Zingg
  • 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
      • Enterprise Installation for Snowflake
        • Setting up Zingg
        • Snowflake Properties
        • Match Configuration
        • Running Asynchronously
        • 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
      • User Defined Mapping Match Types
      • Deterministic Matching
      • Pass Thru Data
      • 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
    • Verification of Blocking Model
    • Building And Saving The Model
    • Finding The Matches
    • Adding Incremental Data
    • Linking Across Datasets
    • Explanation of Models
    • Approval of Clusters
    • Combining Different Match Models
    • Model Difference
    • Persistent ZINGG ID
  • Data Sources and Sinks
    • Zingg Pipes
    • Databricks
    • Snowflake
    • JDBC
      • Postgres
      • MySQL
    • AWS S3
    • Cassandra
    • MongoDB
    • Neo4j
    • Parquet
    • BigQuery
    • Exasol
  • Working With Python
    • Python API
  • Running Zingg On Cloud
    • Running On AWS
    • Running On Azure
    • Running On Databricks
    • Running on Fabric
  • 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 Up Zingg Development Environment
  • Community
  • Frequently Asked Questions
  • Reading Material
  • Security And Privacy
Powered by GitBook

@2021 Zingg Labs, Inc.

On this page

Was this helpful?

Edit on GitHub
  1. Step-By-Step Guide

Persistent ZINGG ID

Reassign the ZINGG IDs for clusters from the original production model

Let's say we have a production flow already deployed, and there is a new release which introduces some additional features (e.g., nicknames, a better blocking tree, or additional hash functions). Or maybe there are changes to the classifiers which are expected to change the output of Zingg.

Now, in that case, we already have existing ZINGG IDs which have gone into the operational systems. So, we want to reassign the ZINGG IDs for clusters from the original production model as far as possible.

We want to provide a user-friendly and easy way for any Zingg user to be able to achieve this.

The reassign phase is run as follows:

./scripts/zingg.sh --phase reassignZinggId –conf <ath to new conf> --originalZinggId <path to original conf>

The output will be updating the output of the new run with the ZINGG_ID in NEW_OUTPUT and keeping a backup of the new output as NEW_OUTPUT_timestamp.

PreviousModel DifferenceNextData Sources and Sinks

Last updated 22 days ago

Was this helpful?