Understanding how an Hadoop cluster is actually used in practice is paramount to properly manage and operate it. In this article I introduce Replephant, an open source Clojure library to perform interactive analysis of Hadoop cluster usage via REPL and to generate usage reports.

Replephant is available at replephant on GitHub.

Replephant in one minute

This section is an appetizer of what you can do with Replephant. Do not worry if something is not immediately obvious to you – the Replephant documentation describes everything in full detail.

First, clone the Replephant repository and start the Clojure REPL. You must have lein (leiningen) already installed; if you do not please follow the Replephant installation instructions.

$ git clone https://github.com/miguno/replephant.git $ cd replephant $ lein repl # once the REPL is loaded the prompt will change to: replephant.core =>

Then you can begin analyzing the usage of your own cluster:

; The root directory is usually the one defined by Hadoop's ; mapred.job.tracker.history.completed.location and/or ; hadoop.job.history.location settings ( def jobs ( load-jobs "/local/path/to/hadoop/job-history-root-dir" )) ; How many jobs are in the log data? ( count jobs ) => 12 ; Show me all the users who ran one or more jobs in the cluster ( distinct ( map :user.name jobs )) => ( "miguno" , "alice" , "bob" , "daniel" , "carl" , "jim" ) ; Consumption of computation resources: which Hadoop users ; account for most of the tasks launched? ( println ( utils/sort-by-value-desc ( tasks-by-user jobs ))) => { "miguno" 2329 , "alice" 2208 , "carl" 1440 , "daniel" 19 , "bob" 2 , "jim" 2 }

Alright, that was a quick start! The next sections cover Replephant in more depth.

Motivation

Understanding how an Hadoop cluster is actually used in practice is paramount to properly manage and operate it. This includes knowing cluster usage across the following dimensions:

Which users account for most of the resource consumption in the cluster (impacts e.g. capacity planning, budgeting and billing in multi-tenant environments, cluster configuration settings such as scheduler pool/queue settings).

account for most of the resource consumption in the cluster (impacts e.g. capacity planning, budgeting and billing in multi-tenant environments, cluster configuration settings such as scheduler pool/queue settings). Which analysis tools such as Pig or Hive are preferred by the users (impacts e.g. cluster roadmap, trainings, providing custom helper libraries and UDFs).

such as Pig or Hive are preferred by the users (impacts e.g. cluster roadmap, trainings, providing custom helper libraries and UDFs). Which data sets account for most of the analyses being performed (impacts e.g. prolonging or canceling data subscriptions, data archiving and aging, HDFS replication settings).

account for most of the analyses being performed (impacts e.g. prolonging or canceling data subscriptions, data archiving and aging, HDFS replication settings). Which MapReduce jobs consume most of the resources in the cluster and for how long (impacts e.g. how the jobs are coded and configured, when and where they are launched; also allows your Ops team to point and shake fingers).

Replephant was created to answer those important questions by inspecting production Hadoop logs (here: so-called Hadoop job configuration and job history files) and allowing you to derive relevant statistics from the data. Notably, it enables you to leverage Clojure’s REPL to interactively perform such analyses. You can even create visualizations and plots from Replephant’s usage reports by drawing upon the data viz magics of tools such as R and Incanter (see FAQ section).

Apart from its original goals Replephant has also proven to be useful in cluster/job troubleshooting and debugging. Because Replephant is lightweight and easy to install operations teams can conveniently run Replephant in production environments if needed.

Related work

The following projects are similar to Replephant:

hadoop-job-analyzer – analyzes Hadoop jobs, aggregates the information according to user specified crosssections, and sends the output to a metrics backend for visualization and analysis (e.g. Graphite). Its analysis is based on parsing Hadoop’s job log files just like Replephant does.

If you are interested in more sophisticated cluster usage analysis you may want to take a look at:

White Elephant (by LinkedIn) is an open source Hadoop log aggregator and dashboard which enables visualization of Hadoop cluster utilization across users and over time.

hRaven (by Twitter) collects run time data and statistics from MapReduce jobs running on Hadoop clusters and stores the collected job history in an easily queryable format. A nice feature of hRaven is that it can group related MapReduce jobs together that are spawned from a single higher-level analysis job such as Pig (e.g. Pig jobs usually manifests themselves in several chained MapReduce jobs). A current drawback of hRaven is that it only supports Cloudera CDH3 up to CDH3u4 – CDH3u5, Hadoop 1.x and Hadoop 2.x are not supported yet.

Commercial offerings such as Cloudera Manager (Enterprise Core), Hortonworks Management Center or MapR M5 include cluster usage reporting features.

Features

Replephant’s main value proposition is to read and parse Hadoop’s raw log files and turn them into ready-to-use Clojure data structures – because as is often the case for such a data analysis preparing and loading the original raw data is the hardest part.

On top of this ETL functionality Replephant also includes a set of basic usage reports such as (tasks-by-user jobs) and convenient filter predicates such as pig? (see Usage section on GitHub). But even more interesting is the fact that you can use the Clojure REPL including all of Clojure’s own powerful features to interactively drill down into the job data yourself.

Getting started

Requirements

Java JDK/JRE >= 6

Leiningen version 2 – either install manually or use your favorite package manager such as HomeBrew for Macs

That’s it!

Installation

Apart from meeting Replephant’s requirements (see above) you only need to clone Replephant’s git repository.

# Option 1: using HTTPS for data transfer $ git clone https://github.com/miguno/replephant.git # Option 2: using SSH for data transfer (requires GitHub user account) $ git clone git@github.com:miguno/replephant.git

Note: This step requires a working Internet connection and appropriate firewall settings, which you may or may not have in a production environment.

Data structures and usage analysis

When you analyze your Hadoop cluster’s usage with Replephant you will be working with two data structures:

Jobs: The main data we are interested in for cluster usage analysis, parsed by Replephant from the raw Hadoop job logs. Data sets: Defined by the user, i.e. you!

Jobs

Jobs are modelled as associative data structures that map Hadoop job parameters as well as Hadoop job history data to their respective values. Both the keys in the data structure – the names of job parameter and the name of data fields in the job history data, which together we just call fields – as well as their values are derived straight from the Hadoop logs.

Replephant converts the keys of the data fields into Clojure keywords according to the following schema:

Job parameters (from job configuration files) are directly converted into keywords. For instance, mapred.input.dir becomes :mapred.input.dir (note the leading colon, which denotes a Clojure keyword).

becomes (note the leading colon, which denotes a Clojure keyword). Job history data including job counters (from job history files) are lowercased and converted into Lisp-style keywords For instance, the job counter HDFS_BYTES_WRITTEN becomes :hdfs-bytes-written and a field such as JOB_PRIORITY becomes :job-priority .

Basically, everything that looks like :words.with.dot.separators is normally a job parameter whereas anything else is derived from job history data. The values of the various fields are, where possible, converted into the appropriate Clojure data types (e.g. a value representing an integer will be correctly turned into an int , the strings “true” and “false” are converted into their respective boolean values).

Here is a (shortened) example of a job data structure read from Hadoop log files:

{ :dfs.access.time.precision 3600000 , ; <<< a job configuration data field :dfs.block.access.token.enable false, ; *** SNIP *** :hdfs-bytes-read 69815515804 , ; <<< a job history data field :hdfs-bytes-written 848734873 , ; *** SNIP *** :io.sort.mb 200 , :job-priority "NORMAL" , :job-queue "default" , :job-status "SUCCESS" , :jobid "job_201206011051_137865" , :jobname "Facebook Social Graph analysis" , ; *** SNIP *** :user.name "miguno" }

Here are some usage analysis examples:

; Consumption of computation resources: which Hadoop users account for most of the tasks launched? ( println ( utils/sort-by-value-desc ( tasks-by-user jobs ))) => { "miguno" 2329 , "alice" 2208 , "carl" 1440 , "daniel" 19 , "bob" 2 , "jim" 2 } ; Consumption of computation resources: which Hadoop users account for most of the jobs launched? ( println ( utils/sort-by-value-desc ( jobs-by-user jobs ))) => { "daniel" 3 , "alice" 3 , "carl" 2 , "miguno" 2 , "bob" 1 , "jim" 1 } ; Consumption of computation resources: which MapReduce tools account for most of the tasks launched? ( println ( utils/sort-by-value-desc ( tasks-by-tool jobs ))) => { :hive 2329 , :other 1440 , :streaming 1778 , :mahout 432 , :pig 21 } ; Consumption of computation resources: which MapReduce tools account for most of the jobs launched? ( println ( utils/sort-by-value-desc ( jobs-by-tool jobs ))) => { :pig 4 , :other 2 , :mahout 2 , :streaming 2 , :hive 2 } ; Find jobs that violate data locality -- those are candidates for optimization and tuning. ; ; The example below is pretty basic. It retrieves all jobs that have 1+ rack-local tasks, ; i.e. tasks where data needs to be transferred over the network (but at least they are from ; the same rack). ; A slightly improved version would also include jobs were data was retrieved from OTHER racks ; during a map tasks, which in pseudo-code is (- all-maps rack-local-maps data-local-maps). ; ( def optimization-candidates ( filter # ( > ( :rack-local-maps % 0 ) 0 ) jobs ))

The following examples demonstrate the predicates built into Replephant:

; Restrict your analysis to a specific subset of all jobs according to one or more predicates ( def hive-jobs ( filter hive? jobs )) ( def jobs-with-compressed-output ( filter compressed-output? jobs )) ( def failed-jobs ( filter failed? jobs )) ; Detect write-only jobs and jobs for which Replephant cannot yet extract input data information. ( def jobs-with-missing-input ( filter missing-input-data? jobs )) ; Helpful to complete your data set definitions ( def jobs-with-unknown-input ( filter ( partial unknown-input-data? data-sets ) jobs ))

In addition to the data derived from Hadoop log files Replephant also adds some Clojure metadata to each job data structure. At the moment only a :job-id field is available. This helps to identify problematic job log files (e.g. those Replephant fails to parse) because at least Replephant will tell you the job id, which you can then use to find the respective raw log files on disk.

( def job ... ) ; ( meta job ) => { :job-id "job_201206011051_137865" }

Note that even though this metadata follows the same naming conventions as the actual job data it is still metadata and as such you must access it via (meta ...) . Accessing the job data structure directly – without meta – only provides you with the log-derived data.

Data sets

You only need to define data sets if you use any of Replephant’s data set related functions such as tasks-by-data-sets . Otherwise you can safely omit this step.

Data sets are used to describe the, well, data sets that are stored in an Hadoop cluster. They allow you to define, for example, that the Twitter Firehose data is stored in this particular location in the cluster. Replephant can then leverage this information to perform usage analysis related to these data sets; for instance, to answer questions such as “How many Hadoop jobs were launched against the Twitter Firehose data in our cluster?”.

Thanks to Clojure’s homoiconicity it is very straight-forward to define data sets so that Replephant can understand which jobs read which data in your Hadoop cluster. You only need to create an associative data structure that maps the name of the data set to a regex pattern that is matched against a job’s input directories (more correctly, input URIs) as configured via mapred.input.dir and mapred.input.dir.mappers . You then pass this data structure to the appropriate Replephant function.

Important note: In order to simplify data set definitions Replephant will automatically extract the path component of input URIs, i.e. it will remove scheme and authority information from mapred.input.dir and mapred.input.dir.mappers values. This means you should write regexes that match against strings such as /path/to/foo/ instead of hdfs:///path/to/foo/ or hdfs://namenode.your.datacenter/path/to/foo/ .

( def data-sets { ; Will match e.g. "hdfs://namenode/twitter/firehose/*", "/twitter/firehose" ; and "/twitter/firehose/*"; see note above "Twitter Firehose data" # "^/twitter/firehose/?" })

Here is another example:

; Consumption of computation resources: which data sets account for most of the tasks launched? ; (data sets are defined in a simple associative data structure; see section "Data sets" below) ( def data-sets { "Twitter Firehose data" # "^/twitter/firehose/?" , "Facebook Social Graph" # "^/facebook/social-graph/?" }) ( println ( utils/sort-by-value-desc ( tasks-by-data-set jobs data-sets ))) => { "Facebook Social Graph data" 2329 , "UNKNOWN DATA SET" 1872 , "Twitter Firehose data" 1799 }

Replephant uses native Clojure regex patterns, which means you have the full power of java.util.regex.Pattern at your disposal.

How Replephant matches job input with data set definitions: Replephant will consider a MapReduce job to be reading a given data set if ANY of the job’s input URIs match the respective regex of the data set. In Hadoop the values of mapred.input.dir and mapred.input.dir.mappers maybe be a single URI or a comma-separated list of URIs; in the latter case Replephant will automatically explode the comma-separated string into a Clojure collection of individual URIs so that you don’t have to write complicated regexes to handle multiple input URIs in your own code (the regex is matched against the individual URIs, one at a time).

Analyzing multiple cluster environments: If you are running, say, a production cluster and a test cluster that host different data sets (or at different locations), it is convenient to create separate data set definitions such as (def production-data-sets { ... }) and (def test-data-sets { ... }) .

See data_sets.clj for further information and for an example definition of multiple data sets.

Visualization

Replehant itself does not implement any native visualization features. However you can leverage all the existing data visualization tools such as R or Incanter (the latter is basically a clone of R written in Clojure).

For your convenience Incanter has been added as a dependency of Replephant, which is a fancy way of saying that you can use Incanter from Replephant’s REPL right out of the box. Here is an example Incanter visualization of cluster usage reported by tasks-by-user :

;; Create a bar chart using Incanter ( def jobs ( load-jobs ... )) ( def u->t ( utils/sort-by-value-desc ( tasks-by-user jobs ))) ( use ' ( incanter core charts )) ( view ( bar-chart ( keys u->t ) ( vals u->t ) :title "Computation resources consumed by user" :x-label "Users" :y-label "Tasks launched" ))

Note: This specific example requires a window system such as X11. In other words it will not work in a text terminal.

This produces the following chart:

How it works

In a nutshell Replephant reads the data in Hadoop job configuration files and job history files into a “job” data structure, which can then be used for subsequent cluster usage analyses.

Background: Hadoop creates a pair of files for each MapReduce job that is executed in a cluster:

A job configuration file , which contains job-related data created at the time when the job was submitted to the cluster. For instance, the location of the job’s input data is specified in this file via the parameter mapred.input.dir . Format: XML Example filename: job_201206222102_0003_conf.xml for a job with ID job_201206222102_0003

, which contains job-related data created at the time when the job was submitted to the cluster. For instance, the location of the job’s input data is specified in this file via the parameter .

<?xml version="1.0" encoding="UTF-8" standalone="no"?> <configuration> <property><name> io.bytes.per.checksum </name><value> 512 </value></property> <property><name> mapred.input.dir </name><value> hdfs://namenode/facebook/social-graph/2012/06/22/ </value></property> <property><name> mapred.job.name </name><value> Facebook Social Graph analysis </value></property> <property><name> mapred.task.profile.reduces </name><value> 0-2 </value></property> <property><name> mapred.reduce.tasks.speculative.execution </name><value> true </value></property> ... </configuration>

An accompanying job history file , which captures run-time information on how the job was actually executed in the cluster. For instance, Hadoop stores a job’s run-time counters such as HDFS_BYTES_WRITTEN (a built-in counter of Hadoop, which as a side note is also shown in the JobTracker web UI when looking at running or completed jobs) as well as application-level custom counters (provided by user code). Format: Custom plain-text encoded format for Hadoop 1.x and 0.20.x, described in JobHistory class Example filename: job_201206222102_0003_1340394471252_miguno_Job2045189006031602801

, which captures run-time information on how the job was actually executed in the cluster. For instance, Hadoop stores a job’s run-time counters such as (a built-in counter of Hadoop, which as a side note is also shown in the JobTracker web UI when looking at running or completed jobs) as well as application-level custom counters (provided by user code).

Meta VERSION="1" . Job JOBID="job_201206011051_137865" JOBNAME="Facebook Social Graph analysis" USER="miguno" SUBMIT_TIME="1367518567144" JOBCONF="hdfs://namenode/app/hadoop/staging/miguno/\.staging/job_201206011051_137865/job\.xml" VIEW_JOB=" " MODIFY_JOB=" " JOB_QUEUE="default" . Job JOBID="job_201206011051_137865" JOB_PRIORITY="NORMAL" . Job JOBID="job_201206011051_137865" LAUNCH_TIME="1367518571729" TOTAL_MAPS="2316" TOTAL_REDUCES="12" JOB_STATUS="PREP" . Task TASKID="task_201206011051_137865_r_000013" TASK_TYPE="SETUP" START_TIME="1367518572156" SPLITS="" . ReduceAttempt TASK_TYPE="SETUP" TASKID="task_201206011051_137865_r_000013" TASK_ATTEMPT_ID="attempt_201206011051_137865_r_000013_0" START_TIME="1367518575026" TRACKER_NAME="slave406:localhost/127\.0\.0\.1:56910" HTTP_PORT="50060" . ...

Depending on your Hadoop version and cluster configuration, Hadoop will store those files in directory trees rooted at mapred.job.tracker.history.completed.location and/or hadoop.job.history.location .

Replephant uses standard XML parsing to read the job configuration files, and relies on the Hadoop 1.x Java API to parse the job history files via DefaultJobHistoryParser . At the moment Replephant retrieves only such history data from job history files that are related to job start, job finish or job failure (e.g. task attempt data is not retrieved). For each job Replephant creates a single associative data structure that contains both the job configuration as well as the job history data in a Clojure-friendly format. This job data structure forms the basis for all subsequent cluster usage analyses as we have seen in the previous section.

Summary

Replephant is a work in progress but already a pretty valuable addition to our Hadoop toolset. If you want to give it a try, head over to the Replephant project homepage and play with it!