1. Java SQL Transpiler Library¶

Transpile, Resolve, Lineage – A Database independent, stand-alone SQL Transpiler, Column Resolver and Lineage Tracer written in pure Java, translating various large RDBMS SQL Dialects into a few smaller RDBMS Dialects for Unit Testing.

JSQLTranspiler allows you to develop and test your Big Data SQL at no cost on a local DuckDB instance before deploying and running it in the cloud.

Internal Functions will be rewritten based on the actual meaning and purpose of the function, respecting different function arguments’ count, order and type. Rewrite of Window- and Aggregate-Functions as well.

Download¶

A – Binaries

Java 11 Binaries¶
Flavor	File	Size
Java Library Stable	JSQLTranspiler-0.6.jar	(80 kb)
Java Library Snapshot	JSQLTranspiler-0.7-SNAPSHOT.jar	(80 kb)
CLI Fat JAR Stable	JSQLTranspilerCLI-0.4-all.jar	(1.3 MB)
CLI Fat JAR Snapshot	JSQLTranspilerCLI-0.5-SNAPSHOT-all.jar	(1.3 MB)

B – Source Code

Like us on the JSQLTranspiler Git Repository

Cloning the repository¶

git clone https://github.com/starlake-ai/JSQLTranspiler.git
cd JSQLTranspiler
./gradlew build

C – Maven Release

<dependency>
    <groupId>com.starlake-ai.jsqltranspiler</groupId>
    <artifactId>jsqltranspiler</artifactId>
    <version>0.6</version>
</dependency>

D – Maven Snapshot

<repositories>
    <repository>
        <id>jsqltranspiler-snapshots</id>
        <snapshots>
            <enabled>true</enabled>
        </snapshots>
        <url>https://s01.oss.sonatype.org/content/repositories/snapshots/</url>
    </repository>
</repositories>
<dependency>
    <groupId>com.starlake-ai.jsqltranspiler</groupId>
    <artifactId>jsqltranspiler</artifactId>
    <version>0.7-SNAPSHOT</version>
</dependency>

E – Gradle Release

repositories {
    mavenCentral()
}

dependencies {
    implementation 'com.starlake-ai.jsqltranspiler:jsqltranspiler:0.6'
}

F – Gradle Snapshot

repositories {
    maven {
        url = uri('https://s01.oss.sonatype.org/content/repositories/snapshots/')
    }
}

dependencies {
    implementation 'com.starlake-ai.jsqltranspiler:jsqltranspiler:0.7-SNAPSHOT'
}

Examples¶

1 – Transpiling

Rewrite Google BigQuery to DuckDB and execute¶

-- Google BigQuery
SELECT
    DATE(2016, 12, 25) AS date_ymd,
    DATE(DATETIME '2016-12-25 23:59:59') AS date_dt,
    DATE(TIMESTAMP '2016-12-25 05:30:00+07', 'America/Los_Angeles') AS date_tstz;

-- Rewritten DuckDB compliant statement
SELECT
    MAKE_DATE(2016, 12, 25) AS date_ymd,
        CAST(DATETIME '2016-12-25 23:59:59' AS DATE) AS date_dt,
        CAST(TIMESTAMP '2016-12-25 05:30:00+07' AT TIME ZONE 'America/Los_Angeles' AS DATE) AS date_tstz;

-- Same Tally
1

-- Same Result
"date_ymd","date_dt","date_tstz"
"2016-12-15","2016-12-15","2016-12-15"

2 – Resolving Columns

Resolve the Star Operator of a deeply nested Query with disguised physical columns¶

/* Schema:
Table a: Columns col1, col2, col3, colAA, colBA
Table b: Columns col1, col2, col3, colBA, colBB
*/

-- provided SELECT with STAR Operators
SELECT *
FROM (  (   SELECT *
            FROM b ) c
            INNER JOIN a
                ON c.col1 = a.col1 ) d
;

-- Resolved Columns via JSQLColumnResolver.rewrite(...)
-- Without needing an actual database connection
SELECT  d.col1
        , d.col2
        , d.col3
        , d.colBA
        , d.colBB
        , d.col1_1
        , d.col2_1
        , d.col3_1
        , d.colAA
        , d.colAB
FROM (  (   SELECT  b.col1
                    , b.col2
                    , b.col3
                    , b.colBA
                    , b.colBB
            FROM b ) c
            INNER JOIN a
                ON c.col1 = a.col1 ) d
;

3 – Tracing Lineage

Trace the actual physical columns¶

/* Schema:
Table a: Columns col1, col2, col3, colAA, colBA
Table b: Columns col1, col2, col3, colBA, colBB
*/

-- provided SELECT with STAR Operator
-- Without needing an actual database connection
SELECT  Sum( colBA + colBB ) AS total
        , ( SELECT col1 AS test
            FROM b ) col2
        , CURRENT_TIMESTAMP() AS col3
FROM a
    INNER JOIN (    SELECT *
                    FROM b ) c
        ON a.col1 = c.col1
;

Trace of the physical tables and columns¶

<?xml version="1.0" encoding="UTF-8"?>
<ColumnSet>
    <Column alias='total' name='Sum'>
        <ColumnSet>
            <Column name='Addition'>
                <ColumnSet>

                    <!-- scope points on the actual physical column b.colBA -->
                    <Column name='colBA' table='c' scope='b.colBA' dataType='java.sql.Types.OTHER' typeName='Other' columnSize='0' decimalDigits='0' nullable=''/>

                    <!-- scope points on the actual physical column b.colBB -->
                    <Column name='colBB' table='c' scope='b.colBB' dataType='java.sql.Types.OTHER' typeName='Other' columnSize='0' decimalDigits='0' nullable=''/>

                </ColumnSet>
            </Column>
        </ColumnSet>
    </Column>
    <Column alias='col2' name='col1'>
        <ColumnSet>
            <Column alias='test' name='col1' table='b' dataType='java.sql.Types.OTHER' typeName='Other' columnSize='0' decimalDigits='0' nullable=''/>
        </ColumnSet>
    </Column>
    <Column alias='col3' name='CURRENT_TIMESTAMP'/>
</ColumnSet>

4 – Find actual tables

Retrieve list of actual physical tables¶

String sqlStr = "SELECT * FROM (SELECT * FROM A) AS A \n" +
        "JOIN B ON A.a = B.a \n" +
        "JOIN C ON A.a = C.a;";
Set<String> tables = TablesNamesFinder.findTablesOrOtherSources(sqlStr);
assertThat(tables).containsExactlyInAnyOrder("A", "B", "C");

tables = TablesNamesFinder.findTables(sqlStr);
assertThat(tables).containsExactlyInAnyOrder("B", "C");

SQL Dialects¶

JSQLTranspiler currently understands the following Big RDBMS dialects:

Google BigQuery

Databricks

Snowflake

Amazon Redshift

and rewrites into to the following small RDBMS dialects:

DuckDB

planned: H2

planned: Postgres

Features¶

Comprehensive support for Query and DML statements (INSERT, DELETE, UPDATE, MERGE)

RDBMS specific Functions, Predicates and Operators

RDBMS specific Date and Number formatting parameters

Extensive ARRAY, ROW and STRUCT support

Deeply Nested Expressions such as correlated Sub-Selects, CTE’s and WITH clauses

Explicit and Implicit Cast expressions, e. g. DATE '2023-12-31', '2023-12-31'::Date and Cast('2023-12-31' AS Date)

SQL Named and Ordinal Parameters: ?, ?1 or :parameter

Lateral Table and Sub-Select Functions, e. g. UNNEST(), TABLE()

Window and Aggregate Functions

Columns Resolution for EXCEPT and REPLACE filters as well as for USING joins (left or right)