13.2. Kafka Connector
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 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 bytescsv
- interpret the value as CSVjson
- 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 supporteddataFormat
- only_default
supported (optional)mapping
- selects the width of the data type convertedformatHint
-<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 byteSHORT
- two bytesINT
- four bytesLONG
- eight bytesFLOAT
- 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
orLONG
. Any other type will throw a conversion error. A value of0
returns false, everything else true. - long based types require a mapping to
BYTE
,SHORT
,INT
orLONG
. Any other type will throw a conversion error. - double based types require a mapping to
FLOAT
orDOUBLE
. 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 supporteddataFormat
- 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 supporteddataFormat
-_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 objectformatHint
- only forcustom-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 |