Within the last couple of years we’ve witnessed a natural evolution in the “Big Data” ecosystem. Where the common theme that you’ve probably heard in the community that, “Memory is King”, and it is. Therefore, if you are looking for performance optimization in your stack, an “in memory” layer should be part of the equation. Enter Tachyon, which provides reliable file sharing across cluster frameworks.
Tachyon can be used to support different framworks, as well as different filesystems. So to bound the scope of this post, we will outline how to setup Tachyon on a local installation to boost performance of map-reduce application whose data is stored in HDFS on Fedora 21.
- BDAS Stack
- YouTube - Introduction to Tachyon
- Tachyon project
- Fedora BIG Data SIG
- Going beyond Hadoop
Installation and Setup
Prior to installing Tachyon please ensure that you have setup your hadoop installation as outlined in the pre-reqs.
First you will need to install the tachyon package:
$ sudo yum install amplab-tachyon
Now you will need to update /etc/hadoop/core-site.xml configuration for hadoop to enable map-reduce to take advantage of tachyon, by appending the following snippet:
<property> <name>fs.tachyon.impl</name> <value>tachyon.hadoop.TFS</value> </property>
Now that all the plumbing is in place you can restart hadoop
systemctl restart hadoop-namenode hadoop-datanode hadoop-nodemanager hadoop-resourcemanager
Next, make certain your local HDFS instance is up and running, then you will need to perform a tachyon format.
$ sudo runuser hdfs -s /bin/bash /bin/bash -c "tachyon.sh format" > Formatting Tachyon @ localhost > Deleting /var/lib/tachyon/journal/ > Formatting hdfs://localhost:8020/tachyon/data > Formatting hdfs://localhost:8020/tachyon/workers
Prior to running the daemons you will need to mount the in-memory filesystem.
$ sudo tachyon-mount.sh SudoMount
Now you can start the daemons.
$ sudo systemctl start tachyon-master tachyon-slave
For completeness you can inspect the logs which are located in the standard system location
$ ls -la /var/log/tachyon
Once you’ve verified tachyon is up and running, you can run a simple mapreduce application as seen below:
$ hadoop jar /usr/share/java/hadoop/hadoop-mapreduce-examples.jar wordcount tachyon://localhost:19998/user/tstclair/input/constitution.txt tachyon://localhost:19998/test1
You’ll notice tha tachyon prefix attached to the input and output locations. This enables hadoop to start the TFS shim which will load and write to tachyon. To verify you can run the following:
$ sudo runuser hdfs -s /bin/bash /bin/bash -c "tachyon.sh tfs ls /test1" > 16.65 KB 02-17-2014 15:41:11:849 In Memory /test1/part-r-00000 > 0.00 B 02-17-2014 15:41:12:366 In Memory /test1/_SUCCESS
If you’re interested in grok'ing further you can probably find the part file under /mnt/ramdisk.
Tachyon provides reliable in memory file sharing across cluster frameworks, as we have seen in our simple example. It also enables some very interesting prospects for other back end filesystems.
In future posts we’ll explore more elaborate configurations using tachyon atop different frameworks and filesystems.