This tool allows you to print all unique JSON ASTs from a MongoDB collection.
It contains:
- SBT project files
- Scala source code
- Scala unit tests source code
- No binaries or jar files except for SBT launcher
Requires mongod server process running on:
localhost:27017
Tests can be run without MongoDB server. All Scala dependencies (JARs) will be downloaded by SBT.
run in shell:
sudo apt-get install mongodb
vim /etc/mongodb.conf # uncomment port
service mongodb restart
run in shell:
sudo yum install mongodb-server mongodb
service mongod start
Go to the project root directory.
Download SBT runner by running:
./get_sbt.sh
Now type ./sbt. After this you can issue commands in SBT prompt:
; clean; compile; test; run
Alternatively you can type in shell:
./sbt update clean compile test run
The easiest way to run apps is to type run in SBT and select app number from the list.
Generate a jar that contains all dependencies with SBT command:
oneJar
Afterwards run it like a regular jar (default main class will be set):
java -jar target/scala-2.12/mongodbschema_2.12-0.0.3-one-jar.jar <collection name>
Connection properties can be set in default.props. To change connection settings at runtime
add default.props file to your current directory similar to one found in src/main/resources/props.
Let's insert a few documents into a test collection using mongo shell:
>
> db.docs.insert({name: "Alex", age: 123})
> db.docs.insert({name: "Alex", age: 123})
> db.docs.insert({name: "Alex", age: 123})
> db.docs.insert({name: "Bob"})
> db.docs.insert({age: 123, name: "Alex"})
> db.docs.insert({tags: ["one", "two", "three"]})
> db.docs.insert({tags: ["one", "two", "three"]})
> db.docs.insert({tags: ["one", "two"]})
> db.docs.insert({tags: ["one", "two", "something else"]})
> db.docs.insert({tags: [12, 23434, 344]})
> db.docs.insert({tags: [12, 23434, 344]})
> db.docs.insert({tags: [12, "23434", 344]})
> db.docs.insert({tags: [12, "23434", 344]})
> db.docs.insert({tags: [12, 23434, "344"]})
Find returns these docs which we just inserted:
> db.docs.find()
{ "_id" : ObjectId("516eaf267173e5159f553038"), "name" : "Alex", "age" : 123 }
{ "_id" : ObjectId("516eaf277173e5159f553039"), "name" : "Alex", "age" : 123 }
{ "_id" : ObjectId("516eaf277173e5159f55303a"), "name" : "Alex", "age" : 123 }
{ "_id" : ObjectId("516eaf2d7173e5159f55303b"), "name" : "Bob" }
{ "_id" : ObjectId("516eaf317173e5159f55303c"), "age" : 123, "name" : "Alex" }
{ "_id" : ObjectId("516eaf4e7173e5159f55303d"), "tags" : [ "one", "two", "three" ] }
{ "_id" : ObjectId("516eaf4f7173e5159f55303e"), "tags" : [ "one", "two", "three" ] }
{ "_id" : ObjectId("516eaf537173e5159f55303f"), "tags" : [ "one", "two" ] }
{ "_id" : ObjectId("516eaf607173e5159f553040"), "tags" : [ "one", "two", "something else" ] }
{ "_id" : ObjectId("516eb8fc7173e5159f553041"), "tags" : [ 12, 23434, 344 ] }
{ "_id" : ObjectId("516eb8fe7173e5159f553042"), "tags" : [ 12, 23434, 344 ] }
{ "_id" : ObjectId("516eba217173e5159f553043"), "tags" : [ 12, "23434", 344 ] }
{ "_id" : ObjectId("516eba267173e5159f553044"), "tags" : [ 12, "23434", 344 ] }
{ "_id" : ObjectId("516eba2f7173e5159f553045"), "tags" : [ 12, 23434, "344" ] }
Now if we run the tool we get the following output:
{
"_id":{
"$oid":"String"
},
"name":"String"
}
{
"_id":{
"$oid":"String"
},
"tags":["Double","String"]
}
{
"_id":{
"$oid":"String"
},
"name":"String",
"age":"Double"
}
{
"_id":{
"$oid":"String"
},
"tags":["String"]
}
{
"_id":{
"$oid":"String"
},
"tags":["Double"]
}
These are all unique ASTs/document schemas that the tool found.
Another example of running this tool on fs.chunks (GridFS) - binary data:
{
"_id":{
"$oid":"String"
},
"files_id":{
"$oid":"String"
},
"n":"Integer",
"data":"<Binary Data>"
}
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