Name that you assign the cluster configuration. If you have a separate job tracker node, type in the hostname here.
In my previous post we had a look at the general storage architecture of HBase. This post explains how the log works in detail, but bear in mind that it describes the current version, which is 0.
I will address the various plans to improve the log for 0. For the term itself please read here. This is important in case something happens to the primary storage.
So if the server crashes it can effectively replay that log to get everything up to where the server should have been just before the crash. It also means that if writing the record to the WAL fails the whole operation must be considered a failure.
Let"s look at the high level view of how this is done in HBase. First the client initiates an action that modifies data. This is currently a call to put Putdelete Delete and incrementColumnValue abbreviated as "incr" here at times. And that also pretty much describes the write-path of HBase.
Eventually when the MemStore gets to a certain size or after a specific time the data is asynchronously persisted to the file system.
In between that timeframe data is stored volatile in memory. We have a look now at the various classes or "wheels" working the magic of the WAL. First up is one of the main classes of this contraption.
What you may have read in my previous post and is also illustrated above is that there is only one instance of the HLog class, which is one per HRegionServer. It is what is called when the above mentioned modification methods are invoked One thing to note here is that for performance reasons there is an option for putdeleteand incrementColumnValue to be called with an extra parameter set: If you invoke this method while setting up for example a Put instance then the writing to WAL is forfeited!
That is also why the downward arrow in the big picture above is done with a dashed line to indicate the optional step. By default you certainly want the WAL, no doubt about that. But say you run a large bulk import MapReduce job that you can rerun at any time.
You gain extra performance but need to take extra care that no data was lost during the import. The choice is yours.
Another important feature of the HLog is keeping track of the changes. This is done by using a "sequence number". It uses an AtomicLong internally to be thread-safe and is either starting out at zero - or at that last known number persisted to the file system. So at the end of opening all storage files the HLog is initialized to reflect where persisting has ended and where to continue.
You will see in a minute where this is used. The image to the right shows three different regions. Each of them covering a different row key range. As mentioned above each of these regions shares the the same single instance of HLog.
What that means in this context is that the data as it arrives at each region it is written to the WAL in an unpredictable order. We will address this further below. Last time I did not address that field since there was no context. Now we have one because the Key Type is what identifies what the KeyValue represents, a "put" or a "delete" where there are a few more variations of the latter to express what is to be deleted, value, column family or a specific column.HBase shines at workloads where scanning huge, two-dimensional tables is a requirement.
On the other hand, Cassandra worked well on write-heavy workload trading off with consistency. Thus it’s more suitable for analytics data collection or sensor data collection when consistency over time is acceptable. Disable mutable indexes on write failure until consistency restored The default behavior with mutable indexes is to mark the index as disabled if a write to them fails at commit time, partially rebuild them in the background, and then mark them as active again once consistency is restored.
Distributed Log Replay Description: After a region server fails, we firstly assign a failed region to another region server with recovering state marked in ZooKeeper. Then a SplitLogWorker directly replays edits from WAL(Write-Ahead-Log)s of the failed region server .
By using the option to disable WAL (write-ahead log) on your LOAD statement, writes into HBase can be faster. However, this is not a safe option. Turning off . Supports both block blobs (suitable for most use cases, such as MapReduce) and page blobs (suitable for continuous write use cases, such as an HBase write-ahead log).
Reference file system paths using URLs using the wasb scheme. In the recent blog post about the Apache HBase Write Path, we talked about the write-ahead-log (WAL), which plays an important role in preventing data loss should a HBase region server failure occur.
This blog post describes how HBase prevents data loss after a region server crashes, using an.