Pg-docstore is a kotlin library that allows you to use postgres as a json document store from Kotlin.
Document stores are very useful in some applications and while popular in the nosql world, sql databases like postgres provide a lot of nice functionality ant performance and is therefore a popular choice for data storage. Using postgres as a document store makes a lot of sense. Transactionality ensures that your data is safe. You can use any of a wide range of managed and cloud based service providers or just host it yourself.
At FORMATION, we use pg-docstore to store millions of documents of different types. We use Elasticsearch for querying, aggregations, and other functionality and therefore have no need for the elaborate querying support in databases. But we do like to keep our data safe, which is why we like postgres. Additionally, we like having a clear separation between what we store and what we query on. So, our architecture includes an ETL pipeline that builds our search index from the raw data in pg-docstore.
Add the maven.tryformation.com repository:
repositories {
mavenCentral()
maven("https://maven.tryformation.com/releases") {
content {
includeGroup("com.tryformation")
}
}
}And then the dependency to commonsMain or main:
// check the latest release tag for the latest version
implementation("com.tryformation:pg-docstore:1.x.y")- document store with crud operations for storing and retrieving json documents
- update function that retrieves, applies your lambda to the retrieved document, and then stores in a transaction.
- serialization is done using kotlinx.serialization
- efficient bulk insert/updates
- efficient querying and dumping of the content of the store with database scrolls. We use this for our ETL pipeline.
- nice Kotlin API with suspend functions, flows, strong typing, etc.
This library builds on jasync-postgresql, which is one of the few database drivers out there that is written in Kotlin and that uses non blocking IO.
Pg-docstore uses jasync-postgresql to connect to Postgresql. Unlike many other database frameworks, this framework uses non blocking IO and is co-routine friendly; and largely written in Kotlin too. This makes it a perfect choice for pg-docstore.
val connection = PostgreSQLConnectionBuilder
.createConnectionPool(
ConnectionPoolConfiguration(
host = "localhost",
port = 5432,
database = "docstore",
username = "postgres",
password = "secret",
)
).asSuspending
// recreate the docs table
connection.reCreateDocStoreTable("docs")The reCreateDocStoreSchema call applies the docstore table schema to a docs table and re-creates that.
We'll use the following simple data class as our model. It's annotated with @Serializable. This enables
model serialization using kotlinx.serialization.
@Serializable
data class MyModel(
val title: String,
val description: String? = null,
val categories: List<String> = listOf(),
val id: String = UUID.randomUUID().toString(),
)Using this data class, we can now create a store.
val store = DocStore(
connection = connection,
serializationStrategy = MyModel.serializer(),
tableName = "docs",
idExtractor = MyModel::id,
// optional, used for text search
textExtractor = {
"${it.title} ${it.description}"
},
// optional, used for tag search
tagExtractor = {
it.categories
}
)The call includes three optional arguments. The optional idExtractor extracts an id from your model,
the default implementation simply looks for an id property by name but using a property reference is
slightly more efficient.
To facilitate querying by plain text (using the text search facilities in postgresql) or tags,
there are the textExtractor and tagExtractor fields. This allows you to search across your
models without requiring a lot of knowledge about the internal structure of the json. The default
implementations simply return null.
In the example, we simply concatenate the title and description to enable text search and we use the content of the categories field to enable tag search.
// do some crud
val doc1 = MyModel("Number 1", "a first document", categories = listOf("foo"))
store.create(doc1)
store.getById(doc1.id)?.let {
println("Retrieved ${it.title}")
}
// you can only create the same id once
try {
store.create(doc1)
} catch (e: GenericDatabaseException) {
// fails
println("id already exists")
}
// you can force the conflict be handled
// by overwriting the original
store.create(
doc1.copy(title = "Numero Uno"),
onConflictUpdate = true
)
// now it is changed
store.getById(doc1.id)?.let {
println("Changed title ${it.title}")
}
// This is a better way to do updates
// update by id and modify the retrieved original
store.update(doc1.id) { original ->
// modify the original
original.copy(title = "Number One")
}
// you can also do it like this
store.update(doc1) { original ->
// still fetches the original
original.copy(title = "This is the one")
}
store.getById(doc1.id)?.let {
println("Now it is ${it.title}")
}
// delete a document
store.delete(doc1)
println("now it's gone: ${store.getById(doc1.id)}")
This prints:
Retrieved Number 1
id already exists
Changed title Numero Uno
Now it is This is the one
now it's gone: null
You can get multiple documents in one go like this:
store.create(MyModel(id = "1", title = "Foo"))
store.create(MyModel(id = "2", title = "Bar"))
val docs =
store.multiGetById(listOf("1", "2"))
println(docs.map { it.title })This prints:
[Foo, Bar]
Inserting documents one at the time is not very efficient. If you have large amounts of documents, you should use bulkInsert.
This works with lists or flows. If you use scrolling searches, which return a flow, you can efficiently update large amounts of documents in one go.
// you can also do bulk inserts using flows or lists
flow {
repeat(200) { index ->
emit(
MyModel(
title = "Bulk $index",
description = "A bulk inserted doc #$index",
categories = listOf("bulk")
)
)
// ensure the documents don't have the same timestamp
delay(1.milliseconds)
}
}.let { f ->
// bulk insert 40 at a time
store.bulkInsert(flow = f, chunkSize = 40)
}Querying is a bit limited in pg-docstore; it's meant to be used in combination with things like opensearch or elasticsearch.
However, there are a few ways in which you can get documents out of the store.
// documents are sorted by recency
println("five most recent documents: ${
store.documentsByRecency(limit = 5).map { it.title }
}")
// we can also scroll through the entire table
// and count the number of documents in the flow
println(
"Total documents: ${
// returns a Flow<T>
store.documentsByRecencyScrolling(
// fetch pages of 5 rows
fetchSize = 5
).count()
}"
)
// of course you can also do a select count(*) ...
println(store.count())
// The search can be restricted using tags
// you need to set up the tagExtractor functino
// in the DocStore params for this to work
println(
"Just the bulk tagged documents: ${
store.documentsByRecencyScrolling(
// filters on the extracted tags
tags = listOf("bulk")
).count()
}"
)
This prints:
five most recent documents: [Bulk 199, Bulk 198, Bulk 197, Bulk 196, Bulk 195]
Total documents: 202
202
Just the bulk tagged documents: 200
While no substitute for a proper search engine, postgres has some text search facilities. We use a simple trigram index which is great for common use cases for things like matching and ranking on ids, email addresses, parts of words, etc.
store.create(MyModel("The quick brown fox"))
// or search on the extracted text
println("Found:")
store.documentsByRecency(
query = "brown fox",
// will produce a lot of hits
// increase for stricter match
similarityThreshold = 0.01,
limit = 5
).forEach {
println("-${it.title}")
}This prints:
Found:
-The quick brown fox
-Foo
-Bar
-Bulk 2
-Bulk 1
Most operations in the docstore are single sql statements and don't use a transaction. However, you can of course control this if you need to, e.g. modify multiple documents in one transaction.
The transact function creates a new docstore with it's own isolated connection and the same parameters
as its parent. The isolated connection is used for the duration of
the transaction and exclusive to your code block. This prevents other parts of your code from sending sql commands on the connection.
If you are used to blocking IO frameworks this may be a bit surprising. However, we need this because connections in jasync are shared and queries are non blocking. Without using an isolated connection, other parts of your code might run their own queries in your transaction, which of course is not desirable.
store.transact { tStore ->
// everything you do with tStore is
// one big transaction
tStore.create(
MyModel(
title = "One more",
categories = listOf("transaction1")
)
)
tStore.create(
MyModel(
title = "And this one too",
categories = listOf("transaction1")
)
)
}
println(
"Both docs exist: ${
store.documentsByRecency(tags = listOf("transaction1")).count()
} after the transaction"
)This prints:
Both docs exist: 2 after the transaction
In case of an exception, there is a rollback.
// rollbacks happen if there are exceptions
val another = MyModel(
title = "Transactional",
description = "yet another document",
categories = listOf("foo")
)
runCatching {
store.transact { tStore ->
tStore.create(another)
println(
"in the transaction it exists as: ${
tStore.getById(another.id)?.title
}"
)
// simulate an error
// causes a rollback and rethrows
error("oopsie")
}
}
// prints null because the transaction was rolled back
println("after the rollback ${store.getById(another.id)?.title}")This prints:
in the transaction it exists as: Transactional
after the rollback null
As FORMATION grows, we will no doubt need more features. Some features that come to mind are sharding, utilizing some of the json features in postgres, geospatial features, or other featutes.
This is a relatively new project; so there may be some bugs, design flaws, etc. However, I've implemented similar stores many times before in past projects and I think I know what I'm doing. If it works for us, it might also work for you.
Give it a try!
The code is provided as is under the MIT. If you are planning to make a contribution, please reach out via the issue tracker first.
This readme is generated with kotlin4example