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Data Shaper
Data Shaper
  • 🚀GETTING STARTED
    • What is Primeur Data Shaper
      • What is the Data Shaper Designer
      • What is the Data Shaper Server
      • What is the Data Shaper Cluster
    • How does the Data Shaper Designer work
      • Designer Views and Graphs
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    • How do the Data Shaper Server and Cluster work
      • Data Shaper Server and Cluster
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    • VFS Graph Components
      • DataOneFileDescriptor (DOFD) metadata
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  • DATA SHAPER DESIGNER
    • Configuration
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    • Transformers
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    • CTL2 - Data Shaper Transformation Language
    • Language Reference
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      • Lookup Table Functions
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      • Data Service HTTP Library Functions
      • Custom CTL Functions
      • CTL2 Appendix - List of National-specific Characters
      • HIDDEN Subgraph Functions
    • Tutorial
      • Creating a Transformation Graph
      • Filtering the records
      • Sorting the Records
      • Processing Speed-up with Parallelization
      • Debugging the Java Transformation
  • DATA SHAPER SERVER
    • Introduction
    • Administration
      • Monitoring
    • Using Graphs
      • Job Queue
      • Execution History
      • Job Inspector
    • Cluster
      • Sandboxes in Cluster
      • Troubleshooting
  • Install Data Shaper
    • Install Data Shaper
      • Introduction to Data Shaper installation process
      • Planning Data Shaper installation
      • Data Shaper System Requirements
      • Data Shaper Domain Master Configuration reference
      • Performing Data Shaper initial installation and master configuration
        • Creating database objects for PostgreSQL
        • Creating database objects for Oracle
        • Executing Data Shaper installer
        • Configuring additional firewall rules for Data Shaper
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On this page
  • Details of the Example Transformation Design
  • Specification of "node allocation"
  • Scalability of the Example Transformation
  1. DATA SHAPER DESIGNER
  2. Data Partitioning in Cluster

Example of Distributed Execution

PreviousGraph Allocation ExamplesNextRemote Edges

The following diagram shows a transformation graph used for parsing invoices generated by cell phone network providers.

The size of these input files may be up to a few gigabytes, so it is very beneficial to design the graph to work in Cluster environment.

Details of the Example Transformation Design

Note there are four Cluster components in the graph and these components define a point of "node allocation" change, so the part of the graph demarcated by these components is highlighted by the red rectangle. The allocation of these components should be performed in parallel. This means that the components inside the rectangle should have convenient allocation. The rest of the graph runs on a single node.

Specification of "node allocation"

There are 2 node allocations used in the graph:

  • node allocation for components running in parallel (demarcated by the four Cluster components)

  • node allocation for the outer part of the graph which runs on a single node

The single node is specified by the sandbox code used in the URLs of input data. The following dialog shows the File URL value: sandbox://data/path-to-csv-file, where data is the ID of the server sandbox containing the specified file. And it is the data local sandbox which defines the single node.

The part of the graph demarcated by the four Cluster components may have specified its allocation by the file URL attribute as well, but this part does not work with files at all, so there is no file URL. Thus, we will use the node allocation attribute. Since components may adopt the allocation from their neighbors, it is sufficient to set it only for one component.

Again, dataPartitioned in the following dialog is the sandbox ID.

This project requires 3 sandboxes: data, dataPartitioned and PhoneChargesDistributed.

  • data

    • contains input and output data

    • local sandbox (yellow folder), so it has only one physical location

    • accessible only on node i-4cc9733b in the specified path

  • dataPartitioned

    • partitioned sandbox (red folder), so it has a list of physical locations on different nodes

    • does not contain any data and since the graph does not read or write to this sandbox, it is used only for the definition of "nodes allocation"

    • on the following figure, the allocation is configured for two Cluster nodes

  • PhoneChargesDistributed

    • common sandbox containing the graph file, metadata, and connections

    • shared sandbox (blue folder), so all Cluster nodes have access to the same files

If the graph was executed with the sandbox configuration of the previous figure, the node allocation would be:

  • components which run only on a single node, will run only on the i-4cc9733b node according to the "data" sandbox location.

  • components with an allocation according to the dataPartitioned sandbox will run on nodes i-4cc9733b and i-52d05425.

Scalability of the Example Transformation

The example transformation has been tested in an Amazon Cloud environment with the following conditions for all executions:

  • the same master node

  • the same input data: 1.2GB of input data, 27 million records

  • three executions for each "node allocation"

  • "node allocation" changed between every 2 executions

  • all nodes has been of "c1.medium" type

We tested "node allocation" cardinality from 1 single node, all the way up to 8 nodes.

The following figure shows the functional dependence of run-time on the number of nodes in the Cluster:

The following figure shows the dependency of a speedup factor on the number of nodes in the Cluster. The speedup factor is the ratio of the average runtime with one Cluster node and the average runtime with x Cluster nodes. Thus: speedupFactor = avgRuntime(1 node) / avgRuntime(x nodes) We can see, that the results are favorable up to 4 nodes. Each additional node still improves the Cluster performance; however, the effect of the improvement decreases. Nine or more nodes in the Cluster may even have a negative effect because their benefit for performance may be lost in the overhead with the management of these nodes.

These results are specific for each transformation, there may be a transformation with a much better or possibly worse function curve.

Table of measured runtimes:

NODES
RUNTIME 1 ame
RUNTIME 2 ame
RUNTIME 3 ame
AVERAGE RUNTIME
SPEEDUP FACTOR

1

861

861

861

861

1

2

467

465

466

466

1.85

3

317

319

314

316.67

2.72

4

236

233

233

234

3.68

5

208

204

204

205.33

4.19

6

181

182

182

181.67

4.74

7

168

168

168

168

5.13

8

172

159

162

164.33

5.24