Use the PXF HDFS Connector to read and write Avro-format data. 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.

PXF supports reading or writing Avro files compressed with these codecs: bzip2, xz, snappy, and deflate.


Ensure that you have met the PXF Hadoop Prerequisites before you attempt to read data from HDFS.

Working with Avro Data

Apache Avro is a data serialization framework where the data is serialized in a compact binary format. Avro specifies that data types be defined in JSON. Avro format data has an independent schema, also defined in JSON. An Avro schema, together with its data, is fully self-describing.

Data Type Mapping

The Avro specification defines primitive, complex, and logical types.

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.

Avro supports other complex data types including arrays of non-primitive types, maps, records, enumerations, and fixed types. Map top-level fields of these complex data types to the Greenplum Database text type. 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.

Avro supports logical data types including date, decimal, duration, time, timestamp, and uuid types.

Read Mapping

PXF uses the following data type mapping when reading Avro data:

Avro Data Type PXF/Greenplum Data Type
boolean boolean
bytes bytea
double double
float real
int int
long bigint
string text
Complex type: Array (any dimension) of type: boolean, bytes, double, float, int, long, string array (any dimension) of type: boolean, bytea, double, real, bigint, text
Complex type: Array of other types
(Avro schema is provided)
Complex type: Map, Record, or Enum text, with delimiters inserted between collection items, mapped key-value pairs, and record data.
Complex type: Fixed bytea (supported for read operations only).
Union Follows the above conventions for primitive or complex data types, depending on the union; must contain 2 elements, one of which must be null.
Logical type: Date date
Logical type: Decimal decimal or numeric
Logical type: duration bytea
Logical type: Time (millisecond precision) time (time without time zone)
Logical type: Time (microsecond precision) time (time without time zone)
Logical type: Timestamp (millisecond precision) timestamp (with or without time zone)
Logical type: Timestamp (microsecond precision) timestamp (with or without time zone)
Logical type: Local Timestamp (millisecond precision) timestamp (with or without time zone)
Logical type: Local Timestamp (microsecond precision) timestamp (with or without time zone)
Logical type: UUID UUID

Write Mapping

PXF supports writing Avro primitive types and arrays of Avro primitive types. PXF supports writing other complex types to Avro as string.

PXF uses the following data type mapping when writing Avro data:

PXF/Greenplum Data Type Avro Data Type
bigint long
boolean boolean
bytea bytes
double double
char1 string
enum string
int int
real float
smallint2 int
text string
varchar string
numeric, date, time, timestamp, timestamptz
(no Avro schema is provided)
array (any dimension) of type: bigint, boolean, bytea, double, int, real, text
(Avro schema is provided)
Array (any dimension) of type: long, boolean, bytes, double, int, float, string
bigint[], boolean[], bytea[], double[], int[], real[], text[]
(no Avro schema is provided)
long[], boolean[], bytes[], double[], int[], float[], string[] (one-dimensional array)
numeric[], date[], time[], timestamp[], timestamptz[]
(Avro is schema is provided)
enum, record string

1 PXF right-pads char[n] types to length n, if required, with white space.
2 PXF converts Greenplum smallint types to int before it writes the Avro data. Be sure to read the field into an int.

Avro Schemas and Data

Avro schemas are defined using JSON, and composed of the same primitive and complex types identified in the data type mapping section above. Avro schema files typically have a .avsc suffix.

Fields in an Avro schema file are defined via an array of objects, each of which is specified by a name and a type.

An Avro data file contains the schema and a compact binary representation of the data. Avro data files typically have the .avro suffix.

You can specify an Avro schema on both read and write operations to HDFS. You can provide either a binary *.avro file or a JSON-format *.avsc file for the schema file:

External Table Type Schema Specified? Description
readable yes PXF uses the specified schema; this overrides the schema embedded in the Avro data file.
readable no PXF uses the schema embedded in the Avro data file.
writable yes PXF uses the specified schema.
writable no PXF creates the Avro schema based on the external table definition.

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 first searches for an absolute file path on the Greenplum hosts. If PXF does not find the schema file there, it searches for the file relative to the PXF classpath. If PXF cannot find the schema file locally, it searches for the file on HDFS.

The $PXF_BASE/conf directory is in the PXF classpath. PXF can locate an Avro schema file that you add to this directory on every Greenplum Database host.

See Writing Avro Data for additional schema considerations when writing Avro data to HDFS.

Creating the External Table

Use the hdfs:avro profile to read or write Avro-format data in HDFS. The following syntax creates a Greenplum Database readable external table that references such a file:

    ( <column_name> <data_type> [, ...] | LIKE <other_table> )
LOCATION ('pxf://<path-to-hdfs-file>?PROFILE=hdfs:avro[&SERVER=<server_name>][&<custom-option>=<value>[...]]')
FORMAT 'CUSTOM' (FORMATTER='pxfwritable_import'|'pxfwritable_export');
[DISTRIBUTED BY (<column_name> [, ... ] ) | DISTRIBUTED RANDOMLY];

The specific keywords and values used in the Greenplum Database CREATE EXTERNAL TABLE command are described in the table below.

Keyword Value
<path‑to‑hdfs‑file> The path to the directory or file in the HDFS data store. When the <server_name> configuration includes a pxf.fs.basePath property setting, PXF considers <path‑to‑hdfs‑file> to be relative to the base path specified. Otherwise, PXF considers it to be an absolute path. <path‑to‑hdfs‑file> must not specify a relative path nor include the dollar sign ($) character.
PROFILE The PROFILE keyword must specify hdfs:avro.
SERVER=<server_name> The named server configuration that PXF uses to access the data. PXF uses the default server if not specified.
<custom‑option> <custom-option>s are discussed below.
FORMAT ‘CUSTOM’ Use FORMATCUSTOM’ with (FORMATTER='pxfwritable_export') (write) or (FORMATTER='pxfwritable_import') (read).
DISTRIBUTED BY If you want to load data from an existing Greenplum Database table into the writable external table, consider specifying the same distribution policy or <column_name> on both tables. Doing so will avoid extra motion of data between segments on the load operation.

For complex types, the PXF hdfs:avro profile inserts default delimiters between collection items and values before display. You can use non-default delimiter characters by identifying values for specific hdfs:avro custom options in the CREATE EXTERNAL TABLE command.

The hdfs:avro profile supports the following <custom-option>s:

Option Keyword Description
COLLECTION_DELIM The delimiter character(s) placed between entries in a top-level array, map, or record field when PXF maps an Avro complex data type to a text column. The default is the comma (,) character. (Read)
MAPKEY_DELIM The delimiter character(s) placed between the key and value of a map entry when PXF maps an Avro complex data type to a text column. The default is the colon : character. (Read)
RECORDKEY_DELIM The delimiter character(s) placed between the field name and value of a record entry when PXF maps an Avro complex data type to a text column. The default is the colon : character. (Read)
SCHEMA The absolute path to the Avro schema file on the Greenplum host or on HDFS, or the relative path to the schema file on the host. (Read and Write)
IGNORE_MISSING_PATH A Boolean value that specifies the action to take when <path-to-hdfs-file> is missing or invalid. The default value is false, PXF returns an error in this situation. When the value is true, PXF ignores missing path errors and returns an empty fragment. (Read)

The PXF hdfs:avro profile supports encoding- and compression-related write options. You specify these write options in the CREATE WRITABLE EXTERNAL TABLE LOCATION clause. The hdfs:avro profile supports the following custom write options:

Write Option Value Description
COMPRESSION_CODEC The compression codec alias. Supported compression codecs for writing Avro data include: bzip2, xz, snappy, deflate, and uncompressed . If this option is not provided, PXF compresses the data using deflate compression.
CODEC_LEVEL The compression level (applicable to the deflate and xz codecs only). This level controls the trade-off between speed and compression. Valid values are 1 (fastest) to 9 (most compressed). The default compression level is 6.

Example: Reading Avro Data

The examples in this section will operate on Avro data with the following field name and data type record schema:

  • id - long
  • username - string
  • followers - array of string (string[])
  • fmap - map of long
  • relationship - enumerated type
  • address - record comprised of street number (int), street name (string), and city (string)

You create an Avro schema and data file, and then create a readable external table to read the data.

Create Schema

Perform the following operations to create an Avro schema to represent the example schema described above.

  1. Create a file named avro_schema.avsc:

    $ vi /tmp/avro_schema.avsc
  2. Copy and paste the following text into avro_schema.avsc:

    "type" : "record",
      "name" : "example_schema",
      "namespace" : "com.example",
      "fields" : [ {
        "name" : "id",
        "type" : "long",
        "doc" : "Id of the user account"
      }, {
        "name" : "username",
        "type" : "string",
        "doc" : "Name of the user account"
      }, {
        "name" : "followers",
        "type" : {"type": "array", "items": "string"},
        "doc" : "Users followers"
      }, {
        "name": "fmap",
        "type": {"type": "map", "values": "long"}
      }, {
        "name": "relationship",
        "type": {
            "type": "enum",
            "name": "relationshipEnum",
            "symbols": ["MARRIED","LOVE","FRIEND","COLLEAGUE","STRANGER","ENEMY"]
      }, {
        "name": "address",
        "type": {
            "type": "record",
            "name": "addressRecord",
            "fields": [
                {"name":"number", "type":"int"},
                {"name":"street", "type":"string"},
                {"name":"city", "type":"string"}]
      } ],
      "doc:" : "A basic schema for storing messages"

Create Avro Data File (JSON)

Perform the following steps to create a sample Avro data file conforming to the above schema.

  1. Create a text file named pxf_avro.txt:

    $ vi /tmp/pxf_avro.txt
  2. Enter the following data into pxf_avro.txt:

    {"id":1, "username":"john","followers":["kate", "santosh"], "relationship": "FRIEND", "fmap": {"kate":10,"santosh":4}, "address":{"number":1, "street":"renaissance drive", "city":"san jose"}}
    {"id":2, "username":"jim","followers":["john", "pam"], "relationship": "COLLEAGUE", "fmap": {"john":3,"pam":3}, "address":{"number":9, "street":"deer creek", "city":"palo alto"}}

    The sample data uses a comma (,) to separate top level records and a colon : to separate map/key values and record field name/values.

  3. Convert the text file to Avro format. There are various ways to perform the conversion, both programmatically and via the command line. In this example, we use the Java Avro tools.

    1. Download the most recent version of the Avro tools jar from to the current working directory.

    2. Convert the file:

      $ java -jar ./avro-tools-1.11.0.jar fromjson --schema-file /tmp/avro_schema.avsc /tmp/pxf_avro.txt > /tmp/pxf_avro.avro

      The generated Avro binary data file is written to /tmp/pxf_avro.avro.

  4. Copy the generated Avro file to HDFS:

    $ hdfs dfs -put /tmp/pxf_avro.avro /data/pxf_examples/

Reading Avro Data

Perform the following operations to create and query an external table that references the pxf_avro.avro file that you added to HDFS in the previous section. When creating the table:

  • Use the PXF default server.
  • Map the top-level primitive fields, id (type long) and username (type string), to their equivalent Greenplum Database types (bigint and text).
  • Map the followers field to a text array (text[]).
  • Map the remaining complex fields to type text.
  • Explicitly set the record, map, and collection delimiters using the hdfs:avro profile custom options.

  • Use the hdfs:avro profile to create a queryable external table from the pxf_avro.avro file:

    postgres=# CREATE EXTERNAL TABLE pxf_hdfs_avro(id bigint, username text, followers text[], fmap text, relationship text, address text)
                LOCATION ('pxf://data/pxf_examples/pxf_avro.avro?PROFILE=hdfs:avro&COLLECTION_DELIM=,&MAPKEY_DELIM=:&RECORDKEY_DELIM=:')
              FORMAT 'CUSTOM' (FORMATTER='pxfwritable_import');
  • Perform a simple query of the pxf_hdfs_avro table:

    postgres=# SELECT * FROM pxf_hdfs_avro;
     id | username |   followers    |        fmap         | relationship |                      address                      
      1 | john     | {kate,santosh} | {kate:10,santosh:4} | FRIEND       | {number:1,street:renaissance drive,city:san jose}
      2 | jim      | {john,pam}     | {pam:3,john:3}      | COLLEAGUE    | {number:9,street:deer creek,city:palo alto}
    (2 rows)

    The simple query of the external table shows the components of the complex type data separated with the delimiters specified in the CREATE EXTERNAL TABLE call.

  • Query the table, displaying the id and the first element of the followers text array:

    postgres=# SELECT id, followers[1] FROM pxf_hdfs_avro;
     id | followers 
      1 | kate
      2 | john

Writing Avro Data

The PXF HDFS connector hdfs:avro profile supports writing Avro data to HDFS. When you create a writable external table to write Avro data, you specify the name of a directory on HDFS. When you insert records into the writable external table, the block(s) of data that you insert are written to one or more files in the directory that you specify.

When you create a writable external table to write data to an Avro file, each table row is an Avro record and each table column is an Avro field.

If you do not specify a SCHEMA file, PXF generates a schema for the Avro file based on the Greenplum Database external table definition. PXF assigns the name of the external table column to the Avro field name. Because Avro has a null type and Greenplum external tables do not support the NOT NULL column qualifier, PXF wraps each data type in an Avro union of the mapped type and null. For example, for a writable external table column that you define with the Greenplum Database text data type, PXF generates the following schema element:

["string", "null"]

PXF returns an error if you provide a schema that does not include a union of the field data type with null, and PXF encounters a NULL data field.

PXF supports writing only Avro primitive data types and Avro Arrays of the types identified in Data Type Write Mapping. PXF does not support writing complex types to Avro:

  • When you specify a SCHEMA file in the LOCATION, the schema must include only primitive data types.
  • When PXF generates the schema, it writes any complex type that you specify in the writable external table column definition to the Avro file as a single Avro string type. For example, if you write an array of the Greenplum numeric type, PXF converts the array to a string, and you must read this data with a Greenplum text-type column.

Example: Writing Avro Data

In this example, you create an external table that writes to an Avro file on HDFS, letting PXF generate the Avro schema. After you insert some data into the file, you create a readable external table to query the Avro data.

The Avro file that you create and read in this example includes the following fields:

  • id: int
  • username: text
  • followers: text[]

Example procedure:

  1. Create the writable external table:

    postgres=# CREATE WRITABLE EXTERNAL TABLE pxf_avrowrite(id int, username text, followers text[])
                LOCATION ('pxf://data/pxf_examples/pxfwrite.avro?PROFILE=hdfs:avro')
              FORMAT 'CUSTOM' (FORMATTER='pxfwritable_export');
  2. Insert some data into the pxf_avrowrite table:

    postgres=# INSERT INTO pxf_avrowrite VALUES (33, 'oliver', ARRAY['alex','frank']);
    postgres=# INSERT INTO pxf_avrowrite VALUES (77, 'lisa', ARRAY['tom','mary']);

    PXF uses the external table definition to generate the Avro schema.

  3. Create an external table to read the Avro data that you just inserted into the table:

    postgres=# CREATE EXTERNAL TABLE read_pxfwrite(id int, username text, followers text[])
                LOCATION ('pxf://data/pxf_examples/pxfwrite.avro?PROFILE=hdfs:avro')
              FORMAT 'CUSTOM' (FORMATTER='pxfwritable_import');
  4. Read the Avro data by querying the read_pxfwrite table:

    postgres=# SELECT id, followers, followers[1], followers[2] FROM read_pxfwrite ORDER BY id;
     id |  followers   | followers | followers 
     33 | {alex,frank} | alex      | frank
     77 | {tom,mary}   | tom       | mary
    (2 rows)
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