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Use PXF object store connector to read and write Parquet-format data between Greengage DB and S3

Anton Monakov

Apache Parquet is an open-source column-oriented data storage format used in the Apache Hadoop ecosystem. A Parquet data file contains a compact binary representation of the data and includes an embedded schema. The schema defines the structure of the data and is composed of the primitive and complex types listed in Data type mapping.

This topic describes how to configure and use the PXF object store connector for reading and writing Parquet data in an object store by using external tables and provides practical examples.

Data type mapping

To read and write Parquet primitive data types in Greengage DB, map Parquet data values to Greengage DB columns of the same type.

Parquet supports a small set of primitive data types and uses metadata annotations that specify how to interpret a primitive type and extend it into a logical type. For example, Parquet stores both the INTEGER and the DATE types as the INT32 primitive type, while an annotation identifies the original type as DATE.

Read mapping

PXF uses the following data type mapping when reading Parquet data.

Parquet physical type Parquet logical type PXF / Greengage DB data type

boolean

 — 

BOOLEAN

binary (byte_array)

 — 

BYTEA

binary (byte_array)

Date

DATE

binary (byte_array)

Timestamp_millis

TIMESTAMP

binary (byte_array)

UTF8

TEXT

double

 — 

FLOAT8

fixed_len_byte_array

Decimal

NUMERIC

float

 — 

REAL

int32

int_8

SMALLINT

int32

Date

DATE

int32

Decimal

NUMERIC

int32

 — 

INTEGER

int64

Decimal

NUMERIC

int64

 — 

BIGINT

int96

 — 

TIMESTAMP
NOTE

PXF supports filter predicate pushdown on all parquet data types listed above, except the fixed_len_byte_array and int96 types.

PXF can read a Parquet LIST nested type when it represents a one-dimensional array of the following Parquet types.

Parquet data type PXF / Greengage DB data type

List of <boolean>

BOOLEAN[]

List of <binary>

BYTEA[]

List of <binary> (Date)

DATE[]

List of <binary> (Timestamp_millis)

TIMESTAMP[]

List of <binary> (UTF8)

TEXT[]

List of <double>

FLOAT8[]

List of <fixed_len_byte_array> (Decimal)

NUMERIC[]

List of <float>

REAL[]

List of <int32> (int_8)

SMALLINT[]

List of <int32> (Date)

DATE[]

List of <int32> (Decimal)

NUMERIC[]

List of <int32>

INTEGER[]

List of <int64> (Decimal)

NUMERIC[]

List of <int64>

BIGINT[]

List of <int96>

TIMESTAMP[]

Write mapping

PXF uses the following data type mapping when writing Parquet data.

PXF / Greengage DB data type Parquet physical type Parquet logical type

BIGINT

int64

 — 

BOOLEAN

boolean

 — 

BPCHAR (1)

binary (byte_array)

UTF8

BYTEA

binary (byte_array)

 — 

DATE

int32

Date

FLOAT8

double

 — 

INTEGER

int32

 — 

NUMERIC / DECIMAL

fixed_len_byte_array

Decimal

REAL

float

 — 

SMALLINT

int32

int_8

TEXT

binary (byte_array)

UTF8

TIMESTAMP (2)

int96

 — 

TIMESTAMPTZ (3)

int96

 — 

VARCHAR

binary (byte_array)

UTF8

Others

Unsupported

 — 

PXF can write a one-dimensional LIST array of the following Parquet data types.

PXF / Greengage DB data type Parquet data type

BIGINT[]

List of <int64>

BOOLEAN[]

List of <boolean>

BPCHAR[] (1)

List of <binary> (UTF8)

BYTEA[]

List of <binary>

DATE[]

List of <int32> (Date)

FLOAT8[]

List of <double>

INTEGER[]

List of <int32>

NUMERIC[] / DECIMAL[]

List of <fixed_len_byte_array> (Decimal)

REAL[]

List of <float>

SMALLINT[]

List of <int32> (int_8)

TEXT[]

List of <binary> (UTF8)

TIMESTAMP[] (2)

List of <int96>

TIMESTAMPTZ[] (3)

List of <int96>

VARCHAR[]

List of <binary> (UTF8)

Others

Unsupported

  1. Parquet does not save the field length, so a BPCHAR value written to Parquet will be a text of undefined length.

  2. PXF localizes the TIMESTAMP type to the current system time zone and converts it to universal time (UTC) before finally converting to int96.

  3. PXF converts the TIMESTAMPTZ type to a UTC timestamp and then converts to int96. The time zone information is lost during this conversion.

Create an external table using the PXF protocol

To create a Greengage DB external table to read or write Parquet data in an object store, use the following general syntax:

CREATE [READABLE | WRITABLE] EXTERNAL TABLE <table_name>
    ( <column_name> <data_type> [, ...] | LIKE <other_table> )
    LOCATION ('pxf://<path_to_data>?PROFILE=<objstore>:parquet[&<custom_option>=<value>[...]]')
    FORMAT 'CUSTOM' (FORMATTER='pxfwritable_import'|'pxfwritable_export')
    [DISTRIBUTED BY (<column_name> [, ... ] ) | DISTRIBUTED RANDOMLY];
Keyword Value

<table_name>

The name of the table to create

<column_name>

The name of the column to create

<data_type>

The data type of the column

LIKE <other_table>

Specifies a table from which the new external table automatically copies all column names, data types, and distribution policy

<path_to_data>

The path to the directory or file in an object store. When the <server_name> configuration includes a pxf.fs.basePath property setting, the <path_to_data> value is considered to be relative to the base path specified. Otherwise, the path is considered absolute. The value must not include the dollar sign ($) character

PROFILE=<objstore>:parquet

The profile is specified as the <objstore>:parquet pair, where <objstore> is the object store prefix.

The following <objstore> prefixes and the corresponding object stores are supported:

  • wasbs — Azure Blob Storage

  • adl — Azure Data Lake

  • gs — Google Cloud Storage

  • s3 — Amazon S3, MinIO, and other S3-compatible object stores

FORMAT 'CUSTOM'

The custom format with the built-in custom formatter functions for read (pxfwritable_import) and write (pxfwritable_export) operations is used to work with Parquet data in an object store

DISTRIBUTED BY

When loading data from a Greengage DB table into a writable external table, consider specifying the same distribution policy or column name on both tables. This will avoid extra motion of data between segments on the load operation. Learn more about table distribution in Distribution

<custom_option>

One of the custom options provided in the LOCATION string as described below

SERVER=<server_name>

The named server configuration that PXF uses to access the data. If the option is omitted, the default server configuration is used

IGNORE_MISSING_PATH

The action to take when <path_to_data> is missing or invalid. If set to false (default), an error is returned. If set to true, PXF ignores missing path errors and returns an empty fragment. Only applies to readable external tables and is ignored in the case of writable external tables

COMPRESSION_CODEC

The compression codec to use when writing data: snappy, gzip, lzo, or uncompressed (no compression).

You must explicitly specify uncompressed if you do not want PXF to compress the data. Otherwise, PXF compresses the data using Snappy compression

ROWGROUP_SIZE

The size (in bytes) of the row group, which provides a logical partitioning of the data into rows. The default row group size is 8 * 1024 * 1024 bytes

PAGE_SIZE

The size (in bytes) of a page, which divides row groups in a column into column chunks. The default page size is 1 * 1024 * 1024 bytes

ENABLE_DICTIONARY

Specifies whether to enable dictionary encoding. The default value is true; dictionary encoding is enabled when writing Parquet files

DICTIONARY_PAGE_SIZE

When dictionary encoding is enabled, defines a single dictionary page per column, per row group. DICTIONARY_PAGE_SIZE is similar to PAGE_SIZE, but is specified for the dictionary. The default dictionary page size is 1 * 1024 * 1024 bytes

PARQUET_VERSION

The Parquet version; the supported values are v1 (default) and v2

SCHEMA

The absolute path to the Parquet schema file on the Greengage DB host or in an object store. If not specified, PXF creates the schema based on the external table definition

Numeric data overflow conditions

PXF uses the HiveDecimal class to write numeric Parquet data, which limits both the precision and the scale of a numeric type to a maximum of 38. When you define a NUMERIC column in an external table without specifying a precision or scale, PXF internally maps the column to DECIMAL(38, 18).

PXF handles the following precision overflow conditions:

  • A NUMERIC column is defined in the external table, and the total digit count of a value exceeds the maximum supported precision of 38, for example, 1234567890123456789012345678901234567890.12345, which has a total digit count of 45.

  • A NUMERIC(<precision>) column is defined with a <precision> value greater than 38, for example NUMERIC(55).

  • A NUMERIC column is defined in the external table, and the integer digit count of a value is greater than 20 (38-18), for example, 123456789012345678901234567890.12345, which has an integer digit count of 30.

If you define a NUMERIC(<precision>, <scale>) column and the integer digit count of a value is greater than <precision>-<scale>, PXF returns an error. For example, you define a NUMERIC(20,4) column and the value is 12345678901234567.12, whose integer digit count of 17 is greater than 20-4=16.

PXF can perform one of the following actions when detecting a numeric data overflow: round the value (the default), return an error, or ignore the overflow. The pxf.parquet.write.decimal.overflow property in the pxf-site.xml server configuration file specifies the action to take.

Value PXF action

round

The default behavior. When PXF encounters an overflow, it attempts to round the value to meet both precision and scale requirements before writing and reports an error if rounding fails. This may potentially leave an incomplete dataset in the external system

error

PXF reports an error when it encounters an overflow, and the transaction fails

ignore

PXF logs a warning and attempts to round the value to meet both precision and scale requirements; otherwise a NULL value is written
NOTE

Learn about configuring a PXF server in Configure a PXF server in PXF documentation.

Examples

These examples demonstrate how to configure and use the PXF object store connector for reading and writing Parquet data in an object store by using external tables.

Prerequisites

To try out the practical examples, connect to the Greengage DB master host as gpadmin using psql as described in Connect to Greengage DB via psql. Then create the customers test database and connect to it:

DROP DATABASE IF EXISTS customers;
CREATE DATABASE customers;
\c customers

To be able to create an external table using the PXF protocol, enable the PXF extension in the database as described in Register PXF in a database in PXF documentation:

CREATE EXTENSION pxf;

Configure the PXF S3 connector

To have PXF connect to an object store, you need to create the corresponding server configuration and then synchronize it to the Greengage DB cluster:

  1. On the Greengage DB master host, log in as gpadmin.

  2. Go to the $PXF_BASE/servers directory and create an S3 server configuration directory named s3. Depending on your object storage, copy the required server configuration file from $PXF_HOME/templates to $PXF_BASE/servers/s3. The example uses the configuration file based on the minio-site.xml template.

    $ mkdir $PXF_BASE/servers/s3
$ cd $PXF_BASE/servers/s3
$ cp $PXF_HOME/templates/minio-site.xml .

    In the configuration file, provide the relevant object store connection details:

    <?xml version="1.0" encoding="UTF-8"?>
<configuration>
    <property>
        <name>fs.s3a.endpoint</name>
        <value>storage.example.com</value>
    </property>
    <property>
        <name>fs.s3a.access.key</name>
        <value>${ACCESS_KEY}</value>
    </property>
    <property>
        <name>fs.s3a.secret.key</name>
        <value>${SECRET_KEY}</value>
    </property>
    <property>
        <name>fs.s3a.fast.upload</name>
        <value>true</value>
    </property>
    <property>
        <name>fs.s3a.path.style.access</name>
        <value>true</value>
    </property>
</configuration>
    NOTE

    Notice that the credentials used to authenticate with the storage service are provided via the ACCESS_KEY and SECRET_KEY environment variables.

    You can configure them as follows:

    $ export ACCESS_KEY=<access_key>
$ export SECRET_KEY=<secret_key>

    While it’s possible to set the credentials directly in the configuration file, it is recommended to use environment variables for security reasons.

  3. Synchronize the server configuration to the Greengage DB cluster hosts:

    $ pxf cluster sync

Create a writable external table

  1. On the Greengage DB master host, create the writable external table that stores data to the customers bucket on the S3 host. In the LOCATION clause, specify the PXF s3:parquet profile and the server configuration. In the FORMAT clause, specify pxfwritable_export, which is the built-in custom formatter function for write operations:

    CREATE WRITABLE EXTERNAL TABLE customers_w (
        id INT,
        name TEXT, 
        ordered_items TEXT[]    
    )
    LOCATION ('pxf://customers?PROFILE=s3:parquet&SERVER=s3')
    FORMAT 'CUSTOM' (FORMATTER='pxfwritable_export');

  2. Populate the customers_w table with sample data:

    INSERT INTO customers_w 
VALUES (1, 'John Doe', ARRAY['laptop', 'monitor']),
       (2, 'Jane Smith', ARRAY['keyboard', 'mouse', 'pad']),
       (3, 'Bob Brown', ARRAY['headphones']),
       (4, 'Alice Green', ARRAY['webcam', 'microphone']);

  3. In the customers bucket on the S3 host, view the created files. The set of files should look similar to the following:

    202-0000000013_0.snappy.parquet
202-0000000013_3.snappy.parquet

Create a readable external table

  1. On the Greengage DB master host, create an external table that reads files from the customers bucket on the S3 host. In the LOCATION clause, specify the PXF s3:parquet profile and the server configuration. In the FORMAT clause, specify pxfwritable_import, which is the built-in custom formatter function for read operations:

    CREATE EXTERNAL TABLE customers_r (
        id INT,
        name TEXT, 
        ordered_items TEXT[]
    )
    LOCATION('pxf://customers?PROFILE=s3:parquet&SERVER=s3')
    FORMAT 'CUSTOM' (FORMATTER='pxfwritable_import');

  2. Query the created external table:

    SELECT * FROM customers_r;

    The output should look as follows:

     id |    name     |    ordered_items
----+-------------+----------------------
  1 | John Doe    | {laptop,monitor}
  3 | Bob Brown   | {headphones}
  4 | Alice Green | {webcam,microphone}
  2 | Jane Smith  | {keyboard,mouse,pad}
(4 rows)
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