Skip to main content

Speedup Sqoop

By Anonymous
Listen:

Sqoop [1] (sql to hadoop) lets easy connect RDBMS into a hadoop infrastructure. Newest plugin comes from Microsoft and let us connect MS-SQL Server and hadoop each together. As a cool feature you can create a jar-file from your job, its pretty easy, just here a line:

sqoop export --connect jdbc:<RDBMS>:thin:@<HOSTNAME>:<PORT>:<DB-NAME> --table<TABLENAME> --username<USERNAME> --password<PASSWORD> --export-dir <HDFS DIR WHICH CONTAINS DATA> --direct --fields-terminated-by '<TERMINATOR (Java)>' --package-name <JOBNAME>.<IDENTIFIER> --outdir <WHERE THE JAR SHOULD WRITTEN> --bindir <BIN_DIR>

After you fired up you'll find a jar-package in --outdir, unzip it and you find your java-code and the precompiled class,so you can start to tune them.

Now lets start the job again, but use the precompiled class:

sqoop export --connect jdbc:<RDBMS>:thin:@<HOSTNAME>:<PORT>:<DB-NAME> --table<TABLENAME> --username<USERNAME> --password<PASSWORD> --export-dir <HDFS DIR WHICH CONTAINS DATA> --direct --fields-terminated-by '<TERMINATOR (Java)>' --jar-file <PATH/TO/JAR> 
--class-name <JOBNAME>.<IDENTIFIER>.<CLASSNAME>

The step above let you increase the export of large datasets dramatically. So I speedup a export of 100k records from hdfs into a oracle-DB from 16sec into 8sec. 


[1] http://incubator.apache.org/sqoop/ 
Labels:

Comments

Post a Comment

Popular posts from this blog

Deal with corrupted messages in Apache Kafka

By
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
Post a Comment
Read more

Hive query shows ERROR "too many counters"

By Anonymous
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
2 comments
Read more

AI's False Reality: Understanding Hallucination

By
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
Post a Comment
Read more