AnalysisException is replaced by its sub-classes that are thrown for tables from Hive external catalog in the following situations:ALTER TABLE .. ADD PARTITION throws PartitionsAlreadyExistException if new partition exists alreadyALTER TABLE .. DROP PARTITION throws NoSuchPartitionsException for not existing partitionsRESET command does not reset the static SQL configuration values to the default. It only clears the runtime SQL configuration values.Starting from 2.4.5, SQL configurations are effective also when a Dataset is converted to an RDD and its plan is executed due to action on the derived RDD. The previous behavior can be restored setting spark.sql.legacy.rdd.applyConf to false: in this case, SQL configurations are ignored for operations performed on a RDD derived from a Dataset.
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.
- Since Spark 2.4.4, according to [MsSqlServer Guide](https://docs.microsoft.com/en-us/sql/connect/jdbc/using-basic-data-types?view=sql-server-2017), MsSQLServer JDBC Dialect uses ShortType and FloatType for SMALLINT and REAL, respectively. Previously, IntegerType and DoubleType is used.
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.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.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.
In PySpark, when Arrow optimization is enabled, previously toPandas just failed when Arrow optimization is unable to be used whereas createDataFrame from Pandas DataFrame allowed the fallback to non-optimization. Now, both toPandas and createDataFrame from Pandas DataFrame allow the fallback by default, which can be switched off by spark.sql.execution.arrow.fallback.enabled.
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.
pandas_udf and toPandas()/createDataFrame() with spark.sql.execution.arrow.enabled set to True, has been marked as experimental. These are still evolving and not currently recommended for use in production.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:
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.
In PySpark, now we need Pandas 0.19.2 or upper if you want to use Pandas related functionalities, such as toPandas, createDataFrame from Pandas DataFrame, etc.
In PySpark, the behavior of timestamp values for Pandas related functionalities was changed to respect session timezone. If you want to use the old behavior, you need to set a configuration spark.sql.execution.pandas.respectSessionTimeZone to False. See SPARK-22395 for details.
In PySpark, na.fill() or fillna also accepts boolean and replaces nulls with booleans. In prior Spark versions, PySpark just ignores it and returns the original Dataset/DataFrame.
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 Broadcast Hint 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.
In PySpark, df.replace does not allow to omit value when to_replace is not a dictionary. Previously, value could be omitted in the other cases and had None by default, which is counterintuitive and error-prone.
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.
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.
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.
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.
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:
{% highlight bash %} ./sbin/start-thriftserver.sh
--conf spark.sql.hive.thriftServer.singleSession=true
... {% endhighlight %}
Since 1.6.1, withColumn method in sparkR supports adding a new column to or replacing existing columns of the same name of a DataFrame.
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.
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.
Resolution of strings to columns in python now supports using dots (.) to qualify the column or access nested values. For example df['table.column.nestedField']. However, this means that if your column name contains any dots you must now escape them using backticks (e.g., table.`column.with.dots`.nested).
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.
DataFrame.withColumn method in pySpark supports adding a new column or replacing existing columns of the same name.
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.
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”)
{% endhighlight %}
// 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”);
{% endhighlight %}
import pyspark.sql.functions as func
df.groupBy(“department”).agg(df[“department”], func.max(“age”), func.sum(“expense”))
df.groupBy(“department”).agg(func.max(“age”), func.sum(“expense”))
sqlContext.setConf(“spark.sql.retainGroupColumns”, “false”)
{% endhighlight %}
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.
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).
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.
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.
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._.
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
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())
{% endhighlight %}
sqlContext.udf().register(“strLen”, (String s) -> s.length(), DataTypes.IntegerType);
{% endhighlight %}
Python UDF registration is unchanged.
When using DataTypes in Python you will need to construct them (i.e. StringType()) instead of referencing a singleton.