
Kafka Connector
===============

Overview
--------

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

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**

*The minimum supported Kafka broker version is 0.10.0.*

Configuration
-------------

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

``` properties
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`, openLooKeng 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**

*openLooKeng 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 openLooKeng 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 openLooKeng, 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 openLooKeng `/functions/json` 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`.

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

  

| Field        | Required | Type        | Description                                                  |
| :----------- | :------- | :---------- | :----------------------------------------------------------- |
| `tableName`  | required | string      | openLooKeng 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 openLooKeng table. |

Each field definition is a JSON object:

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

  

| Field        | Required | Type    | Description                                                  |
| :----------- | :------- | :------ | :----------------------------------------------------------- |
| `name`       | required | string  | Name of the column in the openLooKeng table.                        |
| `type`       | required | string  | openLooKeng type of the column.                                     |
| `dataFormat` | optional | string  | Selects the column decoder for this field. Defaults to the default decoder for this row data format and column type. |
| `dataSchema` | optional | string  | The path or URL where the Avro schema resides. Used only for Avro decoder. |
| `mapping`    | optional | string  | Mapping information for the column. This is decoder specific, see below. |
| `formatHint` | optional | string  | Sets a column specific format hint to the column decoder.    |
| `hidden`     | optional | boolean | Hides the column from `DESCRIBE ` and `SELECT *`. Defaults to `false`. |
| `comment`    | optional | string  | Adds a column comment which is shown with `DESCRIBE `.       |

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

Row Decoding
------------

For key and message, a decoder is used to map message and key data onto
table columns.

The Kafka connector contains the following decoders:

-   `raw` - Kafka message is not interpreted, ranges of raw message bytes are mapped to table columns
-   `csv` - Kafka message is interpreted as comma separated message, and fields are mapped to table columns
-   `json` - Kafka message is parsed as JSON and JSON fields are mapped to table columns
-   `avro` - Kafka message is parsed based on an Avro schema and Avro fields are mapped to table columns

**Note**

If no table definition file exists for a table, the `dummy` decoder is used, which does not expose any columns.


### `raw` Decoder

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

For fields, the following attributes are supported:

-   `dataFormat` - selects the width of the data type converted
-   `type` - openLooKeng data type (see table below for list of supported data types)
-   `mapping` - `<start>[:<end>]`; start and end position of bytes to convert (optional)

The `dataFormat` attribute selects the number of bytes converted. If absent, `BYTE` is assumed. All values are signed.

Supported values are:

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

The `type` attribute defines the openLooKeng data type on which the value is mapped.

Depending on openLooKeng type assigned to column different values of dataFormat can be used:

| openLooKeng data type           | Allowed `dataFormat` values    |
| :----------------------- | :----------------------------- |
| `BIGINT`                 | `BYTE`, `SHORT`, `INT`, `LONG` |
| `INTEGER`                | `BYTE`, `SHORT`, `INT`         |
| `SMALLINT`               | `BYTE`, `SHORT`                |
| `TINYINT`                | `BYTE`                         |
| `DOUBLE`                 | `DOUBLE`, `FLOAT`              |
| `BOOLEAN`                | `BYTE`, `SHORT`, `INT`, `LONG` |
| `VARCHAR` / `VARCHAR(x)` | `BYTE`                         |

The `mapping` attribute specifies the range of the bytes in a key or message used for decoding. It can be one or two numbers separated by a colon (`<start>[:<end>]`).

If only a start position is given:

> -   For fixed width types the column will use the appropriate number  of bytes for the specified `dateFormat` (see above).
>    -   When `VARCHAR` value is decoded all bytes from start position till  the end of the message will be used.

If start and end position are given, then:

> -   For fixed width types the size must be equal to number of bytes  used by specified `dataFormat`.
>    -   For `VARCHAR` all bytes between start (inclusive) and end  (exclusive) are used.

If no `mapping` attribute is specified it is equivalent to setting start position to 0 and leaving end position undefined.

Decoding scheme of numeric data types (`BIGINT`, `INTEGER`, `SMALLINT`, `TINYINT`, `DOUBLE`) is straightforward. A sequence of bytes is read from input message and decoded according to either:

> -   big-endian encoding (for integer types)
> -   IEEE 754 format for (for `DOUBLE`).

Length of decoded byte sequence is implied by the `dataFormat`. For `VARCHAR` data type a sequence of bytes is interpreted according to UTF-8 encoding.

### `csv` Decoder

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 `type` and `mapping` attributes must be defined:

-   `type` - openLooKeng data type (see table below for list of supported data types)
-   `mapping` - the index of the field in the CSV record

`dataFormat` and `formatHint` are not supported and must be omitted. Table below lists supported openLooKeng types which can be used in `type` and decoding scheme:

| openLooKeng data type                          | Decoding rules                                               |
| :-------------------------------------- | :----------------------------------------------------------- |
| `BIGINT` `INTEGER` `SMALLINT` `TINYINT` | Decoded using Java `Long.parseLong()`                        |
| `DOUBLE`                                | Decoded using Java `Double.parseDouble()`                    |
| `BOOLEAN`                               | “true” character sequence maps to `true`; Other character sequences map to `false` |
| `VARCHAR` / `VARCHAR(x)`                | Used as is                                                   |



### `json` Decoder

The JSON decoder converts the bytes representing a message or key into a JSON according to `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` - openLooKeng type of column.
-   `dataFormat` - Field decoder to be used for column.
-   `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.

Table below lists openLooKeng data types which can be used as in `type` and matching field decoders which can be specified via `dataFormat`attribute

| openLooKeng data type                                               | Allowed `dataFormat` values                                  |
| :----------------------------------------------------------- | :----------------------------------------------------------- |
| `BIGINT` `INTEGER` `SMALLINT` `TINYINT` `DOUBLE` `BOOLEAN` `VARCHAR` `VARCHAR(x)` | Default field decoder (omitted `dataFormat` attribute)       |
| `TIMESTAMP` `TIMESTAMP WITH TIME ZONE` `TIME` `TIME WITH TIME ZONE` | `custom-date-time`, `iso8601`, `rfc2822`, `milliseconds-since-epoch`, `seconds-since-epoch` |
| `DATE`                                                       | `custom-date-time`, `iso8601`, `rfc2822`,                    |



### Default Field decoder

This is the standard field decoder supporting all the openLooKeng 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 openLooKeng `DATE`, `TIME`, `TIME WITH TIME ZONE`, `TIMESTAMP` or `TIMESTAMP WITH TIME ZONE` columns, special decoders must be selected using the `dataFormat`
attribute of a field definition.

-   `iso8601` - text based, parses a text field as an ISO 8601 timestamp.
    
-   `rfc2822` - text based, parses a text field as an `2822` timestamp.
    
-   `custom-date-time` - text based, parses a text field according to Joda format pattern specified via `formatHint` attribute. Format pattern should conform to
    <https://www.joda.org/joda-time/apidocs/org/joda/time/format/DateTimeFormat.html>.
    
-   `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.

For `TIMESTAMP WITH TIME ZONE` and `TIME WITH TIME ZONE` data types, if timezone information is present in decoded value, it will be used in openLooKeng value. Otherwise result time zone will be set to `UTC`.

### `avro` Decoder

The Avro decoder converts the bytes representing a message or key in Avro format based on a schema. The message must have the Avro schema embedded. openLooKeng does not support schema-less Avro decoding.

For key/message, using `avro` decoder, the `dataSchema` must be defined. This should point to the location of a valid Avro schema file of the message which needs to be decoded. This location can be a remote web
server (e.g.: `dataSchema: 'http://example.org/schema/avro_data.avsc'`) or local file system(e.g.:
`dataSchema: '/usr/local/schema/avro_data.avsc'`). The decoder will fail if this location is not accessible from the openLooKeng coordinator node.

For fields, the following attributes are supported:

-   `name` - Name of the column in the openLooKeng table.
-   `type` - openLooKeng type of column.
-   `mapping` - slash-separated list of field names to select a field
    from the Avro schema. If field specified in `mapping` does not exist
    in the original Avro schema then a read operation will return NULL.

Table below lists supported openLooKeng types which can be used in `type` for the equivalent Avro field type/s. 

| openLooKeng data type           | Allowed Avro data type |
| :----------------------- | :--------------------- |
| `BIGINT`                 | `INT`, `LONG`          |
| `DOUBLE`                 | `DOUBLE`, `FLOAT`      |
| `BOOLEAN`                | `BOOLEAN`              |
| `VARCHAR` / `VARCHAR(x)` | `STRING`               |
| `VARBINARY`              | `FIXED`, `BYTES`       |
| `ARRAY`                  | `ARRAY`                |
| `MAP`                    | `MAP`                  |



#### Avro schema evolution

The Avro decoder supports schema evolution feature with backward compatibility. With backward compatibility, a newer schema can be used to read Avro data created with an older schema. Any change in the Avro schema must also be reflected in openLooKeng\'s topic definition file. Newly added/renamed fields *must* have a default value in the Avro schema file.

The schema evolution behavior is as follows:

-   Column added in new schema: Data created with an older schema will produce a *default* value when table is using the new schema.
-   Column removed in new schema: Data created with an older schema will no longer output the data from the column that was removed.
-   Column is renamed in the new schema: This is equivalent to removing the column and adding a new one, and data created with an older schema will produce a *default* value when table is using the new
    schema.
-   Changing type of column in the new schema: If the type coercion is supported by Avro, then the conversion happens. An error is thrown for incompatible types.
