Initial study on apache lucene

Today, we are going to learn apache lucene. So first thing first, what is apache lucene?

Apache Lucene is a free open source information retrieval software library, originally written in Java by Doug Cutting. It is supported by the Apache Software Foundation and is released under the Apache Software License.

Let's go into apache lucene "hello world", so we get an basic idea what is it. Go to the offical site and download the latest release. Below is the tutorial I follow from the official documentation, and using apache lucene version 4.10.3 with oracle java 7 with slight modification to the tutorial.

jason@localhost:~/Desktop/lucene-4.10.3$ java -cp ./core/lucene-core-4.10.3.jar:./queryparser/lucene-queryparser-4.10.3.jar:./analysis/common/lucene-analyzers-common-4.10.3.jar:./demo/lucene-demo-4.10.3.jar org.apache.lucene.demo.IndexFiles
Usage: java org.apache.lucene.demo.IndexFiles [-index INDEX_PATH] [-docs DOCS_PATH] [-update]

This indexes the documents in DOCS_PATH, creating a Lucene indexin INDEX_PATH that can be searched with SearchFiles
jason@localhost:~/Desktop/lucene-4.10.3$ java -cp ./core/lucene-core-4.10.3.jar:./queryparser/lucene-queryparser-4.10.3.jar:./analysis/common/lucene-analyzers-common-4.10.3.jar:./demo/lucene-demo-4.10.3.jar org.apache.lucene.demo.IndexFiles -index data/ -docs docs/
Indexing to directory 'data/'...
adding docs/grouping/constant-values.html
adding docs/grouping/index.html
adding docs/grouping/allclasses-noframe.html
adding docs/grouping/overview-frame.html
adding docs/grouping/org/apache/lucene/search/grouping/AbstractGroupFacetCollector.html
...
...
...
adding docs/analyzers-phonetic/deprecated-list.html
adding docs/analyzers-phonetic/package-list
adding docs/analyzers-phonetic/allclasses-frame.html
95794 total milliseconds
jason@localhost:~/Desktop/lucene-4.10.3$ uptime
 21:10:16 up 16:44, 23 users,  load average: 5.45, 4.49, 3.59

As you can see, instead of indexing the source of java class file, I index the javadoc in html format and it works nicely. Although my system is loaded but the index still reasonably quick. Apache lucene finish index within 95seconds for a total of 5818 files. After index are done, if you do a list on the directory data, you will notice the lucene index files. If you want to go into details what are these files before, you should read this documentation.

jason@localhost:~/Desktop/lucene-4.10.3$ ls -l data/
total 13784
-rw-r--r-- 1 jason jason      284 Jan 13 21:07 _0.cfe
-rw-r--r-- 1 jason jason 12387776 Jan 13 21:07 _0.cfs
-rw-r--r-- 1 jason jason      242 Jan 13 21:07 _0.si
-rw-r--r-- 1 jason jason      284 Jan 13 21:07 _1.cfe
-rw-r--r-- 1 jason jason  1677329 Jan 13 21:07 _1.cfs
-rw-r--r-- 1 jason jason      242 Jan 13 21:07 _1.si
-rw-r--r-- 1 jason jason      151 Jan 13 21:07 segments_1
-rw-r--r-- 1 jason jason       36 Jan 13 21:07 segments.gen
-rw-r--r-- 1 jason jason        0 Jan 13 21:06 write.lock

Okay, now to the search.

jason@localhost:~/Desktop/lucene-4.10.3$ java -cp ./core/lucene-core-4.10.3.jar:./queryparser/lucene-queryparser-4.10.3.jar:./analysis/common/lucene-analyzers-common-4.10.3.jar:./demo/lucene-demo-4.10.3.jar  org.apache.lucene.demo.SearchFiles
Exception in thread "main" org.apache.lucene.store.NoSuchDirectoryException: directory '/home/jason/Desktop/lucene-4.10.3/index' does not exist
	at org.apache.lucene.store.FSDirectory.listAll(FSDirectory.java:218)
	at org.apache.lucene.store.FSDirectory.listAll(FSDirectory.java:242)
	at org.apache.lucene.index.SegmentInfos$FindSegmentsFile.run(SegmentInfos.java:801)
	at org.apache.lucene.index.StandardDirectoryReader.open(StandardDirectoryReader.java:53)
	at org.apache.lucene.index.DirectoryReader.open(DirectoryReader.java:67)
	at org.apache.lucene.demo.SearchFiles.main(SearchFiles.java:91)
jason@localhost:~/Desktop/lucene-4.10.3$ java -cp ./core/lucene-core-4.10.3.jar:./queryparser/lucene-queryparser-4.10.3.jar:./analysis/common/lucene-analyzers-common-4.10.3.jar:./demo/lucene-demo-4.10.3.jar org.apache.lucene.demo.SearchFiles --help
Exception in thread "main" org.apache.lucene.store.NoSuchDirectoryException: directory '/home/jason/Desktop/lucene-4.10.3/index' does not exist
	at org.apache.lucene.store.FSDirectory.listAll(FSDirectory.java:218)
	at org.apache.lucene.store.FSDirectory.listAll(FSDirectory.java:242)
	at org.apache.lucene.index.SegmentInfos$FindSegmentsFile.run(SegmentInfos.java:801)
	at org.apache.lucene.index.StandardDirectoryReader.open(StandardDirectoryReader.java:53)
	at org.apache.lucene.index.DirectoryReader.open(DirectoryReader.java:67)
	at org.apache.lucene.demo.SearchFiles.main(SearchFiles.java:91)
jason@localhost:~/Desktop/lucene-4.10.3$ java -cp ./core/lucene-core-4.10.3.jar:./queryparser/lucene-queryparser-4.10.3.jar:./analysis/common/lucene-analyzers-common-4.10.3.jar:./demo/lucene-demo-4.10.3.jar org.apache.lucene.demo.SearchFiles -h
Usage:	java org.apache.lucene.demo.SearchFiles [-index dir] [-field f] [-repeat n] [-queries file] [-query string] [-raw] [-paging hitsPerPage]

See http://lucene.apache.org/core/4_1_0/demo/ for details.
jason@localhost:~/Desktop/lucene-4.10.3$ java -cp ./core/lucene-core-4.10.3.jar:./queryparser/lucene-queryparser-4.10.3.jar:./analysis/common/lucene-analyzers-common-4.10.3.jar:./demo/lucene-demo-4.10.3.jar org.apache.lucene.demo.SearchFiles -index data 
Enter query: 
string
Searching for: string
1674 total matching documents
1. docs/benchmark/org/apache/lucene/benchmark/byTask/utils/Format.html
2. docs/analyzers-common/org/apache/lucene/analysis/util/AbstractAnalysisFactory.html
3. docs/queryparser/deprecated-list.html
4. docs/queryparser/org/apache/lucene/queryparser/classic/class-use/ParseException.html
5. docs/queryparser/org/apache/lucene/queryparser/flexible/core/messages/QueryParserMessages.html
6. docs/core/org/apache/lucene/index/IndexFileNames.html
7. docs/analyzers-stempel/org/egothor/stemmer/Diff.html
8. docs/queryparser/org/apache/lucene/queryparser/ext/Extensions.html
9. docs/facet/org/apache/lucene/facet/FacetsConfig.     html
10. docs/queryparser/org/apache/lucene/queryparser/flexible/messages/package-summary.html
Press (n)ext page, (q)uit or enter number to jump to a page.
n
11. docs/highlighter/org/apache/lucene/search/highlight/class-use/InvalidTokenOffsetsException.html
12. docs/queryparser/org/apache/lucene/queryparser/xml/DOMUtils.html
13. docs/queryparser/org/apache/lucene/queryparser/classic/MultiFieldQueryParser.html
14. docs/core/org/apache/lucene/index/SegmentInfo.html
15. docs/highlighter/org/apache/lucene/search/vectorhighlight/FragmentsBuilder.html
16. docs/highlighter/org/apache/lucene/search/vectorhighlight/class-use/FieldFragList.html
17. docs/highlighter/org/apache/lucene/search/vectorhighlight/BaseFragmentsBuilder.html
18. docs/queryparser/org/apache/lucene/queryparser/flexible/standard/QueryParserUtil.html
19. docs/highlighter/org/apache/lucene/search/highlight/GradientFormatter.html
20. docs/highlighter/org/apache/lucene/search/postingshighlight/PostingsHighlighter.html
Press (p)revious page, (n)ext page, (q)uit or enter number to jump to a page.
q
Enter query: 
quit
Searching for: quit
2 total matching documents
1. docs/demo/src-html/org/apache/lucene/demo/SearchFiles.html
2. docs/changes/Changes.html
Press (q)uit or enter number to jump to a page.
q
Enter query: 
^Cjason@localhost:~/Desktop/lucene-4.10.3$

The search is quick even though in the loaded system. That's it, a light learning experience on apache lucene.

How to setup software raid mirroring for disks on xubuntu

Over a course period of time, disk stop working in a computer is to be expected and if it does, then all the data is lost. Oh no, that's not good! In this article, we will take a look on mirroring the data from a disk to another disk using software, and so the data are duplicated on at least two disks. This will reduced the data loss risk by 50%! There is also hardware raid but in this article, we will look into software raid. Specifically software raid one, that is mirroring. For detail explanation of software raid one, please read on this link  but for a shorter explanation, it is basically save the data into two disk at once and read from two disk.

This article assumed you have two disks with same storage capacity and only one partition per disk and this one partition occupied the whole disk size. So the operating system detected both disks as sda and sdb. Let's start to partition them first. Note, create partition will make your current data lost and make sure you backup your data somewhere else safely before continue.

root@localhost:~# fdisk /dev/sdb 

Welcome to fdisk (util-linux 2.25.1).
Changes will remain in memory only, until you decide to write them.
Be careful before using the write command.


Command (m for help): p
Disk /dev/sdb: 465.8 GiB, 500107862016 bytes, 976773168 sectors
Units: sectors of 1 * 512 = 512 bytes
Sector size (logical/physical): 512 bytes / 512 bytes
I/O size (minimum/optimal): 512 bytes / 512 bytes
Disklabel type: dos
Disk identifier: 0x00053dc0

Device     Boot Start       End   Sectors   Size Id Type
/dev/sdb1        2048 976773119 976771072 465.8G fd Linux raid autodetect

As you can see above, this is supposed to be the end result it should be. You can type m for help. To create a partition, this is your homework, but as a hints, you need add a new partition, with only 1 partition and used all all the cylinder. Then you need to change the disk partition type to Linux raid auto and remember to save the change you made so fdisk will write the partition and partition type to the disk.

Repeat this procedure for another disk, sdc. The partition information of sdc should be identical to sdb above. Note, you can use fdisk -l /dev/sdb and fdisk -l /dev/sdc to verify the disk is changed accordingly.

root@localhost:~# fdisk /dev/sdc

Welcome to fdisk (util-linux 2.25.1).
Changes will remain in memory only, until you decide to write them.
Be careful before using the write command.


Command (m for help): p
Disk /dev/sdc: 465.8 GiB, 500107862016 bytes, 976773168 sectors
Units: sectors of 1 * 512 = 512 bytes
Sector size (logical/physical): 512 bytes / 4096 bytes
I/O size (minimum/optimal): 4096 bytes / 4096 bytes
Disklabel type: dos
Disk identifier: 0x00019748

Device     Boot Start       End   Sectors   Size Id Type
/dev/sdc1        2048 976773119 976771072 465.8G fd Linux raid autodetect

If you do not have mdadm install, you should install it now. To install mdadm, it is as easy as apt-get install mdadm. mdadm is a Linux utility used to manage software RAID devices.

After mdadm is installed, then it is time to add that two partition into mdadm. To do that, issue the following command.

# mdadm --create /dev/md0 --level=mirror --raid-devices=2 /dev/sdb1 /dev/sdc1

The above commend should return immediately and now you can format the new block device using the command.

# mkfs.ext4 /dev/md0

By now, the disk will be formatted to ext4 filesystem and you can check the progress using command cat /proc/mdstat. You can also check the raid detail using this command mdadm --detail /dev/md0 .

root@localhost:~# mdadm --detail /dev/md0
/dev/md0:
        Version : 1.2
  Creation Time : Fri Dec 12 03:54:49 2014
     Raid Level : raid1
     Array Size : 488254464 (465.64 GiB 499.97 GB)
  Used Dev Size : 488254464 (465.64 GiB 499.97 GB)
   Raid Devices : 2
  Total Devices : 2
    Persistence : Superblock is persistent

  Intent Bitmap : Internal

    Update Time : Thu Jan  8 21:42:16 2015
          State : active 
 Active Devices : 2
Working Devices : 2
 Failed Devices : 0
  Spare Devices : 0

           Name : 
           UUID : be4c04c4:349da5d9:cbcd7313:7ec7cf60
         Events : 26492

    Number   Major   Minor   RaidDevice State
       0       8       17        0      active sync   /dev/sdb1
       1       8       33        1      active sync   /dev/sdc1

When the disk is done formatted, you should be able to see output like the following.

root@localhost:~# cat /proc/mdstat 
Personalities : [raid1] [linear] [multipath] [raid0] [raid6] [raid5] [raid4] [raid10] 
md0 : active raid1 sdc1[1] sdb1[0]
      488254464 blocks super 1.2 [2/2] [UU]
      bitmap: 4/4 pages [16KB], 65536KB chunk

unused devices: <none>

Note the UU, if the raid is degraded, like a disk failure, you should be able to see [_U] or [U_] depending on which disk is failing.

The last step is to mount this new device to a mount point so that we can start to use. The example below create a mount point on /mnt/myBackup and mount md0 to /mnt/myBackup

root@localhost:~# mkdir /mnt/myBackup
root@localhost:~# mount /dev/md0 /mnt/myBackup

To make this change survive over a reboot, you should add an entry into /etc/fstab.

/dev/md0 /mnt/myBackup ext4 defaults 1 2

You should also save the raid configuration into mdadm configuration file. The following command does just that.

root@localhost:~# mdadm --detail --scan > /etc/mdadm/mdadm.conf

That's it, I hope your data are save from now on.

Initial study to docker

Docker making so much fuss lately and today we are going to look into Docker. Let's start something basic, what actually is a docker? According to the definition from official site,

Docker is an open platform for developers and sysadmins to build, ship, and run distributed applications. Consisting of Docker Engine, a portable, lightweight runtime and packaging tool, and Docker Hub, a cloud service for sharing applications and automating workflows, Docker enables apps to be quickly assembled from components and eliminates the friction between development, QA, and production environments. As a result, IT can ship faster and run the same app, unchanged, on laptops, data center VMs, and any cloud.

and explanation from wikipedia

Docker is an open-source project that automates the deployment of applications inside software containers, by providing an additional layer of abstraction and automation of operating system–level virtualization on Linux.[2] Docker uses resource isolation features of the Linux kernel such as cgroups and kernel namespaces to allow independent "containers" to run within a single Linux instance, avoiding the overhead of starting virtual machines.[3]

Okay, that's the theory. If you want to quickly get an idea how docker work, you can try it here!

For people who has run virtual machine environment before, it may seem, hey isn't this very similar to the current virtual machine? But they are not the same really. See the software stack below virtual machines versus docker.



Next, we will install docker locally and the below illustration is using debian sid. If you run other linux distribution, you should read this page. First we will install and then start bash in the ubuntu container. Note that when pulling ubuntu image down, may take sometime which depending on your internet speed.

root@localhost:~# apt-get install docker.io
Reading package lists... Done
Building dependency tree       
Reading state information... Done
The following extra packages will be installed:
  aufs-tools cgroupfs-mount libnih-dbus1 libnih1 makedev mountall plymouth
Suggested packages:
  btrfs-tools debootstrap lxc rinse plymouth-themes
The following NEW packages will be installed:
  aufs-tools cgroupfs-mount docker.io libnih-dbus1 libnih1 makedev mountall plymouth
0 upgraded, 8 newly installed, 0 to remove and 557 not upgraded.
Need to get 4,360 kB of archives.
After this operation, 21.6 MB of additional disk space will be used.
Do you want to continue? [Y/n] Y
Get:1 http://cdn.debian.net/debian/ unstable/main makedev all 2.3.1-93 [42.6 kB]
Get:2 http://cdn.debian.net/debian/ unstable/main plymouth amd64 0.9.0-9 [189 kB]
Get:3 http://cdn.debian.net/debian/ unstable/main libnih1 amd64 1.0.3-4.3 [127 kB]
Get:4 http://cdn.debian.net/debian/ unstable/main libnih-dbus1 amd64 1.0.3-4.3 [97.1 kB]
Get:5 http://cdn.debian.net/debian/ unstable/main mountall amd64 2.54 [68.3 kB]
Get:6 http://cdn.debian.net/debian/ unstable/main aufs-tools amd64 1:3.2+20130722-1.1 [92.9 kB]
Get:7 http://cdn.debian.net/debian/ unstable/main cgroupfs-mount all 1.1 [4,572 B]
Get:8 http://cdn.debian.net/debian/ unstable/main docker.io amd64 1.3.3~dfsg1-2 [3,739 kB]
Fetched 4,360 kB in 44s (97.8 kB/s)
Selecting previously unselected package makedev.
(Reading database ... 324961 files and directories currently installed.)
Preparing to unpack .../makedev_2.3.1-93_all.deb ...
Unpacking makedev (2.3.1-93) ...
Selecting previously unselected package plymouth.
Preparing to unpack .../plymouth_0.9.0-9_amd64.deb ...
Unpacking plymouth (0.9.0-9) ...
Selecting previously unselected package libnih1.
Preparing to unpack .../libnih1_1.0.3-4.3_amd64.deb ...
Unpacking libnih1 (1.0.3-4.3) ...
Selecting previously unselected package libnih-dbus1.
Preparing to unpack .../libnih-dbus1_1.0.3-4.3_amd64.deb ...
Unpacking libnih-dbus1 (1.0.3-4.3) ...
Selecting previously unselected package mountall.
Preparing to unpack .../mountall_2.54_amd64.deb ...
Unpacking mountall (2.54) ...
Selecting previously unselected package aufs-tools.
Preparing to unpack .../aufs-tools_1%3a3.2+20130722-1.1_amd64.deb ...
Unpacking aufs-tools (1:3.2+20130722-1.1) ...
Selecting previously unselected package cgroupfs-mount.
Preparing to unpack .../cgroupfs-mount_1.1_all.deb ...
Unpacking cgroupfs-mount (1.1) ...
Selecting previously unselected package docker.io.
Preparing to unpack .../docker.io_1.3.3~dfsg1-2_amd64.deb ...
Unpacking docker.io (1.3.3~dfsg1-2) ...
Processing triggers for man-db (2.7.0.2-5) ...
Processing triggers for dbus (1.8.12-3) ...
Setting up makedev (2.3.1-93) ...
/run/udev or .udevdb or .udev presence implies active udev.  Aborting MAKEDEV invocation.
/run/udev or .udevdb or .udev presence implies active udev.  Aborting MAKEDEV invocation.
/run/udev or .udevdb or .udev presence implies active udev.  Aborting MAKEDEV invocation.
Setting up plymouth (0.9.0-9) ...
update-initramfs: deferring update (trigger activated)
update-rc.d: warning: start and stop actions are no longer supported; falling back to defaults
update-rc.d: warning: start and stop actions are no longer supported; falling back to defaults
Setting up libnih1 (1.0.3-4.3) ...
Setting up libnih-dbus1 (1.0.3-4.3) ...
Setting up mountall (2.54) ...
Setting up aufs-tools (1:3.2+20130722-1.1) ...
Setting up docker.io (1.3.3~dfsg1-2) ...
Adding group `docker' (GID 139) ...
Done.
Processing triggers for dbus (1.8.12-3) ...
Setting up cgroupfs-mount (1.1) ...
Processing triggers for initramfs-tools (0.117) ...
update-initramfs: Generating /boot/initrd.img-3.9-1-amd64
W: mdadm: /etc/mdadm/mdadm.conf defines no arrays.
W: mdadm: no arrays defined in configuration file.
Processing triggers for libc-bin (2.19-13) ...
root@localhost:~#

jason@localhost:~$ docker run -i -t ubuntu /bin/bash
2015/01/08 16:27:07 Post http:///var/run/docker.sock/v1.15/containers/create: dial unix /var/run/docker.sock: permission denied
jason@localhost:~$ sudo docker run -i -t ubuntu /bin/bash
Unable to find image 'ubuntu' locally
Pulling repository ubuntu
8eaa4ff06b53: Download complete 
511136ea3c5a: Download complete 
3b363fd9d7da: Download complete 
607c5d1cca71: Download complete 
f62feddc05dc: Download complete 
Status: Downloaded newer image for ubuntu:latest
root@bedef9a17ac3:/# cat /etc/issue
Ubuntu 14.04.1 LTS \n \l

root@bedef9a17ac3:/# exit
jason@localhost:~$ sudo docker run ubuntu /bin/echo "hello world"
hello world

One would ask, why should I replace virtualbox to docker? There are four main points as outline in this article :

• Faster delivery of your applications


• Deploy and scale more easily


• Get higher density and run more workloads


• Faster deployment makes for easier management

If you think the above points are attracting, perhaps you should consider it and I leave these additional materials for your further exploration.

docker 101 video presentation.
remember to sign up
get the image from docker hub.
last but not least, documentation.

how to improve apache cassandra 1.0.8 read speed

This article is for improve reading speed for apache cassandra 1.0.8. Because the reading improvement determined by many factors, we will investigate all possible areas so the gain will be improve collectively. So you may experience these factors and alter according to suit your node environment to achieve the best result. As the cassandra 1.0 released, the official cited that the read performance has increased up to 400%!

First and foremost, there are numerous articles which I use as a reference has cited copyright, I take no ownership nor credit of their hardwork as that is rightfully belong to them entirely. I only reference their work to improve my knowledge and to help people (like me) who need help and came to read what I share in my article.

Let's split these improvements into two parts, the hardware and the software.

hardware

ssd

ssd disk is way faster than hdd disk in term of reading in multiple magnitude, please read cassandra-benchmark for the benchmark. Although the cassandra using was version 0.8.10, but when cassandra 1.0 was released, read gained tremendous improvement. Then these two improvemetns will be linear gain too. Also, it is recommend to read the aforementioned article as it explain why is the random speed will hurt the read performance for hdd disk.

multiple disks

disks allocation to the commit log should be different than the data directory. Because during data write, data is repeating appending to the commit log. If the data directory is located on different drive, read performance gain should be visible.

 

software

memtable
If write behaviour has a lot of updates, it is good to look into memtable settings. There are two settings which you can start with

• memtable_total_space_in_mb


• commitlog_total_space_in_mb

more memory to this settings means the frequent write (update) will be absorded by the memory and thus, reading will be fast too as read start from memtable first before going into sstable. But because this impact system wide, you might want to gradually start to increase it and measure them. Read below for more information on what these two settings are and how to tune them
http://www.datastax.com/docs/1.0/operations/tuning#memtable-sizing
http://www.datastax.com/docs/1.0/configuration/node_configuration#memtable-total-space-in-mb
http://www.slideshare.net/driftx/cassandra-summit-2010-performance-tuning slide 14 and slide 26
http://wiki.apache.org/cassandra/MemtableThresholds

WP-DataStax-Cassandra page 16
Specifically, for read performance, Cassandra 1.0 optimizes queries by using a lighter-weight data structure for representing a row fragment from a read, than for a row fragment in a memtable into which updates accumulates. Also, with named reads, Cassandra 1.0 includes enhancements for deserializing the most recent versions of requested columns. Combined with the other optimizations, this makes reads in Cassandra as fast as writes for many workloads.

data compression

Previously I have done a study on the compression affect improvement read, read here, here, here and here. Please read the links as it provide comprehensive explanation than I could describe here.

compaction

compaction can improve (or impact) the read speed. Citation fromWP-DataStax-Cassandra,

The above process produces exceptionally fast write operations; however it also can lead to data fragmentation across the disk. Read requests may have to combine data from many SStables as well as Memtables to satisfy end user requests for data, and this can increase query response times.

To reduce data fragmentation and reclaim space taken by obsolete data, Cassandra performs "compactions" that merge the most recent data from many different SStables on disk into a new one.

So with my experience, if you trigger compaction (major) through nodetool, during compaction, the read latency will increase, thus impact but when the compaction is done, the read performance is improved.

In this documentation,  it explain different compaction strategy to use for read or write workload. So identify how's your environment write and read pattern and always measure it so you know what and when it could went wrong. Choose compaction strategy to suit your data model. For instance, if cassandra is not strong at a point, choose other big data technology. Read here for bad experience encountered.

sstables counts

Keep the sstables counts as low as possible for a column family. Excerpt from FULLTEXT02 page 39.

If a read operation is performed, initially the data are read from the memtable. If data are not in the memtable, then data get read from SSTable. Multiple SSTables may be looked up to find the data. Reading directly from SSTable decreases the performance because there are many SSTable that might need to be looked at hence requires an I/O operation means it requires touching the disk. Compared to SSTable, reading directly from memtable is fast because there is no I/O involved. The more the I/O operations are involved, the more performance will be degraded. Performance can also be increased by increasing the size of memtable [7].

Cassandra uses Bloom filter to judge quickly whether the key exists in the SSTable or not before touching the disk. Bloom filter is a efficient data structure that checks whether element is a member of a set by dividing the memory into buckets. Check each bucket to see if a key is present and if any bucket is empty then key was never inserted before. If there are many SSTables, then lots of I/O operations would be needed to read the data which can definitely decrease the performance. This is because of the fact that I/O operations are expensive and therefore compaction is used to improve read performance. Compaction merge two SSTables and sort to become one SSTable, which eventually decreases the number of SSTables and number of I/O operations, hence increasing the performance [7].

key / row cache

key cache should be enable to reduce the search from touching the disk, especially spinning disk. Excerpt from FULLTEXT02 page 47.

By default key cache is enabled and Cassandra caches 20,000 keys per Column Family (CF). The key cache decreases the Input/Output (I/O) operations because if key cache is not enabled then I/O operation is required in order to figure out the exact location of the row. Key cache holds the exact location of the data belonging to that key.

 

Row cache holds the entire content of the row in cache. By default, row cache is disabled. The overhead of enabling or increasing the row cache is that it may require more Java Virtual Machine (JVM) heap of Cassandra. By if jna lib is available, then storing row cache off heap is a good option. This article has diagram on how read is perform.

concurrent_read

Excerpt from FULLTEXT02 page 48.

Read performance can also be increased by tuning the concurrent reads. The rule is span 4 threads per Central Processing Units (CPU) core in the cluster. The higher the number of threads spanned for read, the higher performance can be achieved if the machines have got faster I/O.

A word of cautious, I tried increase concurrent_read from 32 to 64 and see some unpredictable behaviour, so it is better you do this in test environment.

decreasing read consistency level

If your business requirement can tolerate of eventual consistency, then decrease from quorum to one will improve read speed as only one node acknowledgement is sufficient to fulfill the read request compare to a certain amount of nodes in quorum.

turn off swap space

When the node start to swap due to shortage of memory, the response of node be it write or read will be visible. Hence it is best to turn off swap, and let the operating system kill or jvm kill itself to oom than the page swap start to happen.

java heap

Citation from OS-8.1.3-Cassandra Installation and Configuration Guide page 33

HEAP_NEWSIZE : Size of young generation. Larger value leads to longer GC pause times while smaller value will typically lead to more expensive GC. Set in conjunction with MAX_HEAP_SIZE.

So tune it carefully since this is pretty low level. Read this article as it mentioned a few garbage collector settings for cassandra and memory footprint.

upgrade

Each release of software improve or fix the previous defect, so is cassandra. If upgrade is viable, you should consider. For instance, to quote Aaron Morton

1.0 has key and row caches defined per CF, 1.1 has global ones which are better utilised and easier to manage. 1.2 moves bloom filters and compression meta off heap which reduces GC, which will help.  Things normally get faster.

This is also true.

monitoring

Because data load increase and/or decrease will impact the read response time, it is vital if there is monitoring services running. As cited from this paper, p1724_tilmannrabl_vldb2012 Page 1,

In modern enterprise systems it is not uncommon to have thousands of different metrics that are reported from a single host machine.

So monitor crucial metrics by cassandra, example, cpu, java heap and io should give some indicator if your speed has been reduced.

Whilst these are collected knowledge are from public and free will sharing. Any mistake and errors in this article is mine alone and does not reflect to them. Thank you and I hope you learned something.

Investigate into Apache Cassandra Memtable updateLiveRatio logging output

Today, we are going to study apache cassandra 1.0.8 on memtable logging its statistics in the cassandra system.log. Example below

WARN [MemoryMeter:1] 2014-10-17 07:38:15,346 Memtable.java (line 176) setting live ratio to minimum of 1.0 instead of 0.16714977001091136
INFO [MemoryMeter:1] 2014-10-17 07:38:15,346 Memtable.java (line 186) CFS(Keyspace='OpsCenter', ColumnFamily='pdps') liveRatio is 1.6474114033116056 (just-counted was 1.0).  calculation took 3ms for 595 columns

What does the above logging output means? Is cassandra instance operating normally since logging level is warn? Before we dive into the code to understand its meaning, let first read and understand what is memtable in cassandra context.

Excerpts from datastax documentation

Cassandra is optimized for write throughput. Cassandra writes are first written to a commit log (for durability), and then to an in-memory table structure called a memtable. A write is successful once it is written to the commit log and memory, so there is very minimal disk I/O at the time of write. Writes are batched in memory and periodically written to disk to a persistent table structure called an SSTable (sorted string table). Memtables and SSTables are maintained per column family. Memtables are organized in sorted order by row key and flushed to SSTables sequentially (no random seeking as in relational databases).

Whenever method updateRatio() from class Memtable is called, the following codes applies.

public void updateLiveRatio()
{
        if (!MemoryMeter.isInitialized())
        {
            // hack for openjdk.  we log a warning about this in the startup script too.
            logger.warn("MemoryMeter uninitialized (jamm not specified as java agent); assuming liveRatio of 10.0.  Usually this means cassandra-env.sh disabled jamm because you are using a buggy JRE; upgrade to the Sun JRE instead");
            cfs.liveRatio = 10.0;
            return;
        }

        Runnable runnable = new Runnable()
        {
            public void run()
            {
                activelyMeasuring = Memtable.this;

                long start = System.currentTimeMillis();
                // ConcurrentSkipListMap has cycles, so measureDeep will have to track a reference to EACH object it visits.
                // So to reduce the memory overhead of doing a measurement, we break it up to row-at-a-time.
                long deepSize = meter.measure(columnFamilies);
                int objects = 0;
                for (Map.Entry<DecoratedKey, ColumnFamily> entry : columnFamilies.entrySet())
                {
                    deepSize += meter.measureDeep(entry.getKey()) + meter.measureDeep(entry.getValue());
                    objects += entry.getValue().getColumnCount();
                }
                double newRatio = (double) deepSize / currentThroughput.get();

                if (newRatio < MIN_SANE_LIVE_RATIO)
                {
                    logger.warn("setting live ratio to minimum of 1.0 instead of {}", newRatio);
                    newRatio = MIN_SANE_LIVE_RATIO;
                }
                if (newRatio > MAX_SANE_LIVE_RATIO)
                {
                    logger.warn("setting live ratio to maximum of 64 instead of {}", newRatio);
                    newRatio = MAX_SANE_LIVE_RATIO;
                }
                cfs.liveRatio = Math.max(cfs.liveRatio, newRatio);

                logger.info("{} liveRatio is {} (just-counted was {}).  calculation took {}ms for {} columns",
                            new Object[]{ cfs, cfs.liveRatio, newRatio, System.currentTimeMillis() - start, objects });
                activelyMeasuring = null;
            }
        };

        try
        {
            meterExecutor.submit(runnable);
        }
        catch (RejectedExecutionException e)
        {
            logger.debug("Meter thread is busy; skipping liveRatio update for {}", cfs);
        }
}

There is a new thread which will be execute by the executor meterExecutor. This executor if it is not busy, will start to measure this column family associated with this memtable. There are two essential metrics that is involve in the arithmetic, key size plus value size and the column count. deepSize is the summation of the column family , its key and its value. A new variable newRatio is calculated with the deepSize divided by the currentThroughput.

Valid newRatio range is between 1.0 to 64.0 inclusive. When calculated newRatio is less than 1.0, first line of log such as above will started to appear in cassandra system log and newRatio will be reset to 1.0. The same check when newRatio exceed 64.0, it will be logged and value for newRatio reset to maximum valid value of 64.0. Then column family live ratio is updated with whichever which is higher, the current or the new calculated newRatio.

So technically, this is nothing to really concern about. It's a measurement of the number of operations has increased as compare to the previous. Also, given the next throughput, as estimated of size of the memtable can be calculated based on the newRatio. As a side note, this method updateRatio() is called after a mutation has been applied to the memtable but before a flush is requested.

That's it for today, I hope you learned something.

operate casandra using jmx in terminal including changing pool size, compacting sstables and key cache

If you operate apache cassandra cluster and if load per node goes huge (like nodetool info show 800GB), compactions become a problem. It's a big problem for apache cassandra 1.0.8 if you have load per node average hover around 600GB to 1TB. The read performance suffers and at times system uptime load goes high. In some instance, I noticed when repair is running, system load goes more than 20. It's not a concern if this is operating well, but the more often you see this, something has gone wrong. Today, I will share my experience on how to operate cassandra when node load is huge and cassandra instance is still running. Often times, there are nice method that is exposed via jmx but to operate remotely, jmx gui client such as jmxconsole is not ideal. Instead, we will using a jmxterm for these operation in apache cassandra 1.0.8. So let's get started.

Changing pool size

So, it is pretty simple, launch it and set to the bean, and then set the CorePoolSize. The steps will be illustrate below.

$ java -jar jmxterm-1.0-alpha-4-uber.jar
$>open localhost:7199   
#Connection to localhost:7199 is opened
$>bean org.apache.cassandra.request:type=ReplicateOnWriteStage
#bean is set to org.apache.cassandra.request:type=ReplicateOnWriteStage
$>get CorePoolSize
#mbean = org.apache.cassandra.request:type=ReplicateOnWriteStage:
CorePoolSize = 32;
$>info
#mbean = org.apache.cassandra.request:type=ReplicateOnWriteStage
#class name = org.apache.cassandra.concurrent.JMXConfigurableThreadPoolExecutor
# attributes
  %0   - ActiveCount (int, r)
  %1   - CompletedTasks (long, r)
  %2   - CorePoolSize (int, rw)
  %3   - CurrentlyBlockedTasks (int, r)
  %4   - PendingTasks (long, r)
  %5   - TotalBlockedTasks (int, r)
#there's no operations
#there's no notifications
$>set CorePoolSize 64
$>get CorePoolSize
#mbean = org.apache.cassandra.request:type=ReplicateOnWriteStage:
CorePoolSize = 64;

Alter key cache

Often times, when there is heap pressure in the jvm, the safety valve kicks in.  You can restart the cassandra instance or you can reset the key cache back to the initial value. Assuming your column family name FooBar and keyspace just4fun, then the following are steps to illustrate how is this done.

$>bean org.apache.cassandra.db:cache=FooBarKeyCache,keyspace=just4fun,type=Caches
#bean is set to org.apache.cassandra.db:cache=FooBarKeyCache,keyspace=just4fun,type=Caches
$>info
#mbean = org.apache.cassandra.db:cache=FooBarKeyCache,keyspace=just4fun,type=Caches
#class name = org.apache.cassandra.cache.AutoSavingKeyCache
# attributes
  %0   - Capacity (int, rw)
  %1   - Hits (long, r)
  %2   - RecentHitRate (double, r)
  %3   - Requests (long, r)
  %4   - Size (int, r)
#there's no operations
#there's no notifications
$>
$>get Size
#mbean = org.apache.cassandra.db:cache=FooBarKeyCache,keyspace=just4fun,type=Caches:
Size = 122307;

$>set Capacity 250000
#Value of attribute Capacity is set to 250000
$>get Capacity;      
#mbean = org.apache.cassandra.db:cache=FooBarKeyCache,keyspace=just4fun,type=Caches:
$>get Capacity 
#mbean = org.apache.cassandra.db:cache=FooBarKeyCache,keyspace=just4fun,type=Caches:
Capacity = 250000;

Compact sstable

Lastly, to compact sstables. It's amazing we have a sstable that as huge as 84GB! So trigger major compaction is not an option here, often time when load per node goes beyond 600GB, compaction took forever, as GC kick in and cpu keep on recollecting heap, making system load goes high. So here, we will select one sstable that is huge and compact that only. You can also select a few sstable and compact them and separate using comma.

$>bean org.apache.cassandra.db:type=CompactionManager
#bean is set to org.apache.cassandra.db:type=CompactionManager
$>run forceUserDefinedCompaction just4fun FooBar-hc-5-Index.db
#calling operation forceUserDefinedCompaction of mbean org.apache.cassandra.db:type=CompactionManager
#RuntimeMBeanException: java.lang.IllegalArgumentException: FooBar-hc-5-Index.db does not appear to be a data file
$>run forceUserDefinedCompaction just4fun FooBar-hc-401-Data.db 
#calling operation forceUserDefinedCompaction of mbean org.apache.cassandra.db:type=CompactionManager
#operation returns: 
null

The compaction should be started, you can check in cassandra system log or the nodetool compaction. So that's it, I hope you learned something.

Embed video into debian mediawiki

Today, this article is a bit special and a short one, we will configured
something interesting. We will embed video into mediawiki in debian. It's
actually a request from friend and so we will take a look at how to do it.
Let's see the screenshot below and if you want to do something like this, then
read on.



 

Before we get started, you will require root access in debian and already install
and configured mediawiki already.

1. change directory to

# cd /usr/share/mediawiki-extensions

2. pull from mediawiki embedvideo source from github.

# git pull https://github.com/Alexia/mediawiki-embedvideo.git

3. enable this extension.

# cd /etc/mediawiki-extensions/extensions-available
# ln -s /usr/share/mediawiki-extensions/mediawiki-embedvideo/EmbedVideo.php
# cd ../extensions-enabled
# ln -s ../extensions-available/EmbedVideo.php

Easy! Three easy steps. Now, let's edit a wiki page and an example below.

[https://www.youtube.com/watch?v=xmmYEtS13BQ Al Gromer Khan & Klaus Wiese - The Alchemy of Happiness]
{{#ev:youtube|xmmYEtS13BQ}}

[https://www.youtube.com/watch?v=ls7NMH_O5Ck&list=RDVpxBTgbeMsw&index=10 RELAXING MUSIC Relax Mind Body, Sleep Music, Meditation music, Relaxation Music Stress Relief]
{{#ev:youtube|ls7NMH_O5Ck}}

Save the page and that's it!