Kafka连接器教程
介绍
适用于openLooKeng的Kafka连接器允许使用openLooKeng从Apache Kafka访问实时主题数据。本教程演示如何设置主题以及如何创建支持openLooKeng表的主题描述文件。
安装
本教程假定用户熟悉openLooKeng且本地安装有可用的openLooKeng（见手动部署openLooKeng）。教程将专注于设置Apache Kafka并将其与openLooKeng集成。
步骤1：安装Apache Kafka
下载并解压Apache Kafka。
说明
本教程使用Apache Kafka 0.8.1进行了测试。教程应适用于任何0.8.x版本的Apache Kafka。
启动ZooKeeper和Kafka服务器：
$ 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)
...
$ 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)
...
这将在端口2181上启动ZooKeeper，在端口9092上启动Kafka。
步骤2：加载数据
从Maven Central下载tpch-kafka加载器：
$ 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
现在运行kafka-tpch程序，预加载带有tpch数据多个主题：
$ ./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'.
现在，Kafka拥有了多个预先装载了要查询的数据的主题。
步骤3：使Kafka主题对openLooKeng可见
在openLooKeng安装中，为Kafka连接器添加目录属性文件etc/catalog/kafka.properties。该文件列出了Kafka节点和主题：
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
现在启动openLooKeng：
$ bin/launcher start
因为Kafka的表在配置中都有tpch.前缀，所以这些表都在tpch模式中。因为属性文件命名为kafka.properties，所以连接器被装入到kafka目录中。
$ ./openlk-cli --catalog kafka --schema tpch
列出表，以验证操作是否成功：
lk:tpch> SHOW TABLES;
Table
----------
customer
lineitem
nation
orders
part
partsupp
region
supplier
(8 rows)
步骤4：基础数据查询
Kafka数据是非结构化的，没有元数据来描述消息的格式。无需进一步配置，Kafka连接器可以访问数据并以原始形式映射数据，但除了内置列之外没有实际列：
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)
Kafka中的数据可以使用openLooKeng查询，但并不是呈实际表的形式。原始数据可以通过_message和_key列获得，但不会解码为列。由于样本数据是JSON格式，因此可以使用openLooKeng内置的json对数据进行切片。
步骤5：添加主题描述文件
Kafka连接器支持主题描述文件，将原始数据转换为表格式。这些文件位于openLooKeng安装的etc/kafka文件夹中，必须以.json结尾。建议文件名与表名匹配，但不一定必须匹配。
将如下文件添加为etc/kafka/tpch.customer.json，并重启openLooKeng。
{
"tableName": "customer",
"schemaName": "tpch",
"topicName": "tpch.customer",
"key": {
"dataFormat": "raw",
"fields": [
{
"name": "kafka_key",
"dataFormat": "LONG",
"type": "BIGINT",
"hidden": "false"
}
]
}
}
customer表现在有一个额外的列：kafka_key。
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)
主题定义文件将内部Kafka密钥（8字节，原始长度）映射到openLooKeng BIGINT列。
步骤6：将来自主题消息的所有值映射到列
更新etc/kafka/tpch.customer.json文件，为消息添加字段，并重启openLooKeng。由于消息中的字段是JSON，因此使用json数据格式。以下是对键和消息使用不同的数据格式的示例。
{
"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"
}
]
}
}
现在，对于消息的JSON中的所有字段，都定义了列，并且来自早期的求和查询可以直接对account_balance列进行操作：
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)
现在，customer主题消息的所有字段都作为openLooKeng表列可用。
步骤7：使用实时数据
openLooKeng可以在实时数据到达时从Kafka中查询实时数据。为了模拟一个实时数据推送，本教程将实时推文的推送设置到Kafka中。
设置实时Twitter推送
- 下载twistr工具
$ curl -o twistr https://repo1.maven.org/maven2/de/softwareforge/twistr_kafka_0811/1.2/twistr_kafka_0811-1.2.sh
$ chmod 755 twistr
- 在https://dev.twitter.com/创建开发者账号，并设置访问和消费token。
- 创建
twistr.properties文件，将访问密钥和消费者密钥放入其中：
twistr.access-token-key=...
twistr.access-token-secret=...
twistr.consumer-key=...
twistr.consumer-secret=...
twistr.kafka.brokers=localhost:9092
在openLooKeng上创建tweets表
在etc/catalog/kafka.properties文件中添加tweets表：
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
将Twitter推送的主题定义文件添加为etc/kafka/tweets.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"
}
]
}
}
由于此表没有显式的模式名称，因此将把它放入default模式中。
推送实时数据
启动twistr工具：
$ java -Dness.config.location=file:$(pwd) -Dness.config=twistr -jar ./twistr
twistr连接Twitter API，并将“sample tweet”推送到名为twitter_feed的Kafka主题。
现在对实时数据运行查询：
$ ./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)
现在有一个实时推送到Kafka，可以使用openLooKeng查询。
结语：时间戳
在上一步中设置的tweets推送在每个tweet中都包含一个RFC 2822格式的时间戳作为created_at属性。
lk:default> SELECT DISTINCT json_extract_scalar(_message, '$.created_at')) AS raw_date
-> FROM tweets LIMIT 5;
raw_date
--------------------------------
Tue Jul 29 21:07:31 +0000 2014
Tue Jul 29 21:07:32 +0000 2014
Tue Jul 29 21:07:33 +0000 2014
Tue Jul 29 21:07:34 +0000 2014
Tue Jul 29 21:07:35 +0000 2014
(5 rows)
tweets表的主题定义文件包含使用rfc2822转换器转换到时间戳的映射：
...
{
"name": "created_at",
"mapping": "created_at",
"type": "TIMESTAMP",
"dataFormat": "rfc2822"
},
...
这允许将原始数据映射到openLooKeng时间戳列：
lk:default> SELECT created_at, raw_date FROM (
-> SELECT created_at, json_extract_scalar(_message, '$.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)
Kafka连接器包含用于ISO8601、RFC 2822文本格式以及使用自epoch时间以来的秒或毫秒的数的时间戳的转换器。还有一个通用的、基于文本的格式化程序，它使用Joda-Time格式字符串来解析文本列。