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NDC Calcite

This repository contains an adapter that is metadata configurable to support approximately 40 data sources.

Approximately 15 files-based data sources, and 25 JDBC based data sources.

Temporary Instructions - For Getting Started as a Developer with this repo.

Clone the repo && the subrepo

This adapter is based on a forked version of Calcite (the sub-repo)

 git clone --recurse-submodules https://github.com/hasura/ndc-calcite.git calcite-connector
 cd calcite-connector
 git checkout main

Note - this is somewhat simplified - because everything is in the "main" branch. I'll let you research how to manage the primary and sub-branch on your own!

Build the Java project

The project will require jdk 21 and maven. You need to have those installed first.

This is the JNI for calcite. It handles the Calcite to Rust handoff.

You can build it like this.

cd calcite-rs-jni
chmod +x *.sh
./build.sh

This will build the Java jars that the Rust project (at the root of this mono-repo) requires.

Build the Connector and CLI Plugin

cd ..
cargo build --bin ndc-calcite --bin ndc-calcite-cli

Test the file adapter

Note - Run it to perform the test.

./test.sh file # run the tests

Instruction for Testing with a Supergraph using Docker

NOTE Perform all of these operations from the root of the repo!!!

Build the docker image

./build-local.sh v0.1.0

Create a Supergraph

ddn supergraph init test-connector
cd test-connector

Create a connector under default subgraph "app"

mkdir ./app/connector
ddn connector-link add calcite --configure-connector-token secret --configure-host http://local.hasura.dev:8081 --subgraph app/subgraph.yaml --target-env-file .env

Add metadata to the connector

This script is one-and-done, you can't redo without resetting back to prior state. You might consider, committing before running this, to facilitate a rollback.

../cli.sh ./app/connector/calcite 8081 secret

Optional Revise Calcite Adapter

This will setup a SQLite connector. If you want to change the connector DO IT NOW. Go to app/connector/calcite/models/model.json and revise the schema(s). Look at the sample models for ideas, or, get more details from Apache Calcite.

../cli-update-model.sh ./app/connector/calcite

Start supergraph

This is to facilitate the introspection. Introspection will not work offline with ddn connect-link add-all, without the connector being in connector hub. (That's a guess, since I can't prove it.)

HASURA_DDN_PAT=$(ddn auth print-pat) docker compose --env-file .env up --build --watch

Introspect

ddn connector-link update calcite --add-all-resources --subgraph app/subgraph.yaml

Build supergraph

ddn supergraph build local

View in console

Click here to launch Console View

Execute a query

query MyQuery {
  albums(limit: 10) {
    title
    artist {
      name
    }
  }
}

And you should see this:

{
  "data": {
    "albums": [
      {
        "title": "For Those About To Rock We Salute You",
        "artist": {
          "name": "AC/DC"
        }
      },
      {
        "title": "Balls to the Wall",
        "artist": {
          "name": "Accept"
        }
      },
      {
        "title": "Restless and Wild",
        "artist": {
          "name": "Accept"
        }
      },
      {
        "title": "Let There Be Rock",
        "artist": {
          "name": "AC/DC"
        }
      },
      {
        "title": "Big Ones",
        "artist": {
          "name": "Aerosmith"
        }
      },
      {
        "title": "Jagged Little Pill",
        "artist": {
          "name": "Alanis Morissette"
        }
      },
      {
        "title": "Facelift",
        "artist": {
          "name": "Alice In Chains"
        }
      },
      {
        "title": "Warner 25 Anos",
        "artist": {
          "name": "Antônio Carlos Jobim"
        }
      },
      {
        "title": "Plays Metallica By Four Cellos",
        "artist": {
          "name": "Apocalyptica"
        }
      },
      {
        "title": "Audioslave",
        "artist": {
          "name": "Audioslave"
        }
      }
    ]
  }
}

Instructions for Testing with Supergraph using a standalone connector instance

NOTE Perform all of these operations from the root of the repo!!!

Start the standalone instance

./run-connector-local.sh file

You can start any adapter by using the names of the adapter with the ./adapters directory.

Create a Supergraph

ddn supergraph init test-connector
cd test-connector

Create the connector HML file

ddn connector-link add calcite --configure-host http://local.hasura.dev:8080
sed -i.bak -e '11,13d' ./app/metadata/calcite.hml

Start the Supergraph

ddn run docker-start

Introspect and add all resources

ddn connector-link update calcite --add-all-resources

Build the Supergraph

ddn supergraph build local

Restart the Supergraph

docker compose down
ddn run docker-start

View in console

Click here to launch Console View

Data File Formats

Format Adapter Status Notes Current Status Market Position Primary Use Case Notable Features Company Initial Release Latest Major Update Community Support Commercial Support
Arrow Tested file mount Growing Niche In-Memory Analytics High Performance Apache 2016 2023 High High
CSV Tested s3, http, file mount, redis caching Stable Mainstream Data Exchange Simple, Widely Supported N/A 1970s N/A High High
JSON Tested s3, http, file mount, redis caching Stable Mainstream Data Exchange Flexible, Human-Readable N/A 2000s N/A High High
XLSX Tested s3, http, file mount, redis caching Stable Mainstream Data Exchange Spreadsheet Format Microsoft 2007 2023 High High
AVRO Not Interested Stable Niche Data Serialization Schema Evolution, Compact Apache 2009 2023 Moderate Moderate
Parquet Tested file mount (s3 could be added) Growing Growing Big Data Analytics Columnar, Compression Apache 2013 2023 High High

Additional Notes

All projection, filtering and sorting are handled in memory. This means that the entire file is read into memory, and then operated on as a table scan. Wide tables - with narrow projections may not perform as well as expected. Large tables may not perform well.

Databases

Database Adapter Status Current Status Market Position Primary Use Case Notable Features Company Initial Release Latest Major Update Community Support Commercial Support
Cassandra Tested Growing Mainstream NoSQL High Scalability Apache 2008 2023 High High
Druid Growing Niche Real-time Analytics Real-time Data Ingestion Apache 2015 2023 High High
Geode Growing Niche In-Memory Data Grid Distributed Apache 2002 2023 High High
InnoDB Stable Mainstream OLTP Transactional Oracle 2000 2023 High High
Redis Growing Mainstream In-Memory Data Store High Performance Redis Labs 2009 2023 High High
Solr Stable Niche Search Full-Text Search Apache 2004 2023 High High
Spark Interesting Growing Mainstream Big Data Processing Distributed Processing Apache 2014 2023 High High
Splunk Growing Mainstream Log Management Real-time Insights Splunk 2003 2023 High High
Kafka Growing Mainstream Stream Processing High Throughput Apache 2011 2023 High High
SQLite Tested Stable Mainstream Embedded Database Lightweight SQLite Consortium 2000 2023 High High
Netezza Not Interested Declining Niche Data Warehousing High Performance IBM 2000s 2022 Moderate Moderate
Redshift Tested Growing Mainstream Data Warehousing Scalable Amazon 2012 2023 High High
Infobright Not Interested Abandoned Niche Analytics Columnar Storage Infobright 2005 2014 Low None
TeraData Interesting Stable Mainstream Data Warehousing High Scalability Teradata 1979 2023 High High
Vertica Interesting Growing Mainstream Analytics Columnar Storage Micro Focus 2005 2023 High High
Sybase Tested Stable Mainstream OLTP Cross-Platform SAP 1980s 2023 Moderate High
StarRocks Interesting Growing Niche Data Warehousing High Performance StarRocks 2020 2023 High High
Snowflake Dup Growing Mainstream Data Warehousing Serverless Snowflake 2014 2023 High High
Databricks Tested Growing Mainstream Data Warehousing Unified Analytics Databricks 2013 2023 High High
Presto Growing Mainstream SQL Query Engine SQL on Hadoop PrestoDB 2013 2023 High High
Pig Not Interested Declining Niche HDFS Map-Reduce Map-Reduce Apache 2006 2023 High High
Trino Tested Growing Mainstream SQL Query Engine SQL on Hadoop PrestoDB 2013 2023 High High
InterBase Stable Niche OLTP Cross-Platform Embarcadero 1980s 2023 Moderate High
Ingres Not Interested Declining Niche OLTP Open Source Actian 1980s 2022 Low Moderate
Informix Stable Niche OLTP High Availability IBM 1980s 2023 Moderate High
HSQLDB Not Interested Declining Niche OLTP Lightweight HSQLDB 2001 2023 Low Moderate
HIVE Tested Stable Mainstream SQL Query Engine JDBC Apache 2010 2023 High High
H2 Tested Stable Niche OLTP Lightweight H2 2004 2023 High High
DB2 Tested Stable Mainstream OLTP High Performance IBM 1983 2023 High High
Access Interesting Stable Mainstream OLTP User-friendly Microsoft 1992 2023 High High
Exasol Growing Mainstream Analytics High Performance Exasol 2000 2023 High High
Firebolt Growing Mainstream Analytics High Performance Firebolt 2020 2023 High High
SQLStream Growing Mainstream Stream Processing Real-time Analytics SQLstream 2009 2023 Moderate Moderate
Jethro Not Interested Declining Niche Analytics High Performance JethroData 2015 2020 Low Moderate
Firebird Stable Niche OLTP Open Source Firebird Foundation 2000 2023 High High
BigQuery Dup/Tested Growing Mainstream Analytics Serverless Google 2010 2023 High High
Clickhouse Dup Growing Mainstream Analytics Columnar Storage Yandex 2016 2023 High High
Oracle Dup Stable Mainstream Database High Performance Oracle 1979 2023 High High
PostgreSQL Dup/Tested Growing Mainstream Database Open Source PostgreSQL 1996 2023 High High
MySQL Dup? Growing Mainstream Database Open Source Oracle 1995 2023 High High
MS SQL Dup? Stable Mainstream Database High Performance Microsoft 1989 2023 High High