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我正在尝试从twitter获取数据然后将其加载到Hive中进行分析。虽然我能够使用flume（使用Twitter 1％firehose Source）将数据导入HDFS，并且还能够将数据加载到Hive表中。
但是无法看到我期望在twitter数据中出现的所有列，例如user_location，user_description，user_friends_count，user_description，user_statuses_count。从Avro派生的模式只包含两个列标题和正文。
以下是我所做的步骤：
1）创建一个低于conf的水槽代理：
a1.sources = r1
a1.sinks = k1
a1.channels = c1
# Describe/configure the source
a1.sources.r1.type =org.apache.flume.source.twitter.TwitterSource
#a1.sources.r1.type = com.cloudera.flume.source.TwitterSource
a1.sources.r1.consumerKey =XXXXXXXXXXXXXXXXXXXXXXXXXXXX
a1.sources.r1.consumerSecret =XXXXXXXXXXXXXXXXXXXXXXXXXXXX
a1.sources.r1.accessToken =XXXXXXXXXXXXXXXXXXXXXXXXXXXX
a1.sources.r1.accessTokenSecret =XXXXXXXXXXXXXXXXXXXXXXXXXXXX
a1.sources.r1.keywords = bigdata, healthcare, oozie
# Describe the sink
a1.sinks.k1.type = hdfs
a1.sinks.k1.hdfs.path = hdfs://192.168.192.128:8020/hdp/apps/2.2.0.0-2041/flume/twitter
a1.sinks.k1.hdfs.fileType = DataStream
a1.sinks.k1.hdfs.writeFormat = Text
a1.sinks.k1.hdfs.inUsePrefix = _
a1.sinks.k1.hdfs.fileSuffix = .avro
# added for invalid block size error
a1.sinks.k1.serializer = avro_event
#a1.sinks.k1.deserializer.schemaType = LITERAL
# added for exception java.io.IOException:org.apache.avro.AvroTypeException: Found Event, expecting Doc
#a1.sinks.k1.serializer.compressionCodec = snappy
a1.sinks.k1.hdfs.batchSize = 1000
a1.sinks.k1.hdfs.rollSize = 67108864
a1.sinks.k1.hdfs.rollCount = 0
a1.sinks.k1.hdfs.rollInterval = 30
# Use a channel which buffers events in memory
a1.channels.c1.type = memory
a1.channels.c1.capacity = 1000
a1.channels.c1.transactionCapacity = 1000
# Bind the source and sink to the channel
a1.sources.r1.channels = c1
a1.sinks.k1.channel = c1
2）从avro数据文件中导出模式，我不知道为什么从avro数据文件派生的模式只有两列标题和正文：
java -jar avro-tools-1.7.7.jar getschema FlumeData.14315982 30978.avro
{
"type" : "record",
"name" : "Event",
"fields" : [ {
"name" : "headers",
"type" : {
"type" : "map",
"values" : "string"
}
}, {
"name" : "body",
"type" : "bytes"
} ]
}
3）运行上面的代理并获取HDFS中的数据，找出avro数据的模式并创建一个Hive表：
CREATE EXTERNAL TABLE TwitterData
ROW FORMAT SERDE 'org.apache.hadoop.hive.serde2.avro.AvroSerDe'
WITH SERDEPROPERTIES ('avro.schema.literal'='
{
"type" : "record",
"name" : "Event",
"fields" : [ {
"name" : "headers",
"type" : {
"type" : "map",
"values" : "string"
}
}, {
"name" : "body",
"type" : "bytes"
} ]
}
')
STORED AS
INPUTFORMAT 'org.apache.hadoop.hive.ql.io.avro.AvroContainerInputFormat'
OUTPUTFORMAT 'org.apache.hadoop.hive.ql.io.avro.AvroContainerOutputFormat'
LOCATION 'hdfs://192.168.192.128:8020/hdp/apps/2.2.0.0-2041/flume/twitter'
;
4）描述蜂巢表：
hive> describe twitterdata;
OK
headers map<string,string> from deserializer
body binary from deserializer
Time taken: 0.472 seconds, Fetched: 2 row(s)
5）查询表格： 当我查询表时，我看到'body'列中的二进制数据和'header'列中的实际架构信息。
select * from twitterdata limit 1;
OK
{"type":"record","name":"Doc","doc":"adoc","fields":[{"name":"id","type":"string"},{"name":"user_friends_count","type":["int","null"]},{"name":"user_location","type":["string","null"]},{"name":"user_description","type":["string","null"]},{"name":"user_statuses_count","type":["int","null"]},{"name":"user_followers_count","type":["int","null"]},{"name":"user_name","type":["string","null"]},{"name":"user_screen_name","type":["string","null"]},{"name":"created_at","type":["string","null"]},{"name":"text","type":["string","null"]},{"name":"retweet_count","type":["long","null"]},{"name":"retweeted","type":["boolean","null"]},{"name":"in_reply_to_user_id","type":["long","null"]},{"name":"source","type":["string","null"]},{"name":"in_reply_to_status_id","type":["long","null"]},{"name":"media_url_https","type":["string","null"]},{"name":"expanded_url","type":["string","null"]}]}�1|$���)]'��G�$598792495703543808�Bあいたぁぁぁぁぁぁぁ！�~�ゆっけ0725Yukken(2015-05-14T10:10:30Z<ん？なんか意味違うわ�<a href="http://twitter.com/download/iphone" rel="nofollow">Twitter for iPhone</a>�1|$���)]'��
Time taken: 2.24 seconds, Fetched: 1 row(s)
如何创建包含实际架构中所有列的配置单元表，如“header”列中所示。我的意思是所有列，如user_location，user_description，user_friends_count，user_description，user_statuses_count？
从avro数据文件派生的架构不应包含更多列吗？
我在水槽剂（org.apache.flume.source.twitter.TwitterSource）中使用的flume-avro来源是否有任何问题？
感谢您阅读..
感谢Farrukh，我已经完成了错误是配置'a1.sinks.k1.serializer = avro_event'，我将其更改为'a1.sinks.k1.serializer = text'，我能够加载数据到Hive。但现在问题是从Hive中检索数据，我这样做时收到以下错误：
hive> describe twitterdata_09062015;
OK
id string from deserializer
user_friends_count int from deserializer
user_location string from deserializer
user_description string from deserializer
user_statuses_count int from deserializer
user_followers_count int from deserializer
user_name string from deserializer
user_screen_name string from deserializer
created_at string from deserializer
text string from deserializer
retweet_count bigint from deserializer
retweeted boolean from deserializer
in_reply_to_user_id bigint from deserializer
source string from deserializer
in_reply_to_status_id bigint from deserializer
media_url_https string from deserializer
expanded_url string from deserializer
select count(1) as num_rows from TwitterData_09062015;
Query ID = root_20150609130404_10ef21db-705a-4e94-92b7-eaa58226ee2e
Total jobs = 1
Launching Job 1 out of 1
Number of reduce tasks determined at compile time: 1
In order to change the average load for a reducer (in bytes):
set hive.exec.reducers.bytes.per.reducer=<number>
In order to limit the maximum number of reducers:
set hive.exec.reducers.max=<number>
In order to set a constant number of reducers:
set mapreduce.job.reduces=<number>
Starting Job = job_1433857038961_0003, Tracking URL = http://sandbox.hortonworks.com:8088/proxy/application_14338570 38961_0003/
Kill Command = /usr/hdp/2.2.0.0-2041/hadoop/bin/hadoop job -kill job_1433857038961_0003
Hadoop job information for Stage-1: number of mappers: 1; number of reducers: 1
* 13:04:36,856 Stage-1 map = 0%, reduce = 0%
* 13:05:09,576 Stage-1 map = 100%, reduce = 100%
Ended Job = job_1433857038961_0003 with errors
Error during job, obtaining debugging information...
Examining task ID: task_1433857038961_0003_m_000000 (and more) from job job_1433857038961_0003
Task with the most failures(4):
Task ID:
task_1433857038961_0003_m_000000
URL:
http://sandbox.hortonworks.com:8088/taskdetails.jsp?jobid=job_1433857038961_0003&tipid=task_1433857038961_0003_m_0 00000
Diagnostic Messages for this Task:
Error: java.io.IOException: java.io.IOException: org.apache.avro.AvroRuntimeException: java.io.IOException: Block si ze invalid or too large for this implementation: -40
at org.apache.hadoop.hive.io.HiveIOExceptionHandlerChain.handleRecordReaderNextException(HiveIOExceptionHand lerChain.java:121)
答案 0 :(得分：1)
这是一步一步的过程，用于下载推文并将其加载到配置单元
Flume agent
##TwitterAgent for collecting Twitter data to Hadoop HDFS #####
TwitterAgent.sources = Twitter
TwitterAgent.channels = FileChannel
TwitterAgent.sinks = HDFS
TwitterAgent.sources.Twitter.type = org.apache.flume.source.twitter.TwitterSource
TwitterAgent.sources.Twitter.channels = FileChannel
TwitterAgent.sources.Twitter.consumerKey = *************
TwitterAgent.sources.Twitter.consumerSecret = **********
TwitterAgent.sources.Twitter.accessToken = ************
TwitterAgent.sources.Twitter.accessTokenSecret = ***********
TwitterAgent.sources.Twitter.maxBatchSize = 50000
TwitterAgent.sources.Twitter.maxBatchDurationMillis = 100000
TwitterAgent.sources.Twitter.keywords = Apache, Hadoop, Mapreduce, hadooptutorial, Hive, Hbase, MySql
TwitterAgent.sinks.HDFS.channel = FileChannel
TwitterAgent.sinks.HDFS.type = hdfs
TwitterAgent.sinks.HDFS.hdfs.path = hdfs://nn1.itbeams.com:9000/user/flume/tweets/avrotweets
TwitterAgent.sinks.HDFS.hdfs.fileType = DataStream
# you do not need to mentioned avro format here. just mention Text
TwitterAgent.sinks.HDFS.hdfs.writeFormat = Text
TwitterAgent.sinks.HDFS.hdfs.batchSize = 200000
TwitterAgent.sinks.HDFS.hdfs.rollSize = 0
TwitterAgent.sinks.HDFS.hdfs.rollCount = 2000000
TwitterAgent.channels.FileChannel.type = file
TwitterAgent.channels.FileChannel.checkpointDir = /var/log/flume/checkpoint/
TwitterAgent.channels.FileChannel.dataDirs = /var/log/flume/data/
我在avsc文件中创建了avro架构。一旦你创建了然后把这个文件放在hadoop对你的用户文件夹，如/ user / youruser /.
{"type":"record",
"name":"Doc",
"doc":"adoc",
"fields":[{"name":"id","type":"string"},
{"name":"user_friends_count","type":["int","null"]},
{"name":"user_location","type":["string","null"]},
{"name":"user_description","type":["string","null"]},
{"name":"user_statuses_count","type":["int","null"]},
{"name":"user_followers_count","type":["int","null"]},
{"name":"user_name","type":["string","null"]},
{"name":"user_screen_name","type":["string","null"]},
{"name":"created_at","type":["string","null"]},
{"name":"text","type":["string","null"]},
{"name":"retweet_count","type":["long","null"]},
{"name":"retweeted","type":["boolean","null"]},
{"name":"in_reply_to_user_id","type":["long","null"]},
{"name":"source","type":["string","null"]},
{"name":"in_reply_to_status_id","type":["long","null"]},
{"name":"media_url_https","type":["string","null"]},
{"name":"expanded_url","type":["string","null"]}
在hive表中加载了推文。如果你在hql文件中保存代码那么棒。
CREATE TABLE tweetsavro
ROW FORMAT SERDE
'org.apache.hadoop.hive.serde2.avro.AvroSerDe'
STORED AS INPUTFORMAT
'org.apache.hadoop.hive.ql.io.avro.AvroContainerInputFormat'
OUTPUTFORMAT
'org.apache.hadoop.hive.ql.io.avro.AvroContainerOutputFormat'
TBLPROPERTIES ('avro.schema.url'='hdfs:///user/youruser/examples/schema/twitteravroschema.avsc') ;
LOAD DATA INPATH '/user/flume/tweets/avrotweets/FlumeData.*' OVERWRITE INTO TABLE tweetsavro;
hive中的tweetsavro表
hive> describe tweetsavro;
OK
id string from deserializer
user_friends_count int from deserializer
user_location string from deserializer
user_description string from deserializer
user_statuses_count int from deserializer
user_followers_count int from deserializer
user_name string from deserializer
user_screen_name string from deserializer
created_at string from deserializer
text string from deserializer
retweet_count bigint from deserializer
retweeted boolean from deserializer
in_reply_to_user_id bigint from deserializer
source string from deserializer
in_reply_to_status_id bigint from deserializer
media_url_https string from deserializer
expanded_url string from deserializer
Time taken: 0.6 seconds, Fetched: 17 row(s)