sql-to-mongo-db-query-converter 
sql-to-mongo-db-query-converter helps you build quieres for MongoDb based on Queries provided in SQL.
Maven
Add a dependency to com.github.vincentrussell:sql-to-mongo-db-query-converter.
<dependency>
<groupId>com.github.vincentrussell</groupId>
<artifactId>sql-to-mongo-db-query-converter</artifactId>
<version>1.21</version>
</dependency>
Requirements
- JDK 1.8 or higher
Running it from Java
QueryConverter queryConverter = new QueryConverter.Builder().sqlString("select column1 from my_table where value NOT IN ("theValue1","theValue2","theValue3")").build();
MongoDBQueryHolder mongoDBQueryHolder = queryConverter.getMongoQuery();
String collection = mongoDBQueryHolder.getCollection();
Document query = mongoDBQueryHolder.getQuery();
Document projection = mongoDBQueryHolder.getProjection();
Document sort = mongoDBQueryHolder.getSort();
Running it as a standalone jar
java -jar sql-to-mongo-db-query-converter-1.21-standalone.jar -s sql.file -d destination.json
Options
usage: com.github.vincentrussell.query.mongodb.sql.converter.Main [-s
<arg> | -sql <arg> | -i] [-d <arg> | -h <arg>] [-db <arg>] [-a
<arg>] [-u <arg>] [-p <arg>] [-b <arg>]
-s,--sourceFile <arg> the source file.
-sql,--sql <arg> the select statement
-i,--interactiveMode interactive mode
-l,--loopMode interactive loopMode mode
-d,--destinationFile <arg> the destination file. Defaults to
System.out
-h,--host <arg> hosts and ports in the following format
(host:port) default port is 27017
-db,--database <arg> mongo database
-a,--auth database <arg> auth mongo database
-u,--username <arg> usename
-p,--password <arg> password
-b,--batchSize <arg> batch size for query results
Special Aggregation-specific System Properties
-DaggregationAllowDiskUse
Enables writing to temporary files. When set to true, aggregation operations can write data to the _tmp subdirectory in the dbPath directory.
-DaggregationBatchSize
To specify an initial batch size for the cursor
Interactive mode
java -jar target/sql-to-mongo-db-query-converter-1.18-standalone.jar -i
Enter input sql:
select object.key1, object2.key3, object1.key4 from my_collection where object.key2 = 34 AND object2.key4 > 5
******Result:*********
db.my_collection.find({
"$and": [
{
"key2": {
"$numberLong": "34"
}
},
{
"object2.key4": {
"$gt": {
"$numberLong": "5"
}
}
}
]
} , {
"_id": 0,
"object.key1": 1,
"object2.key3": 1,
"object1.key4": 1
})
##Available options
###Dates
select * from my_table where date(column,'YYYY-MM-DD') >= '2016-12-12'
******Result:*********
db.my_table.find({
"column": {
"$gte": {
"$date": 1452556800000
}
}
})
###Natural Language Dates
select * from my_table where date(column,'natural') >= '5000 days ago'
******Result:*********
db.my_table.find({
"column": {
"$gte": {
"$date": 1041700019654
}
}
})
###Regex
select * from my_table where regexMatch(column,'^[ae"gaf]+$')
******Result:*********
db.my_table.find({
"column": {
"$regex": "^[ae\"gaf]+$"
}
})
###NOT Regex match
select * from my_table where notRegexMatch(column,'^[ae"gaf]+$')
******Result:*********
db.my_table.find({
"column": {
"$not": /^[ae\"gaf]+$/
}
})
###Distinct
select distinct column1 from my_table where value IS NULL
******Result:*********
db.my_table.distinct("column1" , {
"value": {
"$exists": false
}
})
###Like
select * from my_table where value LIKE 'start%'
******Result:*********
db.my_table.find({
"value": {
"$regex": "^start.*$"
}
})
###Like
select * from my_table where value NOT LIKE 'start%'
******Result:*********
db.my_table.find({
"value": {
"$not": /^start.*$/
}
})
###In
select column1 from my_table where value IN ("theValue1","theValue2","theValue3")
******Result:*********
db.my_table.find({
"value" : {
"$in" : ["theValue1","theValue2", "theValue3"]
}
})
###Not In
select column1 from my_table where value NOT IN ("theValue1","theValue2","theValue3")
******Result:*********
db.my_table.find({
"value" : {
"$nin" : ["theValue1","theValue2", "theValue3"]
}
})
###Is True
select column1 from my_table where column = true
******Result:*********
db.my_table.find({
"column" : true
})
###Is False
select column1 from my_table where column = false
******Result:*********
db.my_table.find({
"column" : false
})
###Not True
select column1 from my_table where NOT column
******Result:*********
db.my_table.find({
"value" : {$ne: true}
})
###ObjectId Support
select column1 from where OBJECTID('_id') IN ('53102b43bf1044ed8b0ba36b', '54651022bffebc03098b4568')
******Result:*********
db.my_table.find({
"_id" : {$in: [{$oid: "53102b43bf1044ed8b0ba36b"},{$oid: "54651022bffebc03098b4568"}]}
})
select column1 from where OBJECTID('_id') = '53102b43bf1044ed8b0ba36b'
******Result:*********
db.my_table.find({
"_id" : {$oid: "53102b43bf1044ed8b0ba36b"}
})
###Delete
delete from my_table where value IN ("theValue1","theValue2","theValue3")
******Result:*********
3 (number or records deleted)
###Update
UPDATE my_table SET name = 'John Doe', city= 'Melphis' WHERE customerID = 1;
******Result:*********
1 (number or records updated)
###Group By (Aggregation)
select borough, cuisine, count(*) from my_collection WHERE borough LIKE 'Queens%' GROUP BY borough, cuisine ORDER BY count(*) DESC;
******Mongo Query:*********
db.my_collection.aggregate([{
"$match": {
"borough": {
"$regex": "^Queens.*$"
}
}
},{
"$group": {
"_id": {
"borough": "$borough",
"cuisine": "$cuisine"
},
"count": {
"$sum": 1
}
}
},{
"$sort": {
"count": -1
}
},{
"$project": {
"borough": "$_id.borough",
"cuisine": "$_id.cuisine",
"count": 1,
"_id": 0
}
}])
###Having clause with aggregation
select Restaurant.cuisine, count(*) from Restaurants group by Restaurant.cuisine having count(*) > 3;
******Mongo Query:*********
db.Restaurants.aggregate([
{
"$group": {
"_id": "$Restaurant.cuisine",
"count": {
"$sum": 1
}
}
},
{
"$match": {
"$expr": {
"$gt": [
"$count",
3
]
}
}
},
{
"$project": {
"Restaurant.cuisine": "$_id",
"count": 1,
"_id": 0
}
}
])
###Count without GROUP BY
select count(*) as c from table
******Mongo Query:*********
db.table.aggregate([{ "$group": { "_id": {}, "c": { "$sum": 1 } } },{ "$project": { "c": 1, "_id": 0 } }])
###Avg without GROUP BY
select avg(field) as avg from table
******Mongo Query:*********
db.table.aggregate([{ "$group": { "_id": {}, "avg": { "$avg": "$field" } } },{ "$project": { "avg": 1, "_id": 0 } }])
###Joins
select t1.column1, t2.column2 from my_table as t1 inner join my_table2 as t2 on t1.column = t2.column
******Result:*********
db.my_table.aggregate([
{
"$match": {}
},
{
"$lookup": {
"from": "my_table2",
"let": {
"column": "$column"
},
"pipeline": [
{
"$match": {
"$expr": {
"$eq": [
"$$column",
"$column"
]
}
}
}
],
"as": "t2"
}
},
{
"$unwind": {
"path": "$t2",
"preserveNullAndEmptyArrays": false
}
},
{
"$project": {
"_id": 0,
"column1": 1,
"t2.column2": 1
}
}
])
or
select t1.Column1, t2.Column2 from my_table as t1 inner join my_table2 as t2 on t1.nested1.Column = t2.nested2.Column inner join my_table3 as t3 on t1.nested1.Column = t3.nested3.Column where t1.nested1.whereColumn1 = "whereValue1" and t2.nested2.whereColumn2 = "whereValue2" and t3.nested3.whereColumn3 = "whereValue3"
******Result:*********
db.my_table.aggregate([
{
"$match": {
"nested1.whereColumn1": "whereValue1"
}
},
{
"$lookup": {
"from": "my_table2",
"let": {
"nested1_column": "$nested1.Column"
},
"pipeline": [
{
"$match": {
"$and": [
{
"$expr": {
"$eq": [
"$$nested1_column",
"$nested2.Column"
]
}
},
{
"nested2.whereColumn2": "whereValue2"
}
]
}
}
],
"as": "t2"
}
},
{
"$unwind": {
"path": "$t2",
"preserveNullAndEmptyArrays": false
}
},
{
"$lookup": {
"from": "my_table3",
"let": {
"nested1_column": "$nested1.Column"
},
"pipeline": [
{
"$match": {
"$and": [
{
"$expr": {
"$eq": [
"$$nested1_column",
"$nested3.Column"
]
}
},
{
"nested3.whereColumn3": "whereValue3"
}
]
}
}
],
"as": "t3"
}
},
{
"$unwind": {
"path": "$t3",
"preserveNullAndEmptyArrays": false
}
},
{
"$project": {
"_id": 0,
"c1": "$Column1",
"c2": "$t2.Column2",
"c3": "$t3.Column3"
}
}
])
###Alias
select object.key1 as key1, object2.key3 as key3, object1.key4 as key4 from my_collection where object.key2 = 34 AND object2.key4 > 5;
******Mongo Query:*********
db.Restaurants.aggregate([{
"$match": {
"$and": [
{
"Restaurant.cuisine": "American"
},
{
"Restaurant.borough": {
"$gt": "N"
}
}
]
}
},{
"$project": {
"_id": 0,
"key1": "$Restaurant.borough",
"key3": "$Restaurant.cuisine",
"key4": "$Restaurant.address.zipcode"
}
}])
###Alias Group By (Aggregation)
select borough as b, cuisine as c, count(*) as co from my_collection WHERE borough LIKE 'Queens%' GROUP BY borough, cuisine ORDER BY count(*) DESC;
******Mongo Query:*********
db.my_collection.aggregate([{
"$match": {
"borough": {
"$regex": "^Queens.*$"
}
}
},{
"$group": {
"_id": {
"borough": "$borough",
"cuisine": "$cuisine"
},
"co": {
"$sum": 1
}
}
},{
"$sort": {
"co": -1
}
},{
"$project": {
"b": "$_id.borough",
"c": "$_id.cuisine",
"co": 1,
"_id": 0
}
}])
###Offset
select * from table limit 3 offset 4
or
select a, count(*) from table group by a limit 3 offset 4
******Result:*********
is equivalent to the $skip function in mongodb json query language
###Using column names that start with a number. Sorround it in quotes:
SELECT * FROM tb_test WHERE "3rd_column" = 10
###Direct Mongo Integration
You can run the queries against an actual mongodb database and take a look at the results. The default return batch size is 50.
java -jar target/sql-to-mongo-db-query-converter-1.18-SNAPSHOT-standalone.jar -i -h localhost:3086 -db local -b 5
Enter input sql:
select borough, cuisine, count(*) from my_collection GROUP BY borough, cuisine ORDER BY count(*) DESC;
******Query Results:*********
[{
"borough" : "Manhattan",
"cuisine" : "American ",
"count" : 3205
},{
"borough" : "Brooklyn",
"cuisine" : "American ",
"count" : 1273
},{
"borough" : "Queens",
"cuisine" : "American ",
"count" : 1040
},{
"borough" : "Brooklyn",
"cuisine" : "Chinese",
"count" : 763
},{
"borough" : "Queens",
"cuisine" : "Chinese",
"count" : 728
}]
more results? (y/n): y
[{
"borough" : "Manhattan",
"cuisine" : "Café/Coffee/Tea",
"count" : 680
},{
"borough" : "Manhattan",
"cuisine" : "Italian",
"count" : 621
},{
"borough" : "Manhattan",
"cuisine" : "Chinese",
"count" : 510
},{
"borough" : "Manhattan",
"cuisine" : "Japanese",
"count" : 438
},{
"borough" : "Bronx",
"cuisine" : "American ",
"count" : 411
}]
more results? (y/n): n
Change Log
1.21 (2022-03-21)
Enhancements:
- Add support for functions in the projection clause
- Handling speciality functions with IS NULL and IS NOT NULL expression
1.20 (2022-01-23)
Enhancements:
- upgrade jsqlparser to 4.3
- Added support for update SQL statements
Bugs:
- equals operand not working with dates
1.19 (2021-12-05)
Enhancements:
- upgrade flapdoodle to 3.0.0
- upgrade required jdk to 1.8
- upgrade mongoclient to 4.2.3
- upgrade jsqlparser to 4.2
Bugs:
- alias not working properly in having clause.
- in expressions not working with joins
1.18 (2020-09-02)
Bugs:
- functions not handled properly with operators like $eq
1.17 (2020-08-21)
Enhancements:
- support for BETWEEN keyword
Bugs:
- Remove use of $eq operator when not needed
Change Log
1.16 (2020-08-18)
Enhancements:
- remove deprecated constructors on QueryConverter
Bugs:
- Simplifying use of $expr function
1.15 (2020-08-15)
Enhancements:
- Added getQueryAsDocument to the QueryConverter
- Upgraded guava to 24.1.1-jre
- Added "Loop mode" with -l when running in CLI mode.
Bugs:
- Added checkstyle to build process
- Code cleanup
- Negative numbers are supported
Change Log
1.14 (2020-07-22)
Enhancements:
- Support for Double values
- Support for Timestamp values, i.e: {ts '2019-10-11 12:12:23.234'}
- Support for Date values, i.e: {d '2019-10-11'}
1.13 (2020-07-16)
Enhancements:
- Support for NOT LIKE queries
- added notRegexMatch function
Bugs:
- NOT expressions with parentheses not working properly, i.e: NOT (Country = 'Albania')
1.12 (2020-07-07)
Enhancements:
- Fix queries using IN keyword. They were broken in 1.11
1.11 (2020-05-22)
Enhancements:
- Support for subqueries translated to aggregation steps in a recursive way.
- Created a builder for the QueryConverter class and depreciated the constructors for the QueryConverterClass
- Created the ability to prove the following options to aggregation: aggregationAllowDiskUse and aggregationBatchSize
- Support for having clause
- Upgrading com.github.jsqlparser:jsqlparser from v1.4 to v3.1
- Supporting group operations (avg, max, min, count, sum) without "group by" clause for performing a total group.
Change Log
1.10 (2020-02-01)
Enhancements:
- Added the ability to use sql aliases that will do a mongo $project.
- Added the ability to use offset syntax in sql to skip records
- Added the ability to use lookup-let-pipeline strategy of mongo 3.6 and $expr for performing joins.
- Added the ablility to join multiple tables and use them in where or project clause. In the new test class are many examples.
1.9 (2019-04-02)
Enhancements:
- Added the capability for nested custom functions
1.8 (2019-02-01)
Enhancements:
- Upgraded jsqlparser to version 1.4
- Added support for deep nested queries; i.e: select * from my_table where a.b.c.d.e.key = value
1.7 (2018-11-13)
Enhancements:
- Equals, Not Equals, In and Not In ObjectId query support
- regexMatch function can be used with or without equals sign
1.6 (2018-07-24)
Bugs:
- remove double quotes from column names when used in IS NULL query
1.5 (2018-06-15)
Enhancements:
- upgrade jsqlparser to version 1.2
- create flatter structure when chaining ORs and ANDs together
1.4 (2018-03-03)
Enhancements:
- Added support NOT operator on parentheses
- Added support for delete SQL statements
1.3.4 (2018-01-27)
Enhancements:
- Added the ability to pass down custom sql functions down to mongo
1.3.2 (2017-07-02)
Enhancements:
- Added the ability to support queries on boolean fields
- UTF-8 support
1.3.1 (2017-02-19)
Enhancements:
- Added the ability to have default type like Number, String, or Date
- Added the ability to provide a type for each field like Number, String, Date
1.3 (2017-01-31)
Enhancements:
- Added the ability to provide field types for columns passed into the query via Java API. See QueryConverterTest for examples.
1.2 (2016-11-30)
Bugs:
- Fix bug with IN and NOT IN expressions from not converting properly to mongo format properly
1.1 (2016-10-05)
Bugs:
- Fix bug with not being able to parse like queries