A larger buffer consumes more memory on both the client and serverbut results in fewer remote procedure calls.
As a result, when replaying the recovered edits, it is possible to determine if all edits have been written. If the last edit that was written to the HFile is greater than or equal to the edit sequence id included in the file name, it is clear that all writes from the edit file have been completed.
When the region is opened, the recovered. If any such files are present, they are replayed by reading the edits and saving them to the memstore.
After all edit files are replayed, the contents of the memstore are written to disk HFile and the edit files are deleted. Times to complete single threaded log splitting vary, but the process may take several hours if multiple region servers have crashed.
It reduces the time to complete the process dramatically, and hence improves the availability of regions and tables.
For example, we knew a cluster crashed. With single threaded log splitting, it took around 9 hours to recover. With distributed log splitting, it just took around 6 minutes.
Distributed log splitting HBase 0. For one log splitting invocation, all the log files are processed sequentially. After a cluster restarts from crash, unfortunately, all region servers are idle and waiting for the master to finish the log splitting. Instead of having all the region servers remain idle, why not make them useful and help in the log splitting process?
This is the insight behind distributed log splitting With distributed log splitting, the master is the boss.
In each region server, there is a daemon thread called split log worker. Split log worker does the actual work to split the logs. The worker watches the splitlog znode all the time. If there are new tasks, split log worker retrieves the task paths, and then loops through them all to grab any one which is not claimed by other worker yet.
After the split worker completes the current task, it tries to grab another task to work on if any remains. This feature is controlled by the configuration hbase. By default, it is enabled.The sum of the sizes of these objects is highly dependent on your usage patterns and the characteristics of your data.
For this reason, the HBase Web UI and Cloudera Manager each expose several metrics to help you size and tune the BlockCache. What is the Write-ahead-Log you ask?
In my previous post we had a look at the general storage architecture of HBase. One thing that was mentioned is the Write-ahead-Log, or WAL. This post explains how the log works in detail, but bear in mind that it describes the current version, which is Add the following properties to the heartoftexashop.com file on all HBase nodes, the Master server, and all Set heartoftexashop.com to enable custom Write Ahead Log ("WAL") edits to be If you know the database schema.
disable wal Disables write-ahead logging for HBase writes and puts. Disabling write-ahead logging increases the performance of write operations, but it can result in data loss if the region servers fail.
Configuring the Storage Policy for the Write-Ahead Log (WAL) In CDH and higher, you can configure the preferred HDFS storage policy for HBase's write-ahead log (WAL) replicas. This feature allows you to tune HBase's use of SSDs to your available resources and the demands of your workload.
Edit the parameter HBASE_OPTS in the heartoftexashop.com file and add the JVM option -XX:MaxDirectMemorySize=G, replacing with a value large enough to contain your heap and off-heap BucketCache, expressed as a number of gigabytes.