Zingg-0.3.4
  • Welcome to Zingg
  • Step-By-Step Guide
    • Installation
      • Docker
        • Sharing custom data and config files
        • Shared locations
        • File read/write permissions
        • Copying Files To and From the Container
      • Installing From Release
        • Single Machine Setup
        • Spark Cluster Checklist
        • Installing Zingg
        • Verifying The Installation
      • Compiling From Source
    • Hardware Sizing
    • Zingg Runtime Properties
    • Zingg Command Line
    • Configuration
      • Configuring Through Environment Variables
      • Data Input and Output
        • Input Data
        • Output
      • Field Definitions
      • Model Location
      • Tuning Label, Match And Link Jobs
      • Telemetry
    • Working With Training Data
      • Finding Records For Training Set Creation
      • Labeling Records
      • Find And Label
      • Using pre-existing training data
      • Updating Labeled Pairs
      • Exporting Labeled Data
    • Building and saving the model
    • Finding the matches
    • Linking across datasets
  • Data Sources and Sinks
    • Zingg Pipes
    • Snowflake
    • JDBC
      • Postgres
      • MySQL
    • Cassandra
    • MongoDB
    • Neo4j
    • Parquet
    • BigQuery
  • Working With Python
  • Running Zingg on Cloud
    • Running on AWS
    • Running on Azure
    • Running on Databricks
  • Zingg Models
    • Pre-trained models
  • Improving Accuracy
    • Ignoring Commonly Occuring Words While Matching
    • Defining Domain Specific Blocking And Similarity Functions
  • Documenting The Model
  • Interpreting Output Scores
  • Reporting bugs and contributing
    • Setting Zingg Development Environment
  • Community
  • Frequently Asked Questions
  • Reading Material
  • Security And Privacy
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  1. Step-By-Step Guide

Hardware Sizing

Hardware required for different sizes of data

PreviousCompiling From SourceNextZingg Runtime Properties

Last updated 2 years ago

Zingg has been built to scale. Performance is dependent on:

  • The number of records to be matched.

  • The number of fields to be compared against each other.

  • The actual number of duplicates.

Here are some performance numbers you can use to determine the appropriate hardware for your data.

  • 120k records of examples/febrl120k/test.csv take 5 minutes to run on a 4 core, 10 GB RAM local Spark cluster.

  • 5m records of take ~4 hours on a 4 core, 10 GB RAM local Spark cluster.

  • 9m records with 3 fields - first name, last name, email take 45 minutes to run on AWS m5.24xlarge instance with 96 cores, 384 GB RAM

If you have up to a few million records, it may be easier to run Zingg on a single machine in Spark local mode.

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