g. Seq("str").toDS.as[Int] fails, but Seq("str").toDS.as[Boolean] works and throw NPE during execution. In Spark 3.0, the up cast is stricter and turning String into something else is not allowed, i.e. Seq("str").toDS.as[Boolean] will fail during analysis.

Upgrading from Spark SQL 2.3 to 2.4

• In Spark version 2.3 and earlier, the second parameter to array_contains function is implicitly promoted to the element type of first array type parameter. This type promotion can be lossy and may cause array_contains function to return wrong result. This problem has been addressed in 2.4 by employing a safer type promotion mechanism. This can cause some change in behavior and are illustrated in the table below.

  Query	Spark 2.3 or Prior	Spark 2.4	Remarks
  SELECT array_contains(array(1), 1.34D);	true	false	In Spark 2.4, left and right parameters are promoted to array type of double type and double type respectively.
  SELECT array_contains(array(1), '1');	true	AnalysisException is thrown.	Explicit cast can be used in arguments to avoid the exception. In Spark 2.4, AnalysisException is thrown since integer type can not be promoted to string type in a loss-less manner.
  SELECT array_contains(array(1), 'anystring');	null	AnalysisException is thrown.	Explicit cast can be used in arguments to avoid the exception. In Spark 2.4, AnalysisException is thrown since integer type can not be promoted to string type in a loss-less manner.

• Since Spark 2.4, when there is a struct field in front of the IN operator before a subquery, the inner query must contain a struct field as well. In previous versions, instead, the fields of the struct were compared to the output of the inner query. Eg. if a is a struct(a string, b int), in Spark 2.4 a in (select (1 as a, 'a' as b) from range(1)) is a valid query, while a in (select 1, 'a' from range(1)) is not. In previous version it was the opposite.

• In versions 2.2.1+ and 2.3, if spark.sql.caseSensitive is set to true, then the CURRENT_DATE and CURRENT_TIMESTAMP functions incorrectly became case-sensitive and would resolve to columns (unless typed in lower case). In Spark 2.4 this has been fixed and the functions are no longer case-sensitive.

• Since Spark 2.4, Spark will evaluate the set operations referenced in a query by following a precedence rule as per the SQL standard. If the order is not specified by parentheses, set operations are performed from left to right with the exception that all INTERSECT operations are performed before any UNION, EXCEPT or MINUS operations. The old behaviour of giving equal precedence to all the set operations are preserved under a newly added configuration spark.sql.legacy.setopsPrecedence.enabled with a default value of false. When this property is set to true, spark will evaluate the set operators from left to right as they appear in the query given no explicit ordering is enforced by usage of parenthesis.

• Since Spark 2.4, Spark will display table description column Last Access value as UNKNOWN when the value was Jan 01 1970.

• Since Spark 2.4, Spark maximizes the usage of a vectorized ORC reader for ORC files by default. To do that, spark.sql.orc.impl and spark.sql.orc.filterPushdown change their default values to native and true respectively. ORC files created by native ORC writer cannot be read by some old Apache Hive releases. Use spark.sql.orc.impl=hive to create the files shared with Hive 2.1.1 and older.

• Since Spark 2.4, writing an empty dataframe to a directory launches at least one write task, even if physically the dataframe has no partition. This introduces a small behavior change that for self-describing file formats like Parquet and Orc, Spark creates a metadata-only file in the target directory when writing a 0-partition dataframe, so that schema inference can still work if users read that directory later. The new behavior is more reasonable and more consistent regarding writing empty dataframe.

• Since Spark 2.4, expression IDs in UDF arguments do not appear in column names. For example, a column name in Spark 2.4 is not UDF:f(col0 AS colA#28) but UDF:f(col0 AS `colA`).

• Since Spark 2.4, writing a dataframe with an empty or nested empty schema using any file formats (parquet, orc, json, text, csv etc.) is not allowed. An exception is thrown when attempting to write dataframes with empty schema.

• Since Spark 2.4, Spark compares a DATE type with a TIMESTAMP type after promotes both sides to TIMESTAMP. To set false to spark.sql.legacy.compareDateTimestampInTimestamp restores the previous behavior. This option will be removed in Spark 3.0.

• Since Spark 2.4, creating a managed table with nonempty location is not allowed. An exception is thrown when attempting to create a managed table with nonempty location. To set true to spark.sql.legacy.allowCreatingManagedTableUsingNonemptyLocation restores the previous behavior. This option will be removed in Spark 3.0.

• Since Spark 2.4, renaming a managed table to existing location is not allowed. An exception is thrown when attempting to rename a managed table to existing location.

• Since Spark 2.4, the type coercion rules can automatically promote the argument types of the variadic SQL functions (e.g., IN/COALESCE) to the widest common type, no matter how the input arguments order. In prior Spark versions, the promotion could fail in some specific orders (e.g., TimestampType, IntegerType and StringType) and throw an exception.

• Since Spark 2.4, Spark has enabled non-cascading SQL cache invalidation in addition to the traditional cache invalidation mechanism. The non-cascading cache invalidation mechanism allows users to remove a cache without impacting its dependent caches. This new cache invalidation mechanism is used in scenarios where the data of the cache to be removed is still valid, e.g., calling unpersist() on a Dataset, or dropping a temporary view. This allows users to free up memory and keep the desired caches valid at the same time.

• In version 2.3 and earlier, Spark converts Parquet Hive tables by default but ignores table properties like TBLPROPERTIES (parquet.compression 'NONE'). This happens for ORC Hive table properties like TBLPROPERTIES (orc.compress 'NONE') in case of spark.sql.hive.convertMetastoreOrc=true, too. Since Spark 2.4, Spark respects Parquet/ORC specific table properties while converting Parquet/ORC Hive tables. As an example, CREATE TABLE t(id int) STORED AS PARQUET TBLPROPERTIES (parquet.compression 'NONE') would generate Snappy parquet files during insertion in Spark 2.3, and in Spark 2.4, the result would be uncompressed parquet files.

• Since Spark 2.0, Spark converts Parquet Hive tables by default for better performance. Since Spark 2.4, Spark converts ORC Hive tables by default, too. It means Spark uses its own ORC support by default instead of Hive SerDe. As an example, CREATE TABLE t(id int) STORED AS ORC would be handled with Hive SerDe in Spark 2.3, and in Spark 2.4, it would be converted into Spark’s ORC data source table and ORC vectorization would be applied. To set false to spark.sql.hive.convertMetastoreOrc restores the previous behavior.

• In version 2.3 and earlier, CSV rows are considered as malformed if at least one column value in the row is malformed. CSV parser dropped such rows in the DROPMALFORMED mode or outputs an error in the FAILFAST mode. Since Spark 2.4, CSV row is considered as malformed only when it contains malformed column values requested from CSV datasource, other values can be ignored. As an example, CSV file contains the “id,name” header and one row “1234”. In Spark 2.4, selection of the id column consists of a row with one column value 1234 but in Spark 2.3 and earlier it is empty in the DROPMALFORMED mode. To restore the previous behavior, set spark.sql.csv.parser.columnPruning.enabled to false.

• Since Spark 2.4, File listing for compute statistics is done in parallel by default. This can be disabled by setting spark.sql.statistics.parallelFileListingInStatsComputation.enabled to False.

• Since Spark 2.4, Metadata files (e.g. Parquet summary files) and temporary files are not counted as data files when calculating table size during Statistics computation.

• Since Spark 2.4, empty strings are saved as quoted empty strings "". In version 2.3 and earlier, empty strings are equal to null values and do not reflect to any characters in saved CSV files. For example, the row of "a", null, "", 1 was written as a,,,1. Since Spark 2.4, the same row is saved as a,,"",1. To restore the previous behavior, set the CSV option emptyValue to empty (not quoted) string.

• Since Spark 2.4, The LOAD DATA command supports wildcard ? and *, which match any one character, and zero or more characters, respectively. Example: LOAD DATA INPATH '/tmp/folder*/' or LOAD DATA INPATH '/tmp/part-?'. Special Characters like space also now work in paths. Example: LOAD DATA INPATH '/tmp/folder name/'.

• In Spark version 2.3 and earlier, HAVING without GROUP BY is treated as WHERE. This means, SELECT 1 FROM range(10) HAVING true is executed as SELECT 1 FROM range(10) WHERE true and returns 10 rows. This violates SQL standard, and has been fixed in Spark 2.4. Since Spark 2.4, HAVING without GROUP BY is treated as a global aggregate, which means SELECT 1 FROM range(10) HAVING true will return only one row. To restore the previous behavior, set spark.sql.legacy.parser.havingWithoutGroupByAsWhere to true.

• In version 2.3 and earlier, when reading from a Parquet data source table, Spark always returns null for any column whose column names in Hive metastore schema and Parquet schema are in different letter cases, no matter whether spark.sql.caseSensitive is set to true or false. Since 2.4, when spark.sql.caseSensitive is set to false, Spark does case insensitive column name resolution between Hive metastore schema and Parquet schema, so even column names are in different letter cases, Spark returns corresponding column values. An exception is thrown if there is ambiguity, i.e. more than one Parquet column is matched. This change also applies to Parquet Hive tables when spark.sql.hive.convertMetastoreParquet is set to true.

Upgrading from Spark SQL 2.2 to 2.3

• Since Spark 2.3, the queries from raw JSON/CSV files are disallowed when the referenced columns only include the internal corrupt record column (named _corrupt_record by default). For example, spark.read.schema(schema).json(file).filter($"_corrupt_record".isNotNull).count() and spark.read.schema(schema).json(file).select("_corrupt_record").show(). Instead, you can cache or save the parsed results and then send the same query. For example, val df = spark.read.schema(schema).json(file).cache() and then df.filter($"_corrupt_record".isNotNull).count().

• The percentile_approx function previously accepted numeric type input and output double type results. Now it supports date type, timestamp type and numeric types as input types. The result type is also changed to be the same as the input type, which is more reasonable for percentiles.

• Since Spark 2.3, the Join/Filter’s deterministic predicates that are after the first non-deterministic predicates are also pushed down/through the child operators, if possible. In prior Spark versions, these filters are not eligible for predicate pushdown.

• Partition column inference previously found incorrect common type for different inferred types, for example, previously it ended up with double type as the common type for double type and date type. Now it finds the correct common type for such conflicts. The conflict resolution follows the table below:

  InputA \ InputB	NullType	IntegerType	LongType	DecimalType(38,0)*	DoubleType	DateType	TimestampType	StringType
  NullType	NullType	IntegerType	LongType	DecimalType(38,0)	DoubleType	DateType	TimestampType	StringType
  IntegerType	IntegerType	IntegerType	LongType	DecimalType(38,0)	DoubleType	StringType	StringType	StringType
  LongType	LongType	LongType	LongType	DecimalType(38,0)	StringType	StringType	StringType	StringType
  DecimalType(38,0)*	DecimalType(38,0)	DecimalType(38,0)	DecimalType(38,0)	DecimalType(38,0)	StringType	StringType	StringType	StringType
  DoubleType	DoubleType	DoubleType	StringType	StringType	DoubleType	StringType	StringType	StringType
  DateType	DateType	StringType	StringType	StringType	StringType	DateType	TimestampType	StringType
  TimestampType	TimestampType	StringType	StringType	StringType	StringType	TimestampType	TimestampType	StringType
  StringType	StringType	StringType	StringType	StringType	StringType	StringType	StringType	StringType

  Note that, for DecimalType(38,0)*, the table above intentionally does not cover all other combinations of scales and precisions because currently we only infer decimal type like BigInteger/BigInt. For example, 1.1 is inferred as double type.

• Since Spark 2.3, when either broadcast hash join or broadcast nested loop join is applicable, we prefer to broadcasting the table that is explicitly specified in a broadcast hint. For details, see the section Join Strategy Hints for SQL Queries and SPARK-22489.

• Since Spark 2.3, when all inputs are binary, functions.concat() returns an output as binary. Otherwise, it returns as a string. Until Spark 2.3, it always returns as a string despite of input types. To keep the old behavior, set spark.sql.function.concatBinaryAsString to true.

• Since Spark 2.3, when all inputs are binary, SQL elt() returns an output as binary. Otherwise, it returns as a string. Until Spark 2.3, it always returns as a string despite of input types. To keep the old behavior, set spark.sql.function.eltOutputAsString to true.

• Since Spark 2.3, by default arithmetic operations between decimals return a rounded value if an exact representation is not possible (instead of returning NULL). This is compliant with SQL ANSI 2011 specification and Hive’s new behavior introduced in Hive 2.2 (HIVE-15331). This involves the following changes

  • The rules to determine the result type of an arithmetic operation have been updated. In particular, if the precision / scale needed are out of the range of available values, the scale is reduced up to 6, in order to prevent the truncation of the integer part of the decimals. All the arithmetic operations are affected by the change, ie. addition (+), subtraction (-), multiplication (*), division (/), remainder (%) and positive module (pmod).

  • Literal values used in SQL operations are converted to DECIMAL with the exact precision and scale needed by them.

  • The configuration spark.sql.decimalOperations.allowPrecisionLoss has been introduced. It defaults to true, which means the new behavior described here; if set to false, Spark uses previous rules, ie. it doesn’t adjust the needed scale to represent the values and it returns NULL if an exact representation of the value is not possible.

• Un-aliased subquery’s semantic has not been well defined with confusing behaviors. Since Spark 2.3, we invalidate such confusing cases, for example: SELECT v.i from (SELECT i FROM v), Spark will throw an analysis exception in this case because users should not be able to use the qualifier inside a subquery. See SPARK-20690 and SPARK-21335 for more details.

• When creating a SparkSession with SparkSession.builder.getOrCreate(), if there is an existing SparkContext, the builder was trying to update the SparkConf of the existing SparkContext with configurations specified to the builder, but the SparkContext is shared by all SparkSessions, so we should not update them. Since 2.3, the builder comes to not update the configurations. If you want to update them, you need to update them prior to creating a SparkSession.

Upgrading from Spark SQL 2.1 to 2.2

• Spark 2.1.1 introduced a new configuration key: spark.sql.hive.caseSensitiveInferenceMode. It had a default setting of NEVER_INFER, which kept behavior identical to 2.1.0. However, Spark 2.2.0 changes this setting’s default value to INFER_AND_SAVE to restore compatibility with reading Hive metastore tables whose underlying file schema have mixed-case column names. With the INFER_AND_SAVE configuration value, on first access Spark will perform schema inference on any Hive metastore table for which it has not already saved an inferred schema. Note that schema inference can be a very time-consuming operation for tables with thousands of partitions. If compatibility with mixed-case column names is not a concern, you can safely set spark.sql.hive.caseSensitiveInferenceMode to NEVER_INFER to avoid the initial overhead of schema inference. Note that with the new default INFER_AND_SAVE setting, the results of the schema inference are saved as a metastore key for future use. Therefore, the initial schema inference occurs only at a table’s first access.

• Since Spark 2.2.1 and 2.3.0, the schema is always inferred at runtime when the data source tables have the columns that exist in both partition schema and data schema. The inferred schema does not have the partitioned columns. When reading the table, Spark respects the partition values of these overlapping columns instead of the values stored in the data source files. In 2.2.0 and 2.1.x release, the inferred schema is partitioned but the data of the table is invisible to users (i.e., the result set is empty).

• Since Spark 2.2, view definitions are stored in a different way from prior versions. This may cause Spark unable to read views created by prior versions. In such cases, you need to recreate the views using ALTER VIEW AS or CREATE OR REPLACE VIEW AS with newer Spark versions.

Upgrading from Spark SQL 2.0 to 2.1

• Datasource tables now store partition metadata in the Hive metastore. This means that Hive DDLs such as ALTER TABLE PARTITION ... SET LOCATION are now available for tables created with the Datasource API.

  • Legacy datasource tables can be migrated to this format via the MSCK REPAIR TABLE command. Migrating legacy tables is recommended to take advantage of Hive DDL support and improved planning performance.

  • To determine if a table has been migrated, look for the PartitionProvider: Catalog attribute when issuing DESCRIBE FORMATTED on the table.

• Changes to INSERT OVERWRITE TABLE ... PARTITION ... behavior for Datasource tables.

  • In prior Spark versions INSERT OVERWRITE overwrote the entire Datasource table, even when given a partition specification. Now only partitions matching the specification are overwritten.

  • Note that this still differs from the behavior of Hive tables, which is to overwrite only partitions overlapping with newly inserted data.

Upgrading from Spark SQL 1.6 to 2.0

• SparkSession is now the new entry point of Spark that replaces the old SQLContext and

  HiveContext. Note that the old SQLContext and HiveContext are kept for backward compatibility. A new catalog interface is accessible from SparkSession - existing API on databases and tables access such as listTables, createExternalTable, dropTempView, cacheTable are moved here.

• Dataset API and DataFrame API are unified. In Scala, DataFrame becomes a type alias for Dataset[Row], while Java API users must replace DataFrame with Dataset<Row>. Both the typed transformations (e.g., map, filter, and groupByKey) and untyped transformations (e.g., select and groupBy) are available on the Dataset class. Since compile-time type-safety in Python and R is not a language feature, the concept of Dataset does not apply to these languages’ APIs. Instead, DataFrame remains the primary programming abstraction, which is analogous to the single-node data frame notion in these languages.

• Dataset and DataFrame API unionAll has been deprecated and replaced by union

• Dataset and DataFrame API explode has been deprecated, alternatively, use functions.explode() with select or flatMap

• Dataset and DataFrame API registerTempTable has been deprecated and replaced by createOrReplaceTempView

• Changes to CREATE TABLE ... LOCATION behavior for Hive tables.

  • From Spark 2.0, CREATE TABLE ... LOCATION is equivalent to CREATE EXTERNAL TABLE ... LOCATION in order to prevent accidental dropping the existing data in the user-provided locations. That means, a Hive table created in Spark SQL with the user-specified location is always a Hive external table. Dropping external tables will not remove the data. Users are not allowed to specify the location for Hive managed tables. Note that this is different from the Hive behavior.

  • As a result, DROP TABLE statements on those tables will not remove the data.

• spark.sql.parquet.cacheMetadata is no longer used. See SPARK-13664 for details.

Upgrading from Spark SQL 1.5 to 1.6

• From Spark 1.6, by default, the Thrift server runs in multi-session mode. Which means each JDBC/ODBC connection owns a copy of their own SQL configuration and temporary function registry. Cached tables are still shared though. If you prefer to run the Thrift server in the old single-session mode, please set option spark.sql.hive.thriftServer.singleSession to true. You may either add this option to spark-defaults.conf, or pass it to start-thriftserver.sh via --conf:

   ./sbin/start-thriftserver.sh \
     --conf spark.sql.hive.thriftServer.singleSession=true \
     ...
   

• From Spark 1.6, LongType casts to TimestampType expect seconds instead of microseconds. This change was made to match the behavior of Hive 1.2 for more consistent type casting to TimestampType from numeric types. See SPARK-11724 for details.

Upgrading from Spark SQL 1.4 to 1.5

• Optimized execution using manually managed memory (Tungsten) is now enabled by default, along with code generation for expression evaluation. These features can both be disabled by setting spark.sql.tungsten.enabled to false.

• Parquet schema merging is no longer enabled by default. It can be re-enabled by setting spark.sql.parquet.mergeSchema to true.

• In-memory columnar storage partition pruning is on by default. It can be disabled by setting spark.sql.inMemoryColumnarStorage.partitionPruning to false.

• Unlimited precision decimal columns are no longer supported, instead Spark SQL enforces a maximum precision of 38. When inferring schema from BigDecimal objects, a precision of (38, 18) is now used. When no precision is specified in DDL then the default remains Decimal(10, 0).

• Timestamps are now stored at a precision of 1us, rather than 1ns

• In the sql dialect, floating point numbers are now parsed as decimal. HiveQL parsing remains unchanged.

• The canonical name of SQL/DataFrame functions are now lower case (e.g., sum vs SUM).

• JSON data source will not automatically load new files that are created by other applications (i.e. files that are not inserted to the dataset through Spark SQL). For a JSON persistent table (i.e. the metadata of the table is stored in Hive Metastore), users can use REFRESH TABLE SQL command or HiveContext’s refreshTable method to include those new files to the table. For a DataFrame representing a JSON dataset, users need to recreate the DataFrame and the new DataFrame will include new files.

Upgrading from Spark SQL 1.3 to 1.4

DataFrame data reader/writer interface

Based on user feedback, we created a new, more fluid API for reading data in (SQLContext.read) and writing data out (DataFrame.write), and deprecated the old APIs (e.g., SQLContext.parquetFile, SQLContext.jsonFile).

See the API docs for SQLContext.read ( Scala, Java, Python ) and DataFrame.write ( Scala, Java, Python ) more information.

DataFrame.groupBy retains grouping columns

Based on user feedback, we changed the default behavior of DataFrame.groupBy().agg() to retain the grouping columns in the resulting DataFrame. To keep the behavior in 1.3, set spark.sql.retainGroupColumns to false.

// In 1.3.x, in order for the grouping column "department" to show up,
// it must be included explicitly as part of the agg function call.
df.groupBy("department").agg($"department", max("age"), sum("expense"))

// In 1.4+, grouping column "department" is included automatically.
df.groupBy("department").agg(max("age"), sum("expense"))

// Revert to 1.3 behavior (not retaining grouping column) by:
sqlContext.setConf("spark.sql.retainGroupColumns", "false")

// In 1.3.x, in order for the grouping column "department" to show up,
// it must be included explicitly as part of the agg function call.
df.groupBy("department").agg(col("department"), max("age"), sum("expense"));

// In 1.4+, grouping column "department" is included automatically.
df.groupBy("department").agg(max("age"), sum("expense"));

// Revert to 1.3 behavior (not retaining grouping column) by:
sqlContext.setConf("spark.sql.retainGroupColumns", "false");

import pyspark.sql.functions as func

# In 1.3.x, in order for the grouping column "department" to show up,
# it must be included explicitly as part of the agg function call.
df.groupBy("department").agg(df["department"], func.max("age"), func.sum("expense"))

# In 1.4+, grouping column "department" is included automatically.
df.groupBy("department").agg(func.max("age"), func.sum("expense"))

# Revert to 1.3.x behavior (not retaining grouping column) by:
sqlContext.setConf("spark.sql.retainGroupColumns", "false")

Behavior change on DataFrame.withColumn

Prior to 1.4, DataFrame.withColumn() supports adding a column only. The column will always be added as a new column with its specified name in the result DataFrame even if there may be any existing columns of the same name. Since 1.4, DataFrame.withColumn() supports adding a column of a different name from names of all existing columns or replacing existing columns of the same name.

Note that this change is only for Scala API, not for PySpark and SparkR.

Upgrading from Spark SQL 1.0-1.2 to 1.3

In Spark 1.3 we removed the “Alpha” label from Spark SQL and as part of this did a cleanup of the available APIs. From Spark 1.3 onwards, Spark SQL will provide binary compatibility with other releases in the 1.X series. This compatibility guarantee excludes APIs that are explicitly marked as unstable (i.e., DeveloperAPI or Experimental).

Rename of SchemaRDD to DataFrame

The largest change that users will notice when upgrading to Spark SQL 1.3 is that SchemaRDD has been renamed to DataFrame. This is primarily because DataFrames no longer inherit from RDD directly, but instead provide most of the functionality that RDDs provide though their own implementation. DataFrames can still be converted to RDDs by calling the .rdd method.

In Scala, there is a type alias from SchemaRDD to DataFrame to provide source compatibility for some use cases. It is still recommended that users update their code to use DataFrame instead. Java and Python users will need to update their code.

Unification of the Java and Scala APIs

Prior to Spark 1.3 there were separate Java compatible classes (JavaSQLContext and JavaSchemaRDD) that mirrored the Scala API. In Spark 1.3 the Java API and Scala API have been unified. Users of either language should use SQLContext and DataFrame. In general these classes try to use types that are usable from both languages (i.e. Array instead of language-specific collections). In some cases where no common type exists (e.g., for passing in closures or Maps) function overloading is used instead.

Additionally, the Java specific types API has been removed. Users of both Scala and Java should use the classes present in org.apache.spark.sql.types to describe schema programmatically.

Isolation of Implicit Conversions and Removal of dsl Package (Scala-only)

Many of the code examples prior to Spark 1.3 started with import sqlContext._, which brought all of the functions from sqlContext into scope. In Spark 1.3 we have isolated the implicit conversions for converting RDDs into DataFrames into an object inside of the SQLContext. Users should now write import sqlContext.implicits._.

Additionally, the implicit conversions now only augment RDDs that are composed of Products (i.e., case classes or tuples) with a method toDF, instead of applying automatically.

When using function inside of the DSL (now replaced with the DataFrame API) users used to import org.apache.spark.sql.catalyst.dsl. Instead the public dataframe functions API should be used: import org.apache.spark.sql.functions._.

Removal of the type aliases in org.apache.spark.sql for DataType (Scala-only)

Spark 1.3 removes the type aliases that were present in the base sql package for DataType. Users should instead import the classes in org.apache.spark.sql.types

UDF Registration Moved to sqlContext.udf (Java & Scala)

Functions that are used to register UDFs, either for use in the DataFrame DSL or SQL, have been moved into the udf object in SQLContext.

sqlContext.udf.register("strLen", (s: String) => s.length())

sqlContext.udf().register("strLen", (String s) -> s.length(), DataTypes.IntegerType);

Python UDF registration is unchanged.

Compatibility with Apache Hive

Spark SQL is designed to be compatible with the Hive Metastore, SerDes and UDFs. Currently, Hive SerDes and UDFs are based on built-in Hive, and Spark SQL can be connected to different versions of Hive Metastore (from 0.12.0 to 2.3.7 and 3.0.0 to 3.1.2. Also see Interacting with Different Versions of Hive Metastore).

Deploying in Existing Hive Warehouses

The Spark SQL Thrift JDBC server is designed to be “out of the box” compatible with existing Hive installations. You do not need to modify your existing Hive Metastore or change the data placement or partitioning of your tables.

Supported Hive Features

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

• Hive query statements, including:
  • SELECT
  • GROUP BY
  • ORDER BY
  • DISTRIBUTE BY
  • CLUSTER BY
  • SORT BY
• All Hive operators, including:
  • Relational operators (=, <=>, ==, <>, <, >, >=, <=, etc)
  • Arithmetic operators (+, -, *, /, %, etc)
  • Logical operators (AND, &&, OR, ||, etc)
  • Complex type constructors
  • Mathematical functions (sign, ln, cos, etc)
  • String functions (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
  • LEFT ANTI JOIN
  • CROSS JOIN
• Unions
• Sub-queries

  • Sub-queries in the FROM Clause

    SELECT col FROM (SELECT a + b AS col FROM t1) t2

  • Sub-queries in WHERE Clause

    • Correlated or non-correlated IN and NOT IN statement in WHERE Clause

      SELECT col FROM t1 WHERE col IN (SELECT a FROM t2 WHERE t1.a = t2.a)
      SELECT col FROM t1 WHERE col IN (SELECT a FROM t2)
      

    • Correlated or non-correlated EXISTS and NOT EXISTS statement in WHERE Clause

      SELECT col FROM t1 WHERE EXISTS (SELECT t2.a FROM t2 WHERE t1.a = t2.a AND t2.a > 10)
      SELECT col FROM t1 WHERE EXISTS (SELECT t2.a FROM t2 WHERE t2.a > 10)
      

    • Non-correlated IN and NOT IN statement in JOIN Condition

      SELECT t1.col FROM t1 JOIN t2 ON t1.a = t2.a AND t1.a IN (SELECT a FROM t3)

    • Non-correlated EXISTS and NOT EXISTS statement in JOIN Condition

      SELECT t1.col FROM t1 JOIN t2 ON t1.a = t2.a AND EXISTS (SELECT * FROM t3 WHERE t3.a > 10)

• Sampling
• Explain
• Partitioned tables including dynamic partition insertion
• View

  • If column aliases are not specified in view definition queries, both Spark and Hive will generate alias names, but in different ways. In order for Spark to be able to read views created by Hive, users should explicitly specify column aliases in view definition queries. As an example, Spark cannot read v1 created as below by Hive.

    CREATE VIEW v1 AS SELECT * FROM (SELECT c + 1 FROM (SELECT 1 c) t1) t2;
    

    Instead, you should create v1 as below with column aliases explicitly specified.

    CREATE VIEW v1 AS SELECT * FROM (SELECT c + 1 AS inc_c FROM (SELECT 1 c) t1) t2;
    

• All Hive DDL Functions, including:
  • CREATE TABLE
  • CREATE TABLE AS SELECT
  • CREATE TABLE LIKE
  • ALTER TABLE
• Most Hive Data types, including:
  • TINYINT
  • SMALLINT
  • INT
  • BIGINT
  • BOOLEAN
  • FLOAT
  • DOUBLE
  • STRING
  • BINARY
  • TIMESTAMP
  • DATE
  • ARRAY<>
  • MAP<>
  • STRUCT<>

Unsupported Hive Functionality

Below is a list of Hive features that we don’t support yet. Most of these features are rarely used in Hive deployments.

Major Hive Features

• Tables with buckets: bucket is the hash partitioning within a Hive table partition. Spark SQL doesn’t support buckets yet.

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/Output Formats

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

Hive Optimizations

A handful of Hive optimizations are not yet included in Spark. Some of these (such as indexes) are less important due to Spark SQL’s in-memory computational model. Others are slotted for future releases of Spark SQL.

• Block-level bitmap indexes and virtual columns (used to build indexes)
• Automatically determine the number of reducers for joins and groupbys: Currently, in Spark SQL, you need to control the degree of parallelism post-shuffle using “SET spark.sql.shuffle.partitions=[num_tasks];”.
• Meta-data only query: For queries that can be answered by using only metadata, Spark SQL still launches tasks to compute the result.
• Skew data flag: Spark SQL does not follow the skew data flags 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.

Hive UDF/UDTF/UDAF

Not all the APIs of the Hive UDF/UDTF/UDAF are supported by Spark SQL. Below are the unsupported APIs:

• getRequiredJars and getRequiredFiles (UDF and GenericUDF) are functions to automatically include additional resources required by this UDF.
• initialize(StructObjectInspector) in GenericUDTF is not supported yet. Spark SQL currently uses a deprecated interface initialize(ObjectInspector[]) only.
• configure (GenericUDF, GenericUDTF, and GenericUDAFEvaluator) is a function to initialize functions with MapredContext, which is inapplicable to Spark.
• close (GenericUDF and GenericUDAFEvaluator) is a function to release associated resources. Spark SQL does not call this function when tasks finish.
• reset (GenericUDAFEvaluator) is a function to re-initialize aggregation for reusing the same aggregation. Spark SQL currently does not support the reuse of aggregation.
• getWindowingEvaluator (GenericUDAFEvaluator) is a function to optimize aggregation by evaluating an aggregate over a fixed window.

Incompatible Hive UDF

Below are the scenarios in which Hive and Spark generate different results:

• SQRT(n) If n < 0, Hive returns null, Spark SQL returns NaN.
• ACOS(n) If n < -1 or n > 1, Hive returns null, Spark SQL returns NaN.
• ASIN(n) If n < -1 or n > 1, Hive returns null, Spark SQL returns NaN.
• CAST(n AS TIMESTAMP) If n is integral numbers, Hive treats n as milliseconds, Spark SQL treats n as seconds.