Skip to main content

HBase: MSLAB and CMS vs. ParallelGC

Listen:
Tuning Java opts for HBase, for example, are necessary steps to get the best performance and stability in large installations. The optimal recommendation looks like:
HBASE_OPTS="-XX:+UseParNewGC -XX:+UseConcMarkSweepGC -XX:CMSInitiatingOccupancyFraction=70 -XX:+CMSParallelRemarkEnabled"

But you can also achieve great success with:
HBASE_OPTS="-server -XX:+UseParallelGC XX:+UseParallelOldGC -XX:ParallelGCThreads=8"

What are the differences between ParallelGC and CMS? 

CMS uses more CPU, but runs concurrently. If a thread is failing, CMS falls back to a non-parallel mode and stops the VM for the entire time it's collecting. But this risk can be minimized by using MSLAB in your HBase configuration.
ParallelGC have a better throughput and longer pause times, and stop the VM on every collection. Means for HBase, you'll have a pause (around 1 sec per GB), which can lead on high loaded clusters to outages in a non acceptable time range.

MSLAB (MemStore-Local Allocation Buffers)

The most GC pauses are caused by old-gen fragmentation, and CMS can't defragment without pause the VM (Juliet pause). MSLAB now moves the memstore allocations into the configured chunks into the old generation. When you start or upgrade into HBase 0.92x, MSLAB is enabled per default (http://hbase.apache.org/book/upgrade0.92.html). 

hbase.hregion.memstore.mslab.enabled=true
hbase.hregion.memstore.mslab.chunksize=2MB (2MB per default)
hbase.hregion.memstore.mslab.max.allocation=256KB (256KB per default)


More about MSLAB you can find in Todd's presentation: http://www.slideshare.net/cloudera/hbase-hug-presentation


Comments

Popular posts from this blog

Why Is Customer Obsession Disappearing?

 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...

MySQL Scaling in 2024

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...

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 in...