diff --git a/src/docs/user/hive.txt b/src/docs/user/hive.txt index 75a389be..979e7afc 100644 --- a/src/docs/user/hive.txt +++ b/src/docs/user/hive.txt @@ -112,10 +112,14 @@ configuring a new Hive table with the correct InputFormat. This feature currently requires that all partitions of a table be compressed with the lzop codec. -The user can specify the +\--external-table-dir+ option in the sqoop command to -work with an external Hive table (instead of a managed table, i.e. the default behavior). -To import data into an external table, one has to specify +\--hive-import+ in the command -line arguments. Table creation is also supported with the use of +\--create-hive-table+. +External table import ++++++++++++++++++++++ + +You can specify the +\--external-table-dir+ option in the sqoop command to +work with an external Hive table (instead of a managed table, i.e. the default +behavior). To import data into an external table, one has to specify the ++\--hive-import+ option in the command line arguments. Table creation is +also supported with the use of the +\--create-hive-table+ option. Importing into an external Hive table: ---- @@ -126,3 +130,38 @@ Create an external Hive table: ---- $ sqoop import --hive-import --create-hive-table --connect $CONN --table $TABLENAME --username $USER --password $PASS --external-table-dir /tmp/foobar_example --hive-table foobar ---- + +Decimals in Hive import using parquet file +++++++++++++++++++++++++++++++++++++++++++ + +As mentioned above, a Hive import is a two-step process in Sqoop: +first, the data is imported onto HDFS, then a HQL statement is generated and +executed to create the Hive table. + +During the first step, an Avro schema is generated from the SQL data types. +This schema is then used in a regular Parquet import. After the data was +imported onto HDFS successfully, Sqoop takes the Avro schema, maps the Avro +types to Hive types and to generates the HQL statement to create the table. + +Decimal SQL types are converted to Strings in a parquet import per default, +so Decimal columns appear as String columns in Hive per default. You can change +this behavior by enabling logical types for parquet, so that Decimals will be +properly mapped to the Hive type Decimal as well. This can be done with the ++sqoop.parquet.logical_types.decimal.enable+ property. As noted in the section +discussing 'Enabling Logical Types in Avro and Parquet import for numbers', +you should also specify the default precision and scale and enable padding. + +A limitation of Hive is that the maximum precision and scale is 38. When +converting to the Hive Decimal type, precision and scale will be reduced +if necessary to meet this limitation, automatically. The data itself however, +will only have to adhere to the limitations of the Avro schema, thus values +with a precision and scale bigger than 38 are allowed and will be present on +storage, but they won't be readable by Hive, (since Hive is a +schema-on-read tool). + +Enabling padding and specifying a default precision and scale in a Hive Import: +---- +$ sqoop import -Dsqoop.avro.decimal_padding.enable=true -Dsqoop.parquet.logical_types.decimal.enable=true + -Dsqoop.avro.logical_types.decimal.default.precision=38 -Dsqoop.avro.logical_types.decimal.default.scale=10 + --hive-import --connect $CONN --table $TABLENAME --username $USER --password $PASS --as-parquetfile +---- diff --git a/src/docs/user/import.txt b/src/docs/user/import.txt index 79f71012..ae7c7ed6 100644 --- a/src/docs/user/import.txt +++ b/src/docs/user/import.txt @@ -472,36 +472,48 @@ Enabling Logical Types in Avro and Parquet import for numbers ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ To enable the use of logical types in Sqoop's avro schema generation, -i.e. used during both avro and parquet imports, one has to use the -sqoop.avro.logical_types.decimal.enable flag. This is necessary if one +i.e. used both during avro and parquet imports, one has to use the ++sqoop.avro.logical_types.decimal.enable+ property. This is necessary if one wants to store values as decimals in the avro file format. -Padding number types in avro import -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +In case of a parquet import, one has to use the ++sqoop.parquet.logical_types.decimal.enable+ property. + +Padding number types in avro and parquet import ++++++++++++++++++++++++++++++++++++++++++++++++ Certain databases, such as Oracle and Postgres store number and decimal values without padding. For example 1.5 in a column declared -as NUMBER (20,5) is stored as is in Oracle, while the equivalent +as NUMBER (20, 5) is stored as is in Oracle, while the equivalent DECIMAL (20, 5) is stored as 1.50000 in an SQL server instance. -This leads to a scale mismatch during avro import. +This leads to a scale mismatch during the import. -To avoid this error, one can use the sqoop.avro.decimal_padding.enable flag -to turn on padding with 0s. This flag has to be used together with the -sqoop.avro.logical_types.decimal.enable flag set to true. +To avoid this error, one can use the +sqoop.avro.decimal_padding.enable+ +property to turn on padding with 0s during import. Naturally, this property is +used together with logical types enabled, either in avro or in parquet import. -Default precision and scale in avro import -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +Default precision and scale in avro and parquet import +++++++++++++++++++++++++++++++++++++++++++++++++++++++ All of the databases allow users to specify numeric columns without a precision or scale. While MS SQL and MySQL translate these into -a valid precision and scale values, Oracle and Postgres don't. +valid precision and scale, Oracle and Postgres don't. -Therefore, when a table contains NUMBER in a table in Oracle or -NUMERIC/DECIMAL in Postgres, one can specify a default precision and scale -to be used in the avro schema by using the +sqoop.avro.logical_types.decimal.default.precision+ -and +sqoop.avro.logical_types.decimal.default.scale+ flags. +When a table contains a NUMBER column in Oracle or NUMERIC/DECIMAL in +Postgres, one can specify a default precision and scale to be used in the +avro schema by using the +sqoop.avro.logical_types.decimal.default.precision+ +and +sqoop.avro.logical_types.decimal.default.scale+ properties. Avro padding also has to be enabled, if the values are shorter than -the specified default scale. +the specified default scale, together with logical types. + +Even though the name of the properties contain 'avro', the very same properties +(+sqoop.avro.logical_types.decimal.default.precision+ and ++sqoop.avro.logical_types.decimal.default.scale+) +can be used to specify defaults during a parquet import as well. + +The implementation of this logic and the padding is database independent. +However, our tests cover Oracle, Postgres, MS Sql server and MySQL databases +only, therefore these are the supported ones. Large Objects ^^^^^^^^^^^^^ @@ -838,20 +850,27 @@ $ sqoop import --connect jdbc:mysql://db.foo.com/corp \ ---- Enabling logical types in avro import and also turning on padding with 0s: - ---- $ sqoop import -Dsqoop.avro.decimal_padding.enable=true -Dsqoop.avro.logical_types.decimal.enable=true - --connect $CON --username $USER --password $PASS --query "select * from table_name where \$CONDITIONS" + --connect $MYCONN --username $MYUSER --password $MYPASS --query "select * from table_name where \$CONDITIONS" --target-dir hdfs://nameservice1//etl/target_path --as-avrodatafile --verbose -m 1 ---- Enabling logical types in avro import and also turning on padding with 0s, while specifying default precision and scale as well: - ---- $ sqoop import -Dsqoop.avro.decimal_padding.enable=true -Dsqoop.avro.logical_types.decimal.enable=true -Dsqoop.avro.logical_types.decimal.default.precision=38 -Dsqoop.avro.logical_types.decimal.default.scale=10 - --connect $CON --username $USER --password $PASS --query "select * from table_name where \$CONDITIONS" + --connect $MYCONN --username $MYUSER --password $MYPASS --query "select * from table_name where \$CONDITIONS" --target-dir hdfs://nameservice1//etl/target_path --as-avrodatafile --verbose -m 1 ---- + +Enabling logical types in parquet import and also turning on padding with 0s, while specifying default precision and scale as well: +---- +$ sqoop import -Dsqoop.parquet.logical_types.decimal.enable=true -Dsqoop.avro.decimal_padding.enable=true + -Dsqoop.avro.logical_types.decimal.default.precision=38 -Dsqoop.avro.logical_types.decimal.default.scale=10 + --connect $MYCONN --username $MYUSER --password $MYPASS --query "select * from table_name where \$CONDITIONS" + --target-dir hdfs://nameservice1//etl/target_path --as-parquetfile --verbose -m 1 + +----