1. 概述
Druid的数据摄入主要包括两大类：
1. 实时输入摄入：包括Pull,Push两种
- Pull:需要启动一个RealtimeNode节点，通过不同的Firehose摄取不同种类的数据源。
- Push:需要启动Tranquility或是Kafka索引服务。通过HTTP调用的方式进行数据摄入
2. 离线数据摄入:可以通过Realtime节点摄入，也可以通过索引节点启动任务摄入
本文演示环节主要基于上一章部署的集群来进行
2. 实时数据摄入
2.1 Pull
由于Realtime Node 没有提供高可用，可伸缩等特性，对于比较重要的场景推荐使用 Tranquility Server or 或是Tranquility Kafka索引服务
2.2 Push
Indexing service在前文已经介绍过了，Tranquility 是一个Scala库，它通过索引服务实现数据实时的摄入。它之所以存在，是因为Indexing service API属于低层面的。Tranquility是对索引服务进行抽象封装， 对使用者屏蔽了 创建任务，处理分区、复制、服务发现和shema rollover等环节。
通过Tranquility 的数据摄入，可以分为两种方式
Tranquility Server：发送方可以通过Tranquility Server 提供的HTTP接口，向Druid发送数据。
Tranquility Kafka：发送发可以先将数据发送到Kafka,Tranquility Kafka会根据配置从Kafka获取数据，并写到Druid中。
2.2.1 Tranquility Server配置
配置流程如下
1. 开启Tranquility Server，在数据节点上编辑conf/supervise/data-with-query.conf 文件，将Tranquility Server注释放开
# Uncomment to use Tranquility Server
!p95 tranquility-server bin/tranquility server -configFile conf/tranquility/server.json2.拷贝quick里面的server.json
root@native-lufanfeng-4-5-24-140:~/imply-2.3.8# cp conf-quickstart/tranquility/server.json conf/tranquility/javascript:void(0)3.启动服务
root@native-lufanfeng-4-5-24-140:~/imply-2.3.8# bin/supervise -c conf/supervise/data-with-query.conf4.启动信息如下：
[Fri Dec 8 15:41:39 2017] Running command[tranquility-server], logging to[/root/imply-2.3.8/var/sv/tranquility-server.log]: bin/tranquility server -configFile conf/tranquility/server.json5.发送数据
bin/generate-example-metrics | curl -XPOST -H'Content-Type: application/json' --data-binary @- http://localhost:8200/v1/post/tutorial-tranquility-server如果成功会打印出,表名产生了25条数据到druid里
{"result":{"received":25,"sent":25}}
6.查询数据
root@native-lufanfeng-4-5-24-140:~/imply-2.3.8/bin#./plyql -h localhost -p 8082 -q "SELECT server, SUM("count") AS "events", COUNT(*) AS "rows" FROM "tutorial-tranquility-server" GROUP BY server;"
┌──────────────────┬────────┬──────┐
│ server │ events │ rows │
├──────────────────┼────────┼──────┤
│ www1.example.com │ 1 │ 1 │
│ www2.example.com │ 5 │ 4 │
│ www3.example.com │ 7 │ 2 │
│ www4.example.com │ 5 │ 2 │
│ www5.example.com │ 7 │ 7 │
└──────────────────┴────────┴──────┘
重启Tranquility Server:bin/service –restart tranquility-server
2.2.2 Tranquility Kafka配置
配置流程如下
1. 开启Tranquility Kafka，在数据节点上编辑conf/supervise/data-with-query.conf 文件，将Tranquility Kafka注释放开
# Uncomment to use Tranquility Server
!p95 tranquility-server bin/tranquility server -configFile conf/tranquility/server.json
2.拷贝quick里面的kafka.json
root@native-lufanfeng-4-5-24-140:~/imply-2.3.8# cp conf-quickstart/tranquility/kafka.json conf/tranquility/详细配置可参考：http://druid.io/docs/0.10.1/tutorials/tutorial-kafka.html
3.在kafa集群中创建topic
root@native-lufanfeng-3-5-24-139:/opt/PaaS/Talas/lib/Kafka/bin#./kafka-topics.sh --create --zookeeper native-lufanfeng-2-5-24-138:2181,native-lufanfeng-3-5-24-139:2181,native-lufanfeng-4-5-24-140:2181 --replication-factor 1 --partitions 1 --topic tutorial-tranquility-kafka4.启动服务
root@native-lufanfeng-4-5-24-140:~/imply-2.3.8# bin/supervise -c conf/supervise/data-with-query.conf启动信息如下：
[Tue Dec 12 10:43:28 2017] Running command[tranquility-kafka], logging to[/root/imply-2.3.8/var/sv/tranquility-kafka.log]: bin/tranquility kafka -configFile conf/tranquility/kafka.json
使用kafka自带的工具发送数据
root@native-lufanfeng-3-5-24-139:/opt/PaaS/Talas/lib/Kafka/bin# ./kafka-console-producer.sh --broker-list native-lufanfeng-2-5-24-138:9092,native-lufanfeng-3-5-24-139:9092,native-lufanfeng-4-5-24-140:9092 --topic tutorial-tranquility-kafka
{"unit": "milliseconds", "http_method": "GET", "value": 107, "timestamp": "2017-12-12T05:55:59Z", "http_code": "200", "page": "/list", "metricType": "request/latency", "server": "www1.example.com"}
{"unit": "milliseconds", "http_method": "GET", "value": 19, "timestamp": "2017-12-12T05:55:59Z", "http_code": "200", "page": "/list", "metricType": "request/latency", "server": "www1.example.com"}
{"unit": "milliseconds", "http_method": "GET", "value": 135, "timestamp": "2017-12-12T05:55:59Z", "http_code": "200", "page": "/list", "metricType": "request/latency", "server": "www5.example.com"}
{"unit": "milliseconds", "http_method": "GET", "value": 103, "timestamp": "2017-12-12T05:55:59Z", "http_code": "200", "page": "/list", "metricType": "request/latency", "server": "www4.example.com"}
{"unit": "milliseconds", "http_method": "GET", "value": 93, "timestamp": "2017-12-12T05:55:59Z", "http_code": "200", "page": "/", "metricType": "request/latency", "server": "www3.example.com"}
{"unit": "milliseconds", "http_method": "GET", "value": 89, "timestamp": "2017-12-12T05:55:59Z", "http_code": "200", "page": "/list", "metricType": "request/latency", "server": "www2.example.com"}
{"unit": "milliseconds", "http_method": "GET", "value": 7, "timestamp": "2017-12-12T05:55:59Z", "http_code": "200", "page": "/", "metricType": "request/latency", "server": "www5.example.com"}
{"unit": "milliseconds", "http_method": "GET", "value": 65, "timestamp": "2017-12-12T05:55:59Z", "http_code": "200", "page": "/", "metricType": "request/latency", "server": "www3.example.com"}
此时观察kafka-server.log的日志会发现类似于如下输出
2017-12-12 06:21:37,241 [KafkaConsumer-CommitThread] INFO c.m.tranquility.kafka.KafkaConsumer - Flushed {tutorial-tranquility-kafka={receivedCount=0, sentCount=8,droppedCount=8, unparseableCount=0}} pending messages in 0ms and committed offsets in 0ms. 在datasource中,windowPeriod设置成了P10M,timestamp不在当前时间10M内的数据都会被过滤，由于上面的数据的timestamp和执行时间相差了大概26分钟左右，所以都会被drop调，为了达到演示效果，可以对bin/generate-example-metrics-main 的脚本进行调整。代码如下：
# Copyright 2017 Imply Data, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import json
import random
import sys
from datetime import datetime
from kafka import KafkaProducer
from kafka import KafkaClient
hosts="native-lufanfeng-2-5-24-138:9092,native-lufanfeng-3-5-24-139:9092,native-lufanfeng-4-5-24-140:9092"
# hosts="10.48.253.104:9092"
topic='tutorial-tranquility-kafka'
class KafkaSender():
def __init__(self):
self.client=KafkaClient(hosts)
self.producer=KafkaProducer(bootstrap_servers=hosts)
self.client.ensure_topic_exists(topic)
def send_messages(self,msg):
self.producer.send(topic,msg)
self.producer.r
def main():
parser = argparse.ArgumentParser(description='Generate example page request latency metrics.')
parser.add_argument('--count', '-c', type=int, default=25, help='Number of events to generate (negative for unlimited)')
args = parser.parse_args()
count = 0
sender = KafkaSender()
while args.count < 0 or count < args.count:
timestamp = datetime.utcnow().strftime("%Y-%m-%dT%H:%M:%SZ")
r = random.randint(1, 4)
if r == 1 or r == 2:
page = '/'
elif r == 3:
page = '/list'
else:
page = '/get/' + str(random.randint(1, 99))
server = 'www' + str(random.randint(1, 5)) + '.example.com'
latency = max(1, random.gauss(80, 40))
record = json.dumps({
'timestamp': timestamp,
'metricType': 'request/latency',
'value': int(latency),
# Additional dimensions
'page': page,
'server': server,
'http_method': 'GET',
'http_code': '200',
'unit': 'milliseconds'
})
sender.send_messages(record)
print 'Send:%s Successful!' % record
count += 1
try:
main()
except KeyboardInterrupt:
sys.exit(1)
3. 离线数据摄入
3.1 静态文件摄入
使用自带的摄入机制，可以在数据节点摄入本地文件，方法如下：
bin/post-index-task --file quickstart/wikiticker-index.json
wikiticker-index.json 文件中既包括datasource的定义，也包括数据文件位置的配置
3.2 HDFS文件摄入
配置过程可参考：http://druid.io/docs/0.10.1/ingestion/batch-ingestion.html
4. 配置参考
通用配置：https://github.com/druid-io/tranquility/blob/master/docs/configuration.md
数据摄入通用配置:http://druid.io/docs/latest/ingestion/index.html
Tranquility Kafka:https://github.com/druid-io/tranquility/blob/master/docs/kafka.md
5. 其他注意事项
5.1 数据分片
Druid的分片基本都是通过配置tunningConfig来配置的，实时，批量配置的方式会存在一定的差异
实时加载包括下面两种类型
- Linear分片：
- 添加新节点时，原节点的配置不需要调整
- 当存在分片时数据也能被查询
- Numbered分片
- 所有分片存在时，才能查询
- 需要制定分片总数
本地文件加载包括下面两种类型
- 按照Partition大小分片
- 设置总的分片数
Hadoop文件加载包括下面两种类型
- 哈希分片
- 范围分片
5.2 高基数维度优化
对于需要统计维度基数的需求，如果某个维度的基数很大，可能会存在下列问题。维度基数统计主要包括下面两种类型
- Cardinality: 基于HyperLogLog算法，只在查询阶段做了优化，不能减少存储容量，基数大时，效率可能会有问题
- HyperUnique: 在摄入阶段进行优化，对于不需要对高基数维度进行过滤，分组的业务场景可以使用该类型