If PXF cannot find the schema file locally, it searches for the file on HDFS.
If PXF does not find the schema file there, it searches for the file relative to the PXF classpath. PXF first searches for an absolute file path on the Greenplum hosts. When you provide the Avro schema file to PXF, the file must reside in the same location on each Greenplum Database host or the file may reside on the Hadoop file system. PXF creates the Avro schema based on the external table definition.
PXF uses the schema embedded in the Avro data file. PXF uses the specified schema this overrides the schema embedded in the Avro data file. You can provide either a binary *.avro file or a JSON-format *.avsc file for the schema file: External Table Type You can specify an Avro schema on both read and write operations to HDFS. avsc suffix.įields in an Avro schema file are defined via an array of objects, each of which is specified by a name and a type.Īn Avro data file contains the schema and a compact binary representation of the data. Avro Schemas and DataĪvro schemas are defined using JSON, and composed of the same primitive and complex types identified in the data type mapping section above. Long, boolean, bytes, double, int, float, string (one-dimensional array)ġ PXF right-pads char types to length n, if required, with white space.Ģ PXF converts Greenplum smallint types to int before it writes the Avro data. Numeric, date, time, timestamp, timestamptzĪrray (any dimension) of type: bigint, boolean, bytea, double, int, real, textĪrray (any dimension) of type: long, boolean, bytes, double, int, float, stringīigint, boolean, bytea, double, int, real, text PXF uses the following data type mapping when writing Avro data:
PXF supports writing other complex types to Avro as string. PXF supports writing Avro primitive types and arrays of Avro primitive types. Logical type: Local Timestamp (microsecond precision) Logical type: Local Timestamp (millisecond precision) Logical type: Timestamp (microsecond precision) Logical type: Timestamp (millisecond precision) Logical type: Time (microsecond precision) Logical type: Time (millisecond precision) Text, with delimiters inserted between collection items, mapped key-value pairs, and record data.īytea (supported for read operations only).įollows the above conventions for primitive or complex data types, depending on the union must contain 2 elements, one of which must be null. PXF uses the following data type mapping when reading Avro data: Avro Data TypeĬomplex type: Array (any dimension) of type: boolean, bytes, double, float, int, long, stringĪrray (any dimension) of type: boolean, bytea, double, real, bigint, text
#POSTGRES BOOLEAN CODE#
While PXF does not natively support reading these types, you can create Greenplum Database functions or application code to extract or further process subcomponents of these complex data types.Īvro supports logical data types including date, decimal, duration, time, timestamp, and uuid types. Map top-level fields of these complex data types to the Greenplum Database text type.
To represent Avro primitive data types and Avro arrays of primitive types in Greenplum Database, map data values to Greenplum Database columns of the same type.Īvro supports other complex data types including arrays of non-primitive types, maps, records, enumerations, and fixed types. The Avro specification defines primitive, complex, and logical types. An Avro schema, together with its data, is fully self-describing. Avro format data has an independent schema, also defined in JSON. Avro specifies that data types be defined in JSON. Working with Avro DataĪpache Avro is a data serialization framework where the data is serialized in a compact binary format. PrerequisitesĮnsure that you have met the PXF Hadoop Prerequisites before you attempt to read data from HDFS. PXF supports reading or writing Avro files compressed with these codecs: bzip2, xz, snappy, and deflate.
#POSTGRES BOOLEAN HOW TO#
This section describes how to use PXF to read and write Avro data in HDFS, including how to create, query, and insert into an external table that references an Avro file in the HDFS data store. Use the PXF HDFS Connector to read and write Avro-format data.
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