HBase 0.98 Monitoring Support

HBase is a popular open-source, non-relational (NoSQL), column-oriented, distributed database that runs on top of the Hadoop Distributed File System (HDFS).  HBase is well suited for sparse data sets, which are common in many big data use cases.  Fortunately for all its users, SPM now supports monitoring, alerting and anomaly detection for HBase version 0.98.  Even those of you not running version 0.98 (here are the results for our HBase version distribution poll) are still in luck because a lot of HBase metrics captured by SPM are also in 0.94.x, 0.96.x, and even the recently released 1.0 version.  That said, HBase is one of those projects whose metrics change from version to version – some are deletes, some are added, others are modified.  If you have your own tools for monitoring HBase and are trying to monitor more than just the most basic HBase metrics, maintaining those tools must not be fun. Related to this common issue, we recently put together a “Build vs. Buy” post that weighs the pros and cons.

Here at Sematext we make heavy use of HBase.  We have recently moved from 0.94.x to 0.98.x and have been enjoying all its benefits.  Furthermore, we’ve recently updated SPM for HBase to monitor a pile of new HBase metrics.  Of course, we eat our own dog food and immediately got new and interesting insights about our own HBase clusters through some of the new metric charts.

For example, from https://apps.sematext.com/spm-reports/s/VhOltU14Cy we are now able to see the dramatic impact of major compactions on data locality (and thus HBase performance!):   (click to enlarge)

Local_Files_1

And from https://apps.sematext.com/spm-reports/s/7LU1qvs7ur we can see the number and size of HLog files over time:   (click to enlarge)

HLog

Alright, on to all the details!

Shiny, New HBase Metrics

In total, we’re talking 290 metrics: 195 for 0.98 and 95 for previous versions.  And lots of them changed in 0.98.  Here’s a summary of top-level SPM reports.  Each report listed below has one or more charts with one or more HBase metrics.  Juicy stuff.

Master:

  • Servers
  • Assign Manager
  • Balancer
  • FS
  • Snapshot

Region Server:

  • Regions & Stores
  • Requests
  • Files
  • Compact & Flush
  • Cache
  • Operations
  • Check & Mutate
  • WAL
  • Hedged Reads
  • MOB
  • Replication
  • Replication Source

Common / pre-0.98:

  • IPC
  • HBase JVM
  • UGI
  • Requests (pre-0.98)
  • Regions (pre-0.98)
  • Split (pre-0.98)
  • Memstore (pre-0.98)
  • Store (pre-0.98)
  • Compactions (pre-0.98)
  • FS (pre-0.98)
  • Block Cache (pre-0.98)

Screenshot: HBase Operation Calls & Time  (click to enlarge)

HBase_Ops_calls

Screenshot: HBase Slow Operations  (click to enlarge)

HBase_Slow_Ops

Screenshot: HBase Sync & Append Ops & Time  (click to enlarge)

HBase_Sync_Apend_Ops_Time

OK OK, how do I get all this stuff?

If you are not using SPM yet, simply sign up, create your first SPM App, and follow the directions in the UI.  You should see all your HBase metrics in a matter of minutes.  SPM is free for 30 days, requires no commitment or credit card and has no limit.  On Premises version is available as well.

If you are already using SPM, but not monitoring HBase, just create the SPM App for HBase, and follow the directions for installing the SPM agent on your HBase nodes.

If you are already using SPM for monitoring HBase, you just need to upgrade the SPM agent and configure it.

Poll Results: HBase Version Distribution

The results for HBase version distribution poll are in.  Thanks to everyone who took the time to vote!

The distribution pie chart is below, but we could summarize it as follows:

  • A big chunk of HBase clusters, about 30%, are still “stuck” on HBase 0.94.x
  • Over 37% of the HBase clusters are on 0.98.x that, until very recently, was the latest stable version
  • Only about 7% of clusters are on the 0.96.x and we can assume these clusters will soon migrate to either 0.98.x or 1.0.x
  • Somewhat surprisingly, almost 20% of HBase clusters are already on HBase 1.0.0 even though 1.0.0 was released only a few weeks ago

It’s great to see so many clusters moving to 1.0.0 so quickly! As for why there are still so many clusters using 0.94.x, which is several years old, see this comment on the HBase mailing list.  Here at Sematext we make heavy use of HBase and were on 0.94.x version for a long time, too.  A few months ago we’ve moved to 0.98.x and have been enjoying all its benefits.  Furthermore, we’ve recently updated SPM for HBase to monitor a pile of new HBase metrics that provide interesting new insights about our HBase clusters though some of the new metric charts.  For example, we are now able to see the dramatic impact of major compactions on data locality (and thus HBase performance!) — see for yourself – https://apps.sematext.com/spm-reports/s/VhOltU14Cy, or the number and size of HLog files over time — https://apps.sematext.com/spm-reports/s/7LU1qvs7ur.

HBase version distribution

Apache HBase Version Distribution

You may also want to check out the results of our other polls about big data technologies.

HBase Poll: Version You Run?

We are updating SPM for HBase to make sure SPM collects all the key HBase metrics that were added in 0.98, we thought it would be good to see which HBase versions are being used in the wild.  We’re on 0.98 after being on 0.94 for a long time.  How about you?

Please tweet this poll and help us spread the word, so we can get a good, statistically significant results.  We’ll publish the results here and via @sematext (follow us!) in a week.

Please tweet this poll and help us spread the word, so we can get a good, statistically significant results.  We’ll publish the results here and via @sematext (follow us!) in a week.

HBaseWD and HBaseHUT: Handy HBase Libraries Available in Public Maven Repo

HBaseWD is aimed to help distribute writes of records with sequential row keys in HBase (and avoid RegionServer hotspotting). Good introduction can be found here.

We recently published 0.1.0 version of the library to Sonatype public maven repository. Thus, integration in your project became much easier:

  <repositories>
    <repository>
      <id>sonatype release</id>
      <url>https://oss.sonatype.org/content/repositories/releases/</url>
    </repository>
  </repositories>
  <dependency>
    <groupId>com.sematext.hbasewd</groupId>
    <artifactId>hbasewd</artifactId>
    <version>0.1.0</version>
  </dependency>

HBaseHUT is aimed to help in situations when you need to update a lot of records in HBase in read-modify-write style. Good introduction can be found here.

We recently published 0.1.0 version of this library to Sonatype public maven repository too. Integration info:

  <repositories>
    <repository>
      <id>sonatype release</id>
      <url>https://oss.sonatype.org/content/repositories/releases/</url>
    </repository>
  </repositories>
  <dependency>
    <groupId>com.sematext.hbasehut</groupId>
    <artifactId>hbasehut</artifactId>
    <version>0.1.0</version>
  </dependency>

For running (MR jobs) on hadoop-2.0+ (which is a part of CDH4.1+) use 0.1.0-hadoop-2.0 version:

  <dependency>
    <groupId>com.sematext.hbasehut</groupId>
    <artifactId>hbasehut</artifactId>
    <version>0.1.0-hadoop-2.0</version>
  </dependency>

Thank you to all contributors and users of the libraries!

SPM Discountorama Announcement

We are happy to announce the General Availability of SPM, our performance monitoring solution for Apache Solr, ElasticSearch, HBase, SenseiDB, and Java applications, and of course all system metrics. You can also vote for what else you want SPM to monitor.  Over the last N months that we’ve been running SPM we’ve received a lot of good feedback (thanks!), a lot of words of encouragement (thanks!), and even a few nice quotes (another thanks!). Here is one from Jerry Yang, a Software Engineer at Walmart Labs: “I have been using SPM for couple of days and it has been amazing. I learned a lot about my Solr services and was able to optimize based on the results on SPM. Great work.”

Discount Codes

Since holiday season is coming up, we thought we’d offer some discounts every week between now until the end of the year.  Each of the following discounts can be used only during “its week” specified below.  There is a limit to the number of people who can use each discount, so if you want it, don’t waste too much time.  Each discount will reduce the price of SPM SaaS for 365 days after you’ve used it, which effectively means you will get discount until the end of 2013.  Note that when you register for SPM you do not need to enter your credit card information.  You also don’t need to provide it when you create the SPM application for the system you want to monitor.  And it is when you create your SPM application that you can enter the discount code.

  • 20% for the remainder of this week until the end of this Sunday, December 9: NY201320
  • 15% for the week of December 10, 2012: NY201315
  • 10% for the week of December 17, 2012: NY201310
  • 5% for the week of December 24, 2012: NY201305

Note that each discount code expires on Sunday at 00:00 UTC.

SPM Flavours

The above discounts are good for our SPM SaaS.  However, if you’d rather run SPM on your own servers, we do offer SPM on Premises – please get in touch if you are interested in the on premises version.  You can also vote for SPM SaaS vs. On Premise and that way tell us which version you prefer or want.

SPM Plans

There are a few different subscription plans available in SPM SaaS:

  • Basic plan that is free and shows the last 30 minutes of performance data
  • Standard plan that shows the last 30 days of data and costs $0.035/server/hour
  • Pro plan that shows the last 60 days of performance data and costs $0.070/server/hour

If you have not used SPM before, here is what you can expect to see – click on the image to see a large, non-fuzzy version:

We hope you will find SPM useful and fun to use.  We are always looking for feedback – just email spm-support@sematext.com or ping @sematext and tell us what you like or don’t like about SPM.

HBase FuzzyRowFilter: Alternative to Secondary Indexes

In this post we’ll explain the usage of FuzzyRowFilter which can help in many situations where secondary indexes solutions seems to be the only choice to avoid full table scans.

Background

When it comes to HBase the way you design your row key affects everything. It is a common pattern to have composite row key which consists of several parts, e.g. userId_actionId_timestamp. This allows for fast fetching of rows (or single row) based on start/stop row keys which have to be a prefix of the row keys you want to select. E.g. one may select last time of userX logged in by specifying row key prefix “userX_login_”. Or last action of userX by fetching the first row with prefix “userX_”. These partial row key scans work very fast and does not require scanning the whole table: HBase storage is optimized to make them fast.

Problem

However, there are cases when you need to fetch data based on key parts which happen to be in the middle of the row key. In the example above you may want to find last logged in users. When you don’t know the first parts of the key partial row key scan turns into full table scan which might be very slow and resource intensive.

Possible Solution #1: Data Redundancy

One possible way around it would be to use secondary indexes by creating redundant rows with the same data as original ones but with different sequence of the parts of the key (e.g. actionId_timestamp). This solution may not be suitable for some because of its cons:

  • storing extra indexes (usually it requires to store N times more data for N indexes) results in storing a lot more data on disk
  • storing (and serving) extra rows brings additional load on the cluster during writing and reading (extra blocks fighting to be in cache, etc.)
  • writing/updating/deleting several rows is not an atomic operation in HBase

Possible Solution #2: Integrated Secondary Indexes

Another way to attack the problem is to use smart secondary indexes mechanism integrated in HBase which doesn’t rely on data redundancy. E.g. something like IHBase. The problem here is that there’s no out-of-the box solution to be used. This may change with addition of newer CoProcessors functionality (see e.g. HBASE-2038 or this). But as of now existent solutions have their own limitations and drawbacks while new solutions are yet to be completed.

Suggested Solution

First of all, I have to say that solution suggested below is not a silver bullet. Moreover its performance may be very bad and even be close to full table scan in some cases. Even more: it can’t be used in any of the situations described in Background and Problem sections. But in many cases depending on your data the suggested simple solution can be used to avoid secondary indexes burden and still allow for very fast scans. In many other cases it can be used to significantly speed up your full table scans.

Suggested solution is not new and quite simple, but it is usually overlooked by HBase users, though it shouldn’t be.

Fast-Forwarding in Server-side Filter

In recent HBase versions (I believe in 0.90.+) there’s a mechanism that allows skipping the whole range of rows when scanning with server-side filter. These skipped rows data may not even be read from the disk. Based on the current row key the filter can tell scanner to advance to the row with the specific key and by doing that jump over many rows which are simply skipped. For example, this makes it possible to perform fast full-table scans (or large partial key scans) in case there’s enough information about the key and the data that allows to provide efficient hints for skipping a lot of rows during the scan.

Most of the time you’ll have to implement your own custom filter that performs fast-forwarding. Hint for these cases: refer to org.apache.hadoop.hbase.filter.Filter.ReturnCode.SEEK_NEXT_USING_HINT in HBase sources.

FuzzyRowFilter

FuzzyRowFilter is one of the handy filters which is available and which performs fast-forwaring based on the fuzzy row key mask provided by user. It will be available out of the box in the next HBase release, but you can now download its sources from HBASE-6509 (use latest patch) and use it as any other custom filter (there’s no need to patch HBase, etc. it relies on existing functionality).

FuzzyRowFilter takes as parameters row key and a mask info. In example above, in case we want to find last logged in users and row key format is userId_actionId_timestamp (where userId has fixed length of say 4 chars), the fuzzy row key we are looking for is “????_login_”. This translates into the following params for FuzzyRowKey:

FuzzyRowFilter rowFilter = new FuzzyRowFilter(
 Arrays.asList(
  new Pair<byte[], byte[]>(
    Bytes.toBytesBinary("\\x00\\x00\\x00\\x00_login_"),
    new byte[] {1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0})));

I.e. the row key to compare with is provided as the first byte array (at the byte positions where any value is allowed, “\x00″ is set, which is translated into (byte) 0). To tell which positions are fixed and which are not fixed, second byte array is provided (mask info) with zeroes on positions whose values are “fixed” and ones at the “non-fixed” positions.

Thus one can define different fuzzy row key masks, including those with “non-fixed” positions anywhere in the middle of the key. E.g.: “hb?se” or “??pred?ce”.

Note that FuzzyRowFilter accepts more than one mask: if row key satisfies at least one, the row will be included in the result. E.g. by providing masks “????_login_” and “????_register_” we can find last logged in and registered users.

How It Works

In the example above, with the mask “????_login_” scan initially navigates to the first row of the table. It is likely to be a user-action record (let userId to be “0001”), but the action may be not “login”. In this case as filter knows the “current” user (“0001″) and the action it is looking for, filter tells scan to jump to the row with the key “0001_login_”. By doing that, many rows may be skipped from the scanning (if we track other user actions apart from “login”, there are likely a lot more other user-action records than user logins). Then it scans user login actions records until it faces the record with action which is not login, say “0001_logout”. In this case filter knows that there’s no point in scanning this user’s records and tells scanner to jump to the next user “0002_login_” and it will continue scanning its records. Note: there might be no “0002” user, filter knows nothing about users, it simply suggests the next user id by increasing the current one by one. In this case scan will automatically jump to the next existing user, and the steps above will be repeated.

Limitations & Performance Considerations

As you probably already have figured out from the example above, FuzzyRowFilter can be applied only if userId has fixed length. While it is  usually not hard to design the row key format so that its parts have fixed length (at least those parts that we need to mask with “???”), in many situations it may be problematic.

The efficiency of using FuzzyRowFilter (and any other fast-forwarding filters) is determined by how many records filter can actually skip and how many jumps it has to do to skip them.

Performance of the scan based on FuzzyRowFilter usually depends on the cardinality of the fuzzy part. E.g. in the example above, if users number is several hundreds to several thousand, the scan should be very fast: there will only be several hundreds or thousand “jumps” and huge amount of rows might be skipped. If the cardinality is high then scan can take a lot of time. The worst-case scenario is when you have N records and N users, i.e. one record per user. In this case there’s simply nothing to skip.

At times when the performance of full-table scan with the help of FuzzyRowFilter is not suitable for serving online data, it has still proven to be very efficient when you feed data from HBase into MapReduce job. Don’t overlook this!

Summary

There are times when you design the row key for the data to be stored in HBase and feel the need for the secondary indexes, because of very different data access patterns. In this situation consider relying on FuzzyRowFilter for some of the data reading use-cases. Depending on your data with small adjustments of the row key format (sometimes it is not even needed) you can benefit from very fast fetching of records where before you needed to perform full table scans or very large partial key scans.

Plug: if this sort of stuff interests you, we are hiring people who know and love to work with Hadoop, HBase, MapReduce…

@abaranau

Announcing HBase Refcard

We’re happy to announce the very first HBase Refcard proudly authored by two guys from Sematext.  We hope people will find the HBase Refcard useful in their work with HBase, along with the wonderful Apache HBase Reference Guide.  If you think the refcard is missing some important piece of information that deserves to be included or that it contains superfluous content, please do let us know! (e.g., via comments here)

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