This section describes the domain of system experiments, introduces a running example used throughout the rest of the manual, and discusses the problems Peel will solve for you.
System Experiments Basics
The principle goal of a system experiment is to characterise the behavior of a particular system under test (SUT) for a specific set of values configured as system and application parameters. The usual way to achieve this goal is to
- define a workload application which takes specific parameters (e.g., input and output path),
- run it on top of the SUT with a specific configuration (e.g., allocated memory, DOP), and
- measure key performance characteristics (e.g., runtime, throughput, accuracy).
The basic layout of the execution environment observed when running a system experiment is depicted below.
Modern data management, however, rarely relies on a single system running in isolation. Insead, current trends advocate an architecture based on interconnected systems (e.g. HDFS and Yarn, Spark, Flink, Storm), each maintaining its own configuration. A more realistic environment layout therefore looks as follows:
Typically, one is not just interested in the insight obtained by a single experiment, but in trends highlighted by a suite of experiments where a certain SUT configuration or application parameter value is varied and everything else remains fixed.
To illustrate the principles presented above, let us consider a scenario where we want to compare the scale-out characteristics of two parallel dataflow engines - Apache Spark and Apache Flink - based on their ability to count words. The environment layout for a single run of the two applications looks like this:
Step 1: Defining a Workload Application
We start by coding two equivalent workload applications - SparkWC and FlinkWC - against the corresponding parallel dataflow APIs of the two systems. The applications
- read a text corpus from HDFS,
- compute the word frequencies within that corpus, and
- store the resulting collection of (word, count) back in HDFS.
Step 2: Defining the Input Data
In addition, we also write a parallel data generator that generates a random sequence of words to be used as input. To make the properties of the data realistic, the words are sampled i.i.d. out of a pre-defined dictionary following a Zipfian distribution.
Step 3: Running the Experiments
We are now ready execute a series experiments on a cluster with varying capacity. In each step, we perform the following actions:
- Setup the required systems, while doubling the number of DataNodes (HDFS) / TaskManagers (Flink) / Workers (Spark);
- Generate and store input data in HDFS, while keeping the size/nodes ratio fixed;
- Repeatedly execute the two applications and record key performance metrics for each experiment run.
The environment layout will look as follows:
Step 4: Result Analysis / Closing the Loop
The obtained data characterizes the so-called weak-scaling behavior of the two systems for the word counting scenario. Upon running the experiments, the most common steps are:
- Understand the experiment results (e.g., via visual exploration, statistical analysis);
- Make hypotheses that explain the observed phenomenon;
- Refine the old experiment or create a new one in order to verify them.
If you, as a practitioner, want to realize the steps outlined above, you will face the following challenges.
Each system comes with its own installation and configuration manual. You need to have good prior knowledge or invest time reading these manuals in order to understand (i) which parameters work out of the box, (ii) which ones need to be tuned to your current execution environment, and (iii) which what values to chose for those.
When you come to Step 3, you will have the option to either sit in front of the console and steer the experiment lifecycle (system setup & configuration, execution, cleanup) manually, or write a bunch of glue code that does this automatically for you. In a distributed setting, each of these phases is susceptible to occasional errors, so your glue code will need a couple of iterations until it becomes robust enough so you can rely on it.
When you come to Step 4, you will have to extract the data observed at execution time. Since every system has it’s own logging format and specifics, you will have to write more glue code that deals with that, most likely handling the specific problem at hand.
Packaging & Sharing
After fiddling around for some days, you will solve the problem at hand and produce some nice charts to share. Repeatability and reproducibility, however, are important properties of any good experiment, and you might be asked to package and share the experiments code with a skeptical third party. You will have to make sure that everything works well on another developer machine and another execution environment (e.g. EC2).
Peel can help you solve all of the above problems. The remainder of this manual explains how by means of the running example presented above. To get the example, run the following code snippet:
cd "$BUNDLE_SRC" mvn archetype:generate -B \ -Dpackage="org.peelframework.wordcount" \ -DgroupId="org.peelframework" \ -DartifactId="peel-wordcount" \ -DarchetypeGroupId=org.peelframework \ -DarchetypeArtifactId=peel-flinkspark-bundle \ -DarchetypeVersion=1.1.8 cd "peel-wordcount" mvn clean deploy cd "$BUNDLE_BIN/peel-wordcount"