Hbase write ahead log performance appraisal

Rittman Mead Consulting - Fri, Before we get into the detail, I'll first explain a bit about the background to the requirement and the options that ship with OBIEE. Why Concurrent Development The benefits of concurrent development are obvious: It enables you to scale your development team to meet the delivery demands of the business.

Hbase write ahead log performance appraisal

That processing is built as Hive queries that read from and write back to the same HBase table. And while it was working fine on small test datasets, it all blew up once I moved the process to the full dataset.

When the mappers were running they would eventually fail repeatedly, ultimately killing the job with an error that looks like this: In both cases mappers are assigned a split to scan based on TableInputFormat just like our Hive jobsand as they scan they simply put the record out to the new table.

The logs First stop - the logs. The regionservers are littered with lines like the following: After a little more digging and testing with a variety of hbase.

Normally the memstore should flush when it reaches the flush. A new store file is created every time the memstore flushes, and their number is reduced by compacting them into fewer, bigger storefiles during minor and major compactions. By default, compactions will only start if there are at least hbase.

The acceptable levels of heap usage are defined by hbase. There is a thread dedicated to flushing that wakes up regularly and checks these limits: But at this point we only knew the different dials we could turn.

The most obvious problem is that we have too many storefiles, which appears to be a combination of producing too many of them and not compacting them fast enough. This still produced too many storefiles and caused the same blocking maybe a little better than at 64MB then tried MB with multiplier of 4.

The logs and ganglia showed that the flushes were happening well before MB still around MB "due to global heap pressure" - a sign that total memstores were consuming too much heap. So how to reduce the number of storefiles? This meant increasing the setting hbase.

It was starting to look good - the number of "Blocking updates" log messages dropped to a handful per run, but it was still enough to affect one or two jobs to the point of them getting killed. Things looked kind of grim.

hbase write ahead log performance appraisal

After endless poring over ganglia charts, we kept coming back to one unexplained blip that seemed to coincide with the start of the storefile explosion that eventually killed the jobs.

Average memstore flush size over time At about the halfway point of the jobs the size of memstore flushes would spike and then gradually increase until the job died. Keep in mind that the chart shows averages, and it only took a few of those flushes to wait for storefiles long enough to fill to 1GB and then start the blocking that was our undoing.

Back to the logs. From the start of Figure 1 we can see that things appear to be going smoothly - the memstores are flushing at or just above MB, which means they have enough heap and are doing their jobs.

From the logs we see the flushes happening fine, but there are regular lines like the following: Under global heap pressure: Then starting from around Flush thread woke up with memory above low water. So what we have is memstores initially being forced to flush because of minor heap pressure adds storefiles faster than we can compact.

Then we have memstores delaying flushes because of too many storefiles memstores start getting bigger - our graph spike. Then the write ahead log WAL complains about too many of its logs, which forces a memstore flush so that the WAL HLog can be safely discarded - this again adds storefiles.

hbase write ahead log performance appraisal

And for good measure the flushing thread now wakes up, finds its out of heap, and starts attempting flushes, which just aggravates the problem adding more storefiles to the pile.Big Data has the fueling capacity for the transformation of world to the digital world.

The word “Big Data” is itself misleading one. It gives us an illusion as if data after certain size is. Job Detail. Desktop Rollout Engineer - Swan Hill VIC. Monitor Installation; Write and/or modify and/or execute existing SAS code in accordance with specifications provided.

Demonstrated experience implementing and maintaining IPS/IDS, SIEM, Central Log management. Review: HBase is massively scalable -- and hugely complex HBase is optimized for read performance. For writes, HBase seeks to maintain consistency. (a "write-ahead log"), then to an.

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