Skip to main content

Flume 1.3.1 Windows binary release online

Listen:
Andy Blozhou, a Chinese Flume enthusiast provide precompiled Windows binaries of Flume-1.3.1, including a startup bat and Avro bat.
You can grap this build on their website http://abloz.com/flume/windows_download.html :

======== snip ========

This is the flume-ng 1.3.1 windows version for download.

simple usage:
unzip the apache-flume-1.3.1-bin.zip
run bin/flume.bat for agent. 
run bin/flume-avroclient.bat for avro-client. 
Need modify for your own env. 
detail:
(To compile flume-ng on windows, please reference http://mapredit.blogspot.com/2012/07/run-flume-13x-on-windows.html or my chinese version http://abloz.com/2013/02/18/compile-under-windows-flume-1-3-1.html)

1.download the windows version of flume 1.3.1 file apache-flume-1.3.1-bin.zip from http://abloz.com/flume/windows_download.html
2.unzip the apache-flume-1.3.1-bin.zip to a directory.
3.install jdk 1.6 from oracle,and set JAVA_HOME of the env.
download from http://www.oracle.com/technetwork/java/javase/downloads/index.html
4.test agent:
4.1 modify settings of conf/console.conf,conf/hdfs.conf for agent test.
4.2 test source syslog, sink: console out agent
4.2.1 check flume.bat,modify the variables to your env.
4.2.2 click flume.bat
4.2.3 on another computer run command:
echo "<13>test msg" >/tmp/msg
nc -v your_flume_sysloghost port < /tmp/msg
4.2.4 check your syslog host flume output
4.2.5 samples see http://abloz.com/2013/02/18/compile-under-windows-flume-1-3-1.html
4.3 test avro-client
4.3.1 run a avro source flume agent on a node.
4.3.2 modify flume-avroclient.bat and head.txt
4.3.3 run flume-avroclient.bat

tested on windows7 32bit version

enjoy!
Andy
2013.2.20
http://abloz.com

Comments

Popular posts from this blog

Deal with corrupted messages in Apache Kafka

Under some strange circumstances, it can happen that a message in a Kafka topic is corrupted. This often happens when using 3rd party frameworks with Kafka. In addition, Kafka < 0.9 does not have a lock on Log.read() at the consumer read level, but does have a lock on Log.write(). This can lead to a rare race condition as described in KAKFA-2477 [1]. A likely log entry looks like this: ERROR Error processing message, stopping consumer: (kafka.tools.ConsoleConsumer$) kafka.message.InvalidMessageException: Message is corrupt (stored crc = xxxxxxxxxx, computed crc = yyyyyyyyyy Kafka-Tools Kafka stores the offset of each consumer in Zookeeper. To read the offsets, Kafka provides handy tools [2]. But you can also use zkCli.sh, at least to display the consumer and the stored offsets. First we need to find the consumer for a topic (> Kafka 0.9): bin/kafka-consumer-groups.sh --zookeeper management01:2181 --describe --group test Prior to Kafka 0.9, the only way to get this inform

Hive query shows ERROR "too many counters"

A hive job face the odd " Too many counters:"  like Ended Job = job_xxxxxx with exception 'org.apache.hadoop.mapreduce.counters.LimitExceededException(Too many counters: 201 max=200)' FAILED: Execution Error, return code 1 from org.apache.hadoop.hive.ql.exec.MapRedTask Intercepting System.exit(1) These happens when operators are used in queries ( Hive Operators ). Hive creates 4 counters per operator, max upto 1000, plus a few additional counters like file read/write, partitions and tables. Hence the number of counter required is going to be dependent upon the query.  To avoid such exception, configure " mapreduce.job.counters.max " in mapreduce-site.xml to a value above 1000. Hive will fail when he is hitting the 1k counts, but other MR jobs not. A number around 1120 should be a good choice. Using " EXPLAIN EXTENDED " and " grep -ri operators | wc -l " print out the used numbers of operators. Use this value to tweak the MR s

AI's False Reality: Understanding Hallucination

Artificial Intelligence (AI) has leapfrogged to the poster child of technological innovation, on track to transform industries in a scale similar to the Industrial Revolution of the 1800s. But in this case, as cutting-edge technology, AI presents its own unique challenge, exploiting our human behavior of "love to trust", we as humans face a challenge: AI hallucinations. This phenomenon, where AI models generate outputs that are factually incorrect, misleading, or entirely fabricated, raises complex questions about the reliability and trust of AI models and larger systems. The tendency for AI to hallucinate comes from several interrelated factors. Overfitting – a condition where models become overly specialized to their training data – can lead to confident but wildly inaccurate responses when presented with novel scenarios (Guo et al., 2017). Moreover, biases embedded within datasets shape the models' understanding of the world; if these datasets are flawed or unreprese