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Dr. Elephant is a performance monitoring and tuning tool for Hadoop and Spark. It automatically gathers a job's metrics, analyzes them, and presents them in a simple way for easy consumption. Its goal is to improve developer productivity and increase cluster efficiency by making it easier to tune the jobs. It analyzes the Hadoop and Spark jobs using a set of pluggable, configurable, rule-based heuristics that provide insights on how a job performed, and then uses the results to make suggestions about how to tune the job to make it perform more efficiently. It also computes a number of metrics for a job which provides valuable information about the job performance on the cluster.

Why Dr. Elephant?

Most of the Hadoop optimization tools out there, whether open source or proprietary, are designed to collect system resource metrics and monitor cluster resources. They are focused on simplifying the deployment and management of Hadoop clusters. Very few tools are designed to help Hadoop users optimize their flows. The ones that are available are either inactive or have failed to scale and support the growing Hadoop frameworks. Dr. Elephant supports Hadoop with a variety of frameworks and can be easily extended to newer frameworks. It also has support for Spark. You can plugin and configure as many custom heuristics as you like. It is designed to help the users of Hadoop and Spark understand the internals of their flow and to help them tune their jobs easily.

Key Features

• Pluggable and configurable rule-based heuristics that diagnose a job;
• Out-of-the-box integration with Azkaban scheduler and support for adding any other Hadoop scheduler, such as Oozie;
• Representation of historic performance of jobs and flows;
• Job-level comparison of flows;
• Diagnostic heuristics for MapReduce and Spark;
• Easily extensible to newer job types, applications, and schedulers;
• REST API to fetch all the information.

Getting Started

How does it work?

Dr. Elephant gets a list of all recent succeeded and failed applications, at regular intervals, from the YARN resource manager. The metadata for each application—namely, the job counters, configurations, and the task data—are fetched from the Job History server. Once it has all the metadata, Dr. Elephant runs a set of heuristics on them and generates a diagnostic report on how the individual heuristics and the job as a whole performed. These are then tagged with one of five severity levels, to indicate potential performance problems.

Use Cases

At Linkedin, developers use Dr. Elephant for a number of different use cases including monitoring how their flow isperforming on the cluster, understanding why their flow is running slow, how and what can be tuned to improve theirflow, comparing their flow against previous executions, troubleshooting etc. Dr. Elephant’s performance green-lightingis a prerequisite to run jobs on production clusters.

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Sample Job Analysis/Tuning

Dr. Elephant’s home page, or the dashboard, includes all the latest analysed jobs along with some statistics.

Once a job completes, it can be found in the Dashboard, or by filtering on the Search page. One can filter jobs by thejob id, the flow execution url(if scheduled from a scheduler), the user who triggered the job, job finish time, the typeof the job, or even based on severity of the individual heuristics.

The search results provide a high level analysis report of the jobs using color coding to represent severity levels onhow the job and the heuristics performed. The color Red means the job is in critical state and requires tuning whileGreen means the job is running efficiently.

Once one filters and identifies one’s job, one can click on the result to get the complete report. The report includes details on each of the individual heuristics and a link, [Explain], which provides suggestions on how to tune the job to improve that heuristic.

Goal

The goal of this project is to build a framework which can automatically tune hadoop mapreduce parameters to optimize a given cost function. In this case, the cost function is to minimize resource usage, while letting the job complete successfully. In future we also want to support execution time as cost function. We are using iterative approach and Particle swarm optimization algorithm for auto tuning. These iteration are done during normal production run of the job. We have seen improvement in the range of 20-30% in resource usage with in 15-20 iteration of the job.

Auto tuning starts with default parameters of the job and after each run fitness of current parameters are calculated and algorithm suggest new parameters. For interacting with Dr Elephant a new API called getCurrentRunParameters is written, which returns parameter for the current run for the given job.

Components

Daemon

There are 4 daemons in the Auto Tuning module:

• Baseline Computatation : This daemon computes the current average resource usage and execution time of the new enabled job using historical data from Dr Elephant.
• Job Completion Detector : Once the new parameters are sent for execution, this daemon keep polling for the job completion. For Azkaban scheduler it uses Azkaban rest API.
• Fitness Computation Daemon: Once the job is completed (Successful/Failed), this daemon computes the fitness of the parameter set using resource usage and input data size.
• Param Generator: Once fitness is computed for all existing parameter set, this daemon suggest new set of parameters. Currently we use PSO algorithm for new parameter suggestion.

Rest API

There is one new API called getCurrentRunParameters which gets suggested parameters from DB and returns it to caller. Currently this is the only interaction point between external system and Dr Elephant for auto tuning.

This is the beta version for the Auto Tuning and supports following features:

• Currently it support Pig Script for optimization
• Supports Azkaban Scheduler
• Supports enabling or disabling auto tuning as a whole
• Constraints are applied on parameters to make sure no job fails because of auto tuning
• Penalty for the parameters in case it exceed resource usage/execution time from allowed limits

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We have plan to support following features for future version

• Hive and Spark support
• Execution time optimization
• Reporting for visualizing improvements

Schema changes for auto tuning:

Table 1: tuning_algorithmThis table identifies algorithm to be used for the optimization metric (Resource, time) and job type (Pig, Hive). In general there should be one algorithm for one job type, but framework supports multiple algorithms for one row as well.

Table 2: tuning_parameterThis table represents Hadoop parameters to be optimized for each algorithm in tuning_algorithm. For example mapreduce.map.memory.mb, mapreduce.task.io.sort.mb etc.

Table 3: flow_definitionThis table represent one flow, it can be from any scheduler like Azkaban, Oozie, Appworx etc.

Table 4: job_definitionThis table represent the job to be optimized. This table contains general info other than auto tuning related information. Broken job definition info in two table, as this can be used for Dr Elephant basic info, as not all jobs will be enabled for auto tuning.

Table 5: tuning_job_definitionThis table represent the job to be optimized and contains information required for auto tuning only.

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Table 6: flow_executionThis table represent one execution of a flow.

Table 7: job_executionThis table represent jobs from one execution of a flow. Contains information about execution of a job other than auto tuning related info. Broken execution related info in two table, as this can be used for Dr Elephant basic info, as not all jobs will be enabled for auto tuning.

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Table 8: tuning_job_executionThis table represent jobs from one execution of a flow and contains auto tuning related information. This one execution is corresponding to one set of parameters.

Table 9: job_saved_stateInternal table for optimization algorithm. Stores the current state of job to be optimized.

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Table 10: job_suggested_param_valueSuggested parameter value corresponding to one execution of the job.