Presto 0.100 Documentation

4.4. Kafka连接器

4.4. Kafka连接器

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 etc/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 etc/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 specifc 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 of 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 decode 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函数 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 data types are supported
  • dataFormat - Only _default supported, can be omitted.
  • mapping - selects the width of the data type converted
  • formatHint - optional, <start>[:<end>]; start and end position of bytes to convert

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 data types are supported
  • dataFormat - Only _default supported, can be omitted
  • mapping - field index used for the column. Must be given
  • 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 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 fields 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