13.2. Kafka Connector

Overview
Configuration
Configuration Properties
Internal Columns
Table Definition Files
Key and Message in Kafka
Row Decoding

Overview

This connector allows the use of Apache Kafka topics as tables in Presto. Each message is presented as a row in Presto.

Topics can be live: rows will appear as data arrives and disappear as segments get dropped. This can result in strange behavior if accessing the same table multiple times in a single query (e.g., performing a self join).

Note

Apache Kafka 0.8+ is supported although it is highly recommend to use 0.8.1 or later.

Configuration

To configure the Kafka connector, create a catalog properties file ~/.prestoadmin/catalog/kafka.properties with the following contents, replacing the properties as appropriate:

connector.name=kafka
kafka.table-names=table1,table2
kafka.nodes=host1:port,host2:port

Multiple Kafka Clusters

You can have as many catalogs as you need, so if you have additional Kafka clusters, simply add another properties file to ~/.prestoadmin/catalog with a different name (making sure it ends in .properties). For example, if you name the property file sales.properties, Presto will create a catalog named sales using the configured connector.

Configuration Properties

The following configuration properties are available:

Property Name	Description
`kafka.table-names`	List of all tables provided by the catalog
`kafka.default-schema`	Default schema name for tables
`kafka.nodes`	List of nodes in the Kafka cluster
`kafka.connect-timeout`	Timeout for connecting to the Kafka cluster
`kafka.buffer-size`	Kafka read buffer size
`kafka.table-description-dir`	Directory containing topic description files
`kafka.hide-internal-columns`	Controls whether internal columns are part of the table schema or not

`kafka.table-names`

Comma-separated list of all tables provided by this catalog. A table name can be unqualified (simple name) and will be put into the default schema (see below) or qualified with a schema name (<schema-name>.<table-name>).

For each table defined here, a table description file (see below) may exist. If no table description file exists, the table name is used as the topic name on Kafka and no data columns are mapped into the table. The table will still contain all internal columns (see below).

This property is required; there is no default and at least one table must be defined.

`kafka.default-schema`

Defines the schema which will contain all tables that were defined without a qualifying schema name.

This property is optional; the default is default.

`kafka.nodes`

A comma separated list of hostname:port pairs for the Kafka data nodes.

This property is required; there is no default and at least one node must be defined.

Note

Presto must still be able to connect to all nodes of the cluster even if only a subset is specified here as segment files may be located only on a specific node.

`kafka.connect-timeout`

Timeout for connecting to a data node. A busy Kafka cluster may take quite some time before accepting a connection; when seeing failed queries due to timeouts, increasing this value is a good strategy.

This property is optional; the default is 10 seconds (10s).

`kafka.buffer-size`

Size of the internal data buffer for reading data from Kafka. The data buffer must be able to hold at least one message and ideally can hold many messages. There is one data buffer allocated per worker and data node.

This property is optional; the default is 64kb.

`kafka.table-description-dir`

References a folder within Presto deployment that holds one or more JSON files (must end with .json) which contain table description files.

This property is optional; the default is etc/kafka.

`kafka.hide-internal-columns`

In addition to the data columns defined in a table description file, the connector maintains a number of additional columns for each table. If these columns are hidden, they can still be used in queries but do not show up in DESCRIBE <table-name> or SELECT *.

This property is optional; the default is true.

Internal Columns

For each defined table, the connector maintains the following columns:

Column name	Type	Description
`_partition_id`	BIGINT	ID of the Kafka partition which contains this row.
`_partition_offset`	BIGINT	Offset within the Kafka partition for this row.
`_segment_start`	BIGINT	Lowest offset in the segment (inclusive) which contains this row. This offset is partition specific.
`_segment_end`	BIGINT	Highest offset in the segment (exclusive) which contains this row. The offset is partition specific. This is the same value as `_segment_start` of the next segment (if it exists).
`_segment_count`	BIGINT	Running count for the current row within the segment. For an uncompacted topic, `_segment_start + _segment_count` is equal to `_partition_offset`.
`_message_corrupt`	BOOLEAN	True if the decoder could not decode the message for this row. When true, data columns mapped from the message should be treated as invalid.
`_message`	VARCHAR	Message bytes as an UTF-8 encoded string. This is only useful for a text topic.
`_message_length`	BIGINT	Number of bytes in the message.
`_key_corrupt`	BOOLEAN	True if the key decoder could not decode the key for this row. When true, data columns mapped from the key should be treated as invalid.
`_key`	VARCHAR	Key bytes as an UTF-8 encoded string. This is only useful for textual keys.
`_key_length`	BIGINT	Number of bytes in the key.

For tables without a table definition file, the _key_corrupt and _message_corrupt columns will always be false.

Table Definition Files

Kafka maintains topics only as byte messages and leaves it to producers and consumers to define how a message should be interpreted. For Presto, this data must be mapped into columns to allow queries against the data.

Note

For textual topics that contain JSON data, it is entirely possible to not use any table definition files, but instead use the Presto JSON Functions and Operators to parse the _message column which contains the bytes mapped into an UTF-8 string. This is, however, pretty cumbersome and makes it difficult to write SQL queries.

A table definition file consists of a JSON definition for a table. The name of the file can be arbitrary but must end in .json.

{
    "tableName": ...,
    "schemaName": ...,
    "topicName": ...,
    "key": {
        "dataFormat": ...,
        "fields": [
            ...
        ]
    },
    "message": {
        "dataFormat": ...,
        "fields": [
            ...
       ]
    }
}

Field	Required	Type	Description
`tableName`	required	string	Presto table name defined by this file.
`schemaName`	optional	string	Schema which will contain the table. If omitted, the default schema name is used.
`topicName`	required	string	Kafka topic that is mapped.
`key`	optional	JSON object	Field definitions for data columns mapped to the message key.
`message`	optional	JSON object	Field definitions for data columns mapped to the message itself.

Key and Message in Kafka

Starting with Kafka 0.8, each message in a topic can have an optional key. A table definition file contains sections for both key and message to map the data onto table columns.

Each of the key and message fields in the table definition is a JSON object that must contain two fields:

Field	Required	Type	Description
`dataFormat`	required	string	Selects the decoder for this group of fields.
`fields`	required	JSON array	A list of field definitions. Each field definition creates a new column in the Presto table.

Each field definition is a JSON object:

{
    "name": ...,
    "type": ...,
    "dataFormat": ...,
    "mapping": ...,
    "formatHint": ...,
    "hidden": ...,
    "comment": ...
}

Field	Required	Type	Description
`name`	required	string	Name of the column in the Presto table.
`type`	required	string	Presto type of the column.
`dataFormat`	optional	string	Selects the column decoder for this field. Default to the default decoder for this row data format and column type.
`mapping`	optional	string	Mapping information for the column. This is decoder specific, see below.
`formatHint`	optional	string	Sets a column specifc format hint to the column decoder.
`hidden`	optional	boolean	Hides the column from `DESCRIBE <table name>` and `SELECT *`. Defaults to `false`.
`comment`	optional	string	Add a column comment which is shown with `DESCRIBE <table name>`.

There is no limit on field descriptions for either key or message.

Row Decoding

For key and message, a decoder is used to map data onto columns. If no table definition file exists for a table, the dummy decoder is used.

The Kafka connector contains the following decoders:

raw - do not convert the row data, use as raw bytes
csv - interpret the value as CSV
json - convert the value to a JSON object

The main purpose of the decoders is to select the appropriate field decoders to interpret the message or key data.

Presto supports only four physical data types onto which the Presto types are mapped: boolean, long, double and a sequence of bytes which is treated as a string.

`raw` Decoder

The raw decoder supports reading of raw (byte based) values from a message or key and converting it into Presto columns.

For fields, the following attributes are supported:

type - all Presto primitive data types are supported
dataFormat - only _default supported (optional)
mapping - selects the width of the data type converted
formatHint - <start>[:<end>]; start and end position of bytes to convert (optional)

The mapping column selects the number of bytes converted. If absent, BYTE is assumed. All values are signed.

Supported values are:

BYTE - one byte
SHORT - two bytes
INT - four bytes
LONG - eight bytes
FLOAT - four bytes (IEEE 754 format)
DOUBLE - eight bytes (IEEE 754 format)

The type column defines the Presto data type on which the value is mapped.

boolean based types require a mapping to BYTE, SHORT, INT or LONG. Any other type will throw a conversion error. A value of 0 returns false, everything else true.
long based types require a mapping to BYTE, SHORT, INT or LONG. Any other type will throw a conversion error.
double based types require a mapping to FLOAT or DOUBLE. Any other type will throw a conversion error.
string based types require a mapping to BYTE. Any other type will throw a conversion error.

The formatHint field specifies the position of the bytes in a key or message. It can be one or two numbers separated by a colon (<start>[:<end>]). If only a start position is given, the column will use the appropriate number of bytes for the type (see above). string based types (VARCHAR) will use all bytes to the end of the message. If start and end position is given, then for fixed with types the size must be at least the size of the type. For string based types, all bytes between start (inclusive) and end (exclusive) are used.

`csv` Decoder

Note

The CSV decoder is of beta quality and should be used with caution.

The CSV decoder converts the bytes representing a message or key into a string using UTF-8 encoding and then interprets the result as a CSV (comma-separated value) line.

For fields, the following attributes are supported:

type - all Presto primitive data types are supported
dataFormat - only _default supported (optional)
mapping - field index used for the column (required)
formatHint - not supported, ignored
boolean based types return true if the field value is the string “true” (case insensitive), false otherwise.
long and double based types parse the field value according to Java long and double parse rules.
string types use the field as-is (text using UTF-8 encoding)

`json` Decoder

The JSON decoder converts the bytes representing a message or key into a JSON according to RFC 4627. Note that the message or key MUST convert into a JSON object, not an array or simple type.

For fields, the following attributes are supported:

type - all Presto primitive data types are supported
dataFormat - _default, custom-date-time, iso8601, rfc2822, milliseconds-since-epoch, seconds-since-epoch. If missing, _default is used.
mapping - slash-separated list of field names to select a field from the JSON object
formatHint - only for custom-date-time, see below

The JSON decoder supports multiple field decoders, with _default being used for standard table columns and a number of decoders for date and time based types.

`_default` Field decoder

This is the standard field decoder supporting all the Presto physical data types. A field value will be coerced by JSON conversion rules into boolean, long, double or string values. For non-date/time based columns, this decoder should be used.

Date and Time Decoders

To convert values from JSON objects into Presto DATE, TIME or TIMESTAMP columns, special decoders can be selected using the dataFormat attribute of a field definition.

Text Decoders

iso8601 - text based, parses a text field as an ISO 8601 timestamp.
rfc2822 - text based, parses a text field as an RFC 2822 timestamp.
custom-date-time - text based, a formatting hint is required which is parsed as a Joda-Time formatting string.

Presto Type	JSON Text	JSON Long
string type	as-is	parse according to format type, return millis since epoch
long-based type	parse according to format type, return millis since epoch	return as millis since epoch

Number Decoders

milliseconds-since-epoch - number based, interprets a text or number as number of milliseconds since the epoch.
seconds-since-epoch - number based, interprets a text or number as number of milliseconds since the epoch.

Presto Type	JSON Text	JSON Long
string type	parse as long, format as ISO8601	format as ISO8601
long-based type	parse as long, return millis since epoch	return millis since epoch

13.2. Kafka Connector

Overview

Configuration

Multiple Kafka Clusters

Configuration Properties

kafka.table-names

kafka.default-schema

kafka.nodes

kafka.connect-timeout

kafka.buffer-size

kafka.table-description-dir

kafka.hide-internal-columns

Internal Columns

Table Definition Files

Key and Message in Kafka

Row Decoding

raw Decoder

csv Decoder

json Decoder

_default Field decoder

Date and Time Decoders

Text Decoders

Number Decoders

`kafka.table-names`

`kafka.default-schema`

`kafka.nodes`

`kafka.connect-timeout`

`kafka.buffer-size`

`kafka.table-description-dir`

`kafka.hide-internal-columns`

`raw` Decoder

`csv` Decoder

`json` Decoder

`_default` Field decoder