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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.
Central part to HLog's functionality is the append method, which internally eventually calls doWrite. 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.
What we are missing though is where the KeyValue belongs to, i. That is stored in the HLogKey. What is also stored is the above sequence number. With each record that number is incremented to be able to keep a sequential order of edits. Finally it records the "Write Time", a time stamp to record when the edit was written to the log.HBase Architecture Write-Ahead Log.
What is the write-ahead log (WAL), you ask? In a previous article we looked at the general storage architecture of HBase. One thing that was mentioned was the WAL.
This post explains how the log works in detail, but bear in mind that it describes the current version, which is Turning this off means that the RegionServer will not write the Put to the Write Ahead Log When writing a lot of data to an HBase table from a MR job (e.g., with TableOutputFormat), and specifically where Puts are being emitted from the Mapper, skip the Reducer step.
When a Reducer step is used, all of the output (Puts) from the . Jul 12, · But System Admin team at my company mentioned about Compactions in MapR-DB.
In HBase you can disable the WAL (Write Ahead Log) which will give you better performance, however you create the potential for data loss. In HBase you can disable the WAL (Write Ahead Log) which will give you better performance, however Reviews: 5.
Get details on HBase’s architecture, including the storage format, write-ahead log, background processes, and more Integrate HBase with Hadoop’s MapReduce framework for massively parallelized data processing jobs.
Get details on HBase’s architecture, including the storage format, write-ahead log, background processes, and more Integrate HBase with Hadoop's MapReduce framework for massively parallelized data processing jobs.
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