
Kafka Connector Tutorial
========================

Introduction
------------

The Kafka Connector for openLooKeng allows access to live topic data from Apache Kafka using openLooKeng. This tutorial shows how to set up topics and how to create the topic description files that back openLooKeng tables.

Installation
------------

This tutorial assumes familiarity with openLooKeng and a working local openLooKeng installation (see [Deploying openLooKeng Manually](../installation/deployment.md). It will focus on setting up Apache Kafka and integrating it with openLooKeng.

### Step 1: Install Apache Kafka

Download and extract [Apache Kafka](https://kafka.apache.org/).


**Note**

*This tutorial was tested with Apache Kafka 0.8.1. It should work with* *any 0.8.x version of Apache Kafka.*

Start ZooKeeper and the Kafka server:

``` shell
$ bin/zookeeper-server-start.sh config/zookeeper.properties
[2013-04-22 15:01:37,495] INFO Reading configuration from: config/zookeeper.properties (org.apache.zookeeper.server.quorum.QuorumPeerConfig)
...
```

``` shell
$ bin/kafka-server-start.sh config/server.properties
[2013-04-22 15:01:47,028] INFO Verifying properties (kafka.utils.VerifiableProperties)
[2013-04-22 15:01:47,051] INFO Property socket.send.buffer.bytes is overridden to 1048576 (kafka.utils.VerifiableProperties)
...
```

This will start Zookeeper on port `2181` and Kafka on port `9092`.

### Step 2: Load data

Download the tpch-kafka loader from Maven central:

``` shell
$ curl -o kafka-tpch https://repo1.maven.org/maven2/de/softwareforge/kafka_tpch_0811/1.0/kafka_tpch_0811-1.0.sh
$ chmod 755 kafka-tpch
```

Now run the `kafka-tpch` program to preload a number of topics with tpch data:

``` shell
$ ./kafka-tpch load --brokers localhost:9092 --prefix tpch. --tpch-type tiny
2014-07-28T17:17:07.594-0700     INFO    main    io.airlift.log.Logging    Logging to stderr
2014-07-28T17:17:07.623-0700     INFO    main    de.softwareforge.kafka.LoadCommand    Processing tables: [customer, orders, lineitem, part, partsupp, supplier, nation, region]
2014-07-28T17:17:07.981-0700     INFO    pool-1-thread-1    de.softwareforge.kafka.LoadCommand    Loading table 'customer' into topic 'tpch.customer'...
2014-07-28T17:17:07.981-0700     INFO    pool-1-thread-2    de.softwareforge.kafka.LoadCommand    Loading table 'orders' into topic 'tpch.orders'...
2014-07-28T17:17:07.981-0700     INFO    pool-1-thread-3    de.softwareforge.kafka.LoadCommand    Loading table 'lineitem' into topic 'tpch.lineitem'...
2014-07-28T17:17:07.982-0700     INFO    pool-1-thread-4    de.softwareforge.kafka.LoadCommand    Loading table 'part' into topic 'tpch.part'...
2014-07-28T17:17:07.982-0700     INFO    pool-1-thread-5    de.softwareforge.kafka.LoadCommand    Loading table 'partsupp' into topic 'tpch.partsupp'...
2014-07-28T17:17:07.982-0700     INFO    pool-1-thread-6    de.softwareforge.kafka.LoadCommand    Loading table 'supplier' into topic 'tpch.supplier'...
2014-07-28T17:17:07.982-0700     INFO    pool-1-thread-7    de.softwareforge.kafka.LoadCommand    Loading table 'nation' into topic 'tpch.nation'...
2014-07-28T17:17:07.982-0700     INFO    pool-1-thread-8    de.softwareforge.kafka.LoadCommand    Loading table 'region' into topic 'tpch.region'...
2014-07-28T17:17:10.612-0700    ERROR    pool-1-thread-8    kafka.producer.async.DefaultEventHandler    Failed to collate messages by topic, partition due to: Failed to fetch topic metadata for topic: tpch.region
2014-07-28T17:17:10.781-0700     INFO    pool-1-thread-8    de.softwareforge.kafka.LoadCommand    Generated 5 rows for table 'region'.
2014-07-28T17:17:10.797-0700    ERROR    pool-1-thread-3    kafka.producer.async.DefaultEventHandler    Failed to collate messages by topic, partition due to: Failed to fetch topic metadata for topic: tpch.lineitem
2014-07-28T17:17:10.932-0700    ERROR    pool-1-thread-1    kafka.producer.async.DefaultEventHandler    Failed to collate messages by topic, partition due to: Failed to fetch topic metadata for topic: tpch.customer
2014-07-28T17:17:11.068-0700    ERROR    pool-1-thread-2    kafka.producer.async.DefaultEventHandler    Failed to collate messages by topic, partition due to: Failed to fetch topic metadata for topic: tpch.orders
2014-07-28T17:17:11.200-0700    ERROR    pool-1-thread-6    kafka.producer.async.DefaultEventHandler    Failed to collate messages by topic, partition due to: Failed to fetch topic metadata for topic: tpch.supplier
2014-07-28T17:17:11.319-0700     INFO    pool-1-thread-6    de.softwareforge.kafka.LoadCommand    Generated 100 rows for table 'supplier'.
2014-07-28T17:17:11.333-0700    ERROR    pool-1-thread-4    kafka.producer.async.DefaultEventHandler    Failed to collate messages by topic, partition due to: Failed to fetch topic metadata for topic: tpch.part
2014-07-28T17:17:11.466-0700    ERROR    pool-1-thread-5    kafka.producer.async.DefaultEventHandler    Failed to collate messages by topic, partition due to: Failed to fetch topic metadata for topic: tpch.partsupp
2014-07-28T17:17:11.597-0700    ERROR    pool-1-thread-7    kafka.producer.async.DefaultEventHandler    Failed to collate messages by topic, partition due to: Failed to fetch topic metadata for topic: tpch.nation
2014-07-28T17:17:11.706-0700     INFO    pool-1-thread-7    de.softwareforge.kafka.LoadCommand    Generated 25 rows for table 'nation'.
2014-07-28T17:17:12.180-0700     INFO    pool-1-thread-1    de.softwareforge.kafka.LoadCommand    Generated 1500 rows for table 'customer'.
2014-07-28T17:17:12.251-0700     INFO    pool-1-thread-4    de.softwareforge.kafka.LoadCommand    Generated 2000 rows for table 'part'.
2014-07-28T17:17:12.905-0700     INFO    pool-1-thread-2    de.softwareforge.kafka.LoadCommand    Generated 15000 rows for table 'orders'.
2014-07-28T17:17:12.919-0700     INFO    pool-1-thread-5    de.softwareforge.kafka.LoadCommand    Generated 8000 rows for table 'partsupp'.
2014-07-28T17:17:13.877-0700     INFO    pool-1-thread-3    de.softwareforge.kafka.LoadCommand    Generated 60175 rows for table 'lineitem'.
```

Kafka now has a number of topics that are preloaded with data to query.

### Step 3: Make the Kafka topics known to openLooKeng

In your openLooKeng installation, add a catalog properties file `etc/catalog/kafka.properties` for the Kafka connector. This file lists the Kafka nodes and topics:

``` properties
connector.name=kafka
kafka.nodes=localhost:9092
kafka.table-names=tpch.customer,tpch.orders,tpch.lineitem,tpch.part,tpch.partsupp,tpch.supplier,tpch.nation,tpch.region
kafka.hide-internal-columns=false
```

Now start openLooKeng:

``` shell
$ bin/launcher start
```

Because the Kafka tables all have the `tpch.` prefix in the configuration, the tables are in the `tpch` schema. The connector is mounted into the `kafka` catalog because the properties file is named `kafka.properties`.

Start the [openLooKeng CLI](../installation/cli.md):

``` shell
$ ./openlk-cli --catalog kafka --schema tpch
```

List the tables to verify that things are working:

``` sql
lk:tpch> SHOW TABLES;
  Table
----------
 customer
 lineitem
 nation
 orders
 part
 partsupp
 region
 supplier
(8 rows)
```

### Step 4: Basic data querying

Kafka data is unstructured and it has no metadata to describe the format of the messages. Without further configuration, the Kafka connector can access the data and map it in raw form but there are no actual columns besides the built-in ones:

``` sql
lk:tpch> DESCRIBE customer;
      Column       |  Type   | Extra |                   Comment
-------------------+---------+-------+---------------------------------------------
 _partition_id     | bigint  |       | Partition Id
 _partition_offset | bigint  |       | Offset for the message within the partition
 _segment_start    | bigint  |       | Segment start offset
 _segment_end      | bigint  |       | Segment end offset
 _segment_count    | bigint  |       | Running message count per segment
 _key              | varchar |       | Key text
 _key_corrupt      | boolean |       | Key data is corrupt
 _key_length       | bigint  |       | Total number of key bytes
 _message          | varchar |       | Message text
 _message_corrupt  | boolean |       | Message data is corrupt
 _message_length   | bigint  |       | Total number of message bytes
(11 rows)

lk:tpch> SELECT count(*) FROM customer;
 _col0
-------
  1500

lk:tpch> SELECT _message FROM customer LIMIT 5;
                                                                                                                                                 _message
--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
 {"rowNumber":1,"customerKey":1,"name":"Customer#000000001","address":"IVhzIApeRb ot,c,E","nationKey":15,"phone":"25-989-741-2988","accountBalance":711.56,"marketSegment":"BUILDING","comment":"to the even, regular platelets. regular, ironic epitaphs nag e"}
 {"rowNumber":3,"customerKey":3,"name":"Customer#000000003","address":"MG9kdTD2WBHm","nationKey":1,"phone":"11-719-748-3364","accountBalance":7498.12,"marketSegment":"AUTOMOBILE","comment":" deposits eat slyly ironic, even instructions. express foxes detect slyly. blithel
 {"rowNumber":5,"customerKey":5,"name":"Customer#000000005","address":"KvpyuHCplrB84WgAiGV6sYpZq7Tj","nationKey":3,"phone":"13-750-942-6364","accountBalance":794.47,"marketSegment":"HOUSEHOLD","comment":"n accounts will have to unwind. foxes cajole accor"}
 {"rowNumber":7,"customerKey":7,"name":"Customer#000000007","address":"TcGe5gaZNgVePxU5kRrvXBfkasDTea","nationKey":18,"phone":"28-190-982-9759","accountBalance":9561.95,"marketSegment":"AUTOMOBILE","comment":"ainst the ironic, express theodolites. express, even pinto bean
 {"rowNumber":9,"customerKey":9,"name":"Customer#000000009","address":"xKiAFTjUsCuxfeleNqefumTrjS","nationKey":8,"phone":"18-338-906-3675","accountBalance":8324.07,"marketSegment":"FURNITURE","comment":"r theodolites according to the requests wake thinly excuses: pending
(5 rows)

lk:tpch> SELECT sum(cast(json_extract_scalar(_message, '$.accountBalance') AS double)) FROM customer LIMIT 10;
   _col0
------------
 6681865.59
(1 row)
```

The data from Kafka can be queried using openLooKeng but it is not yet in actual table shape. The raw data is available through the `_message` and `_key` columns but it is not decoded into columns. As the sample data is in JSON format, the [json](../functions/json.md) built into openLooKeng can be used to slice the data.

### Step 5: Add a topic description file

The Kafka connector supports topic description files to turn raw data into table format. These files are located in the `etc/kafka` folder in the openLooKeng installation and must end with `.json`. It is recommended that
the file name matches the table name but this is not necessary.

Add the following file as `etc/kafka/tpch.customer.json` and restart openLooKeng:

``` json
{
    "tableName": "customer",
    "schemaName": "tpch",
    "topicName": "tpch.customer",
    "key": {
        "dataFormat": "raw",
        "fields": [
            {
                "name": "kafka_key",
                "dataFormat": "LONG",
                "type": "BIGINT",
                "hidden": "false"
            }
        ]
    }
}
```

The customer table now has an additional column: `kafka_key`.

``` sql
lk:tpch> DESCRIBE customer;
      Column       |  Type   | Extra |                   Comment
-------------------+---------+-------+---------------------------------------------
 kafka_key         | bigint  |       |
 _partition_id     | bigint  |       | Partition Id
 _partition_offset | bigint  |       | Offset for the message within the partition
 _segment_start    | bigint  |       | Segment start offset
 _segment_end      | bigint  |       | Segment end offset
 _segment_count    | bigint  |       | Running message count per segment
 _key              | varchar |       | Key text
 _key_corrupt      | boolean |       | Key data is corrupt
 _key_length       | bigint  |       | Total number of key bytes
 _message          | varchar |       | Message text
 _message_corrupt  | boolean |       | Message data is corrupt
 _message_length   | bigint  |       | Total number of message bytes
(12 rows)

lk:tpch> SELECT kafka_key FROM customer ORDER BY kafka_key LIMIT 10;
 kafka_key
-----------
         0
         1
         2
         3
         4
         5
         6
         7
         8
         9
(10 rows)
```

The topic definition file maps the internal Kafka key (which is a raw long in eight bytes) onto a openLooKeng `BIGINT` column.

### Step 6: Map all the values from the topic message onto columns

Update the `etc/kafka/tpch.customer.json` file to add fields for the message and restart openLooKeng. As the fields in the message are JSON, it uses the `json` data format. This is an example where different data formats are used for the key and the message.

``` json
{
    "tableName": "customer",
    "schemaName": "tpch",
    "topicName": "tpch.customer",
    "key": {
        "dataFormat": "raw",
        "fields": [
            {
                "name": "kafka_key",
                "dataFormat": "LONG",
                "type": "BIGINT",
                "hidden": "false"
            }
        ]
    },
    "message": {
        "dataFormat": "json",
        "fields": [
            {
                "name": "row_number",
                "mapping": "rowNumber",
                "type": "BIGINT"
            },
            {
                "name": "customer_key",
                "mapping": "customerKey",
                "type": "BIGINT"
            },
            {
                "name": "name",
                "mapping": "name",
                "type": "VARCHAR"
            },
            {
                "name": "address",
                "mapping": "address",
                "type": "VARCHAR"
            },
            {
                "name": "nation_key",
                "mapping": "nationKey",
                "type": "BIGINT"
            },
            {
                "name": "phone",
                "mapping": "phone",
                "type": "VARCHAR"
            },
            {
                "name": "account_balance",
                "mapping": "accountBalance",
                "type": "DOUBLE"
            },
            {
                "name": "market_segment",
                "mapping": "marketSegment",
                "type": "VARCHAR"
            },
            {
                "name": "comment",
                "mapping": "comment",
                "type": "VARCHAR"
            }
        ]
    }
}
```

Now for all the fields in the JSON of the message, columns are defined and the sum query from earlier can operate on the `account_balance` column directly:

``` sql
lk:tpch> DESCRIBE customer;
      Column       |  Type   | Extra |                   Comment
-------------------+---------+-------+---------------------------------------------
 kafka_key         | bigint  |       |
 row_number        | bigint  |       |
 customer_key      | bigint  |       |
 name              | varchar |       |
 address           | varchar |       |
 nation_key        | bigint  |       |
 phone             | varchar |       |
 account_balance   | double  |       |
 market_segment    | varchar |       |
 comment           | varchar |       |
 _partition_id     | bigint  |       | Partition Id
 _partition_offset | bigint  |       | Offset for the message within the partition
 _segment_start    | bigint  |       | Segment start offset
 _segment_end      | bigint  |       | Segment end offset
 _segment_count    | bigint  |       | Running message count per segment
 _key              | varchar |       | Key text
 _key_corrupt      | boolean |       | Key data is corrupt
 _key_length       | bigint  |       | Total number of key bytes
 _message          | varchar |       | Message text
 _message_corrupt  | boolean |       | Message data is corrupt
 _message_length   | bigint  |       | Total number of message bytes
(21 rows)

lk:tpch> SELECT * FROM customer LIMIT 5;
 kafka_key | row_number | customer_key |        name        |                address                | nation_key |      phone      | account_balance | market_segment |                                                      comment
-----------+------------+--------------+--------------------+---------------------------------------+------------+-----------------+-----------------+----------------+---------------------------------------------------------------------------------------------------------
         1 |          2 |            2 | Customer#000000002 | XSTf4,NCwDVaWNe6tEgvwfmRchLXak        |         13 | 23-768-687-3665 |          121.65 | AUTOMOBILE     | l accounts. blithely ironic theodolites integrate boldly: caref
         3 |          4 |            4 | Customer#000000004 | XxVSJsLAGtn                           |          4 | 14-128-190-5944 |         2866.83 | MACHINERY      |  requests. final, regular ideas sleep final accou
         5 |          6 |            6 | Customer#000000006 | sKZz0CsnMD7mp4Xd0YrBvx,LREYKUWAh yVn  |         20 | 30-114-968-4951 |         7638.57 | AUTOMOBILE     | tions. even deposits boost according to the slyly bold packages. final accounts cajole requests. furious
         7 |          8 |            8 | Customer#000000008 | I0B10bB0AymmC, 0PrRYBCP1yGJ8xcBPmWhl5 |         17 | 27-147-574-9335 |         6819.74 | BUILDING       | among the slyly regular theodolites kindle blithely courts. carefully even theodolites haggle slyly alon
         9 |         10 |           10 | Customer#000000010 | 6LrEaV6KR6PLVcgl2ArL Q3rqzLzcT1 v2    |          5 | 15-741-346-9870 |         2753.54 | HOUSEHOLD      | es regular deposits haggle. fur
(5 rows)

lk:tpch> SELECT sum(account_balance) FROM customer LIMIT 10;
   _col0
------------
 6681865.59
(1 row)
```

Now all the fields from the `customer` topic messages are available as openLooKeng table columns.

### Step 7: Use live data

openLooKeng can query live data in Kafka as it arrives. To simulate a live feed of data, this tutorial sets up a feed of live tweets into Kafka.

#### Setup a live Twitter feed

-   Download the twistr tool

``` shell
$ curl -o twistr https://repo1.maven.org/maven2/de/softwareforge/twistr_kafka_0811/1.2/twistr_kafka_0811-1.2.sh
$ chmod 755 twistr
```

-   Create a developer account at <https://dev.twitter.com/> and set up an access and consumer token.
-   Create a `twistr.properties` file and put the access and consumer key and secrets into it:

``` properties
twistr.access-token-key=...
twistr.access-token-secret=...
twistr.consumer-key=...
twistr.consumer-secret=...
twistr.kafka.brokers=localhost:9092
```

#### Create a tweets table on openLooKeng

Add the tweets table to the `etc/catalog/kafka.properties` file:

``` properties
connector.name=kafka
kafka.nodes=localhost:9092
kafka.table-names=tpch.customer,tpch.orders,tpch.lineitem,tpch.part,tpch.partsupp,tpch.supplier,tpch.nation,tpch.region,tweets
kafka.hide-internal-columns=false
```

Add a topic definition file for the Twitter feed as `etc/kafka/tweets.json`:

``` json
{
    "tableName": "tweets",
    "topicName": "twitter_feed",
    "dataFormat": "json",
    "key": {
        "dataFormat": "raw",
        "fields": [
            {
                "name": "kafka_key",
                "dataFormat": "LONG",
                "type": "BIGINT",
                "hidden": "false"
            }
        ]
    },
    "message": {
        "dataFormat":"json",
        "fields": [
            {
                "name": "text",
                "mapping": "text",
                "type": "VARCHAR"
            },
            {
                "name": "user_name",
                "mapping": "user/screen_name",
                "type": "VARCHAR"
            },
            {
                "name": "lang",
                "mapping": "lang",
                "type": "VARCHAR"
            },
            {
                "name": "created_at",
                "mapping": "created_at",
                "type": "TIMESTAMP",
                "dataFormat": "rfc2822"
            },
            {
                "name": "favorite_count",
                "mapping": "favorite_count",
                "type": "BIGINT"
            },
            {
                "name": "retweet_count",
                "mapping": "retweet_count",
                "type": "BIGINT"
            },
            {
                "name": "favorited",
                "mapping": "favorited",
                    "type": "BOOLEAN"
            },
            {
                "name": "id",
                "mapping": "id_str",
                "type": "VARCHAR"
            },
            {
                "name": "in_reply_to_screen_name",
                "mapping": "in_reply_to_screen_name",
                "type": "VARCHAR"
            },
            {
                "name": "place_name",
                "mapping": "place/full_name",
                "type": "VARCHAR"
            }
        ]
    }
}
```

As this table does not have an explicit schema name, it will be placed into the `default` schema.

#### Feed live data

Start the twistr tool:

``` shell
$ java -Dness.config.location=file:$(pwd) -Dness.config=twistr -jar ./twistr
```

`twistr` connects to the Twitter API and feeds the \"sample tweet\" feed into a Kafka topic called `twitter_feed`.

Now run queries against live data:

``` shell
$ ./openlk-cli --catalog kafka --schema default

lk:default> SELECT count(*) FROM tweets;
 _col0
-------
  4467
(1 row)

lk:default> SELECT count(*) FROM tweets;
 _col0
-------
  4517
(1 row)

lk:default> SELECT count(*) FROM tweets;
 _col0
-------
  4572
(1 row)

lk:default> SELECT kafka_key, user_name, lang, created_at FROM tweets LIMIT 10;
     kafka_key      |    user_name    | lang |       created_at
--------------------+-----------------+------+-------------------------
 494227746231685121 | burncaniff      | en   | 2014-07-29 14:07:31.000
 494227746214535169 | gu8tn           | ja   | 2014-07-29 14:07:31.000
 494227746219126785 | pequitamedicen  | es   | 2014-07-29 14:07:31.000
 494227746201931777 | josnyS          | ht   | 2014-07-29 14:07:31.000
 494227746219110401 | Cafe510         | en   | 2014-07-29 14:07:31.000
 494227746210332673 | Da_JuanAnd_Only | en   | 2014-07-29 14:07:31.000
 494227746193956865 | Smile_Kidrauhl6 | pt   | 2014-07-29 14:07:31.000
 494227750426017793 | CashforeverCD   | en   | 2014-07-29 14:07:32.000
 494227750396653569 | FilmArsivimiz   | tr   | 2014-07-29 14:07:32.000
 494227750388256769 | jmolas          | es   | 2014-07-29 14:07:32.000
(10 rows)
```

There is now a live feed into Kafka which can be queried using openLooKeng.

### Epilogue: Time stamps

The tweets feed that was set up in the last step contains a time stamp in RFC 2822 format as `created_at` attribute in each tweet.

``` sql
lk:default> SELECT DISTINCT json_extract_scalar(_message, '$.created_at')) AS raw_date
             -> FROM tweets LIMIT 5;
            raw_date
--------------------------------
 Tue Jul 29 21:07:31 +0000 2014
 Tue Jul 29 21:07:32 +0000 2014
 Tue Jul 29 21:07:33 +0000 2014
 Tue Jul 29 21:07:34 +0000 2014
 Tue Jul 29 21:07:35 +0000 2014
(5 rows)
```

The topic definition file for the tweets table contains a mapping onto a timestamp using the `rfc2822` converter:

``` json
...
{
    "name": "created_at",
    "mapping": "created_at",
    "type": "TIMESTAMP",
    "dataFormat": "rfc2822"
},
...
```

This allows the raw data to be mapped onto a openLooKeng timestamp column:

``` sql
lk:default> SELECT created_at, raw_date FROM (
             ->   SELECT created_at, json_extract_scalar(_message, '$.created_at') AS raw_date
             ->   FROM tweets)
             -> GROUP BY 1, 2 LIMIT 5;
       created_at        |            raw_date
-------------------------+--------------------------------
 2014-07-29 14:07:20.000 | Tue Jul 29 21:07:20 +0000 2014
 2014-07-29 14:07:21.000 | Tue Jul 29 21:07:21 +0000 2014
 2014-07-29 14:07:22.000 | Tue Jul 29 21:07:22 +0000 2014
 2014-07-29 14:07:23.000 | Tue Jul 29 21:07:23 +0000 2014
 2014-07-29 14:07:24.000 | Tue Jul 29 21:07:24 +0000 2014
(5 rows)
```

The Kafka connector contains converters for ISO 8601, RFC 2822 text formats and for number-based timestamps using seconds or miilliseconds since the epoch. There is also a generic, text-based formatter which uses Joda-Time format strings to parse text columns.
