Skip to main content

Hive on Spark at CDH 5.3

By Anonymous
Listen:

However, since Hive on Spark is not (yet) officially supported by Cloudera some manual steps are required to get Hive on Spark within CDH 5.3 working. Please note that there are four important requirements additionally to the hands-on work:

  1. Spark Gateway nodes needs to be a Hive Gateway node as well
  2. In case the client configurations are redeployed, you need to copy the hive-site.xml again
  3. In case CDH is upgraded (also for minor patches, often updated without noticing you), you need to adjust the class paths
  4. Hive libraries need to be present on all executors (CM should take care of this automatically)

Login to your spark server(s) and copy the running hive-site.xml to spark:

cp /etc/hive/conf/hive-site.xml /etc/spark/conf/

Start your spark shell with (replace <CDH_VERSION> with your parcel version, e.g. 5.3.2-1.cdh5.3.2.p0.10) and load the hive context within spark-shell:

spark-shell --master yarn-client --driver-class-path "/opt/cloudera/parcels/CDH-<CDH_VERSION>/lib/hive/lib/*" --conf spark.executor.extraClassPath="/opt/cloudera/parcels/CDH-<CDH_VERSION>/lib/hive/lib/*"
..
scala> val hive = new org.apache.spark.sql.hive.HiveContext(sc)
sql: org.apache.spark.sql.hive.HiveContext = org.apache.spark.sql.hive.HiveContext@1c966488
scala> var s1 = hive.sql("SELECT COUNT(*) FROM sample_07").collect()
s1: Array[org.apache.spark.sql.Row] = Array([823])
Labels:

Comments

Post a Comment

Popular posts from this blog

Why Is Customer Obsession Disappearing?

By
 It's wild that even with all the cool tech we've got these days, like AI solving complex equations and doing business across time zones in a flash, so many companies are still struggling with the basics: taking care of their customers.The drama around Coinbase's customer support is a prime example of even tech giants messing up. And it's not just Coinbase — it's a big-picture issue for the whole industry. At some point, the idea of "customer obsession" got replaced with "customer automation," and now we're seeing the problems that came with it. "Cases" What Not to Do Coinbase, as main example, has long been synonymous with making cryptocurrency accessible. Whether you’re a first-time buyer or a seasoned trader, their platform was once the gold standard for user experience. But lately, their customer support practices have been making headlines for all the wrong reasons: Coinbase - Stuck in the Loop:  Users have reported being caugh...
Post a Comment
Read more

MySQL Scaling in 2024

By
When your MySQL database reaches its performance limits, vertical scaling through hardware upgrades provides a temporary solution. Long-term growth, though, requires a more comprehensive approach. This involves optimizing the database strategically and integrating complementary technologies. Caching The implementation of a caching layer, such as Memcached or Redis , can result in a notable reduction in the load and an increase ni performance at MySQL. In-memory stores cache data that is accessed frequently, enabling near-instantaneous responses and freeing the database for other tasks. For applications with heavy read traffic on relatively static data (e.g. product catalogues, user profiles), caching represents a low-effort, high-impact solution. Consider a online shop product catalogue with thousands of items. With each visit to the website, the application queries the database in order to retrieve product details. By using caching, the retrieved details can be stored in Memcached (a...
Post a Comment
Read more

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 in...
Post a Comment
Read more