A PostgreSQL Change-Data-Capture (CDC) based system for event sourcing.
Many services inside Modular Finance depend on the same core data. This data is useful for many services, but keeping it in sync can be cumbersome. Some projects have tried to break away from the core database while still needing some of its data. Currently, no standardized way of synchronizing data is used. A CDC system allows capturing changes from a database in real-time and propagating those changes to downstream consumers. This can have many uses beyond keeping two SQL databases in sync, such as streaming changes to a specialized search database.
Creek consists of two major parts: producers and consumers. A producer is responsible for listening for change events on a PostgreSQL database and publishing events on a Message Queue (MQ). The MQ used is NATS JetStream. Events are published to topics corresponding to the table name. Generally, a practical system will only consist of one producer database that acts as a single source of truth, but creek is flexible and allows using multiple producers, as long as they have different table names.
A system may consist of multiple consumers, and even different types of consumers. A PostgreSQL consumer has been implemented. This consumer applies changes on from a source table on a topic to a specified table in a consumer PostgreSQL database.
graph TD
source_db -->|changes|source_db
source_db[(Source DB)] --> |Postgres Streaming Logical Replication|producer
producer[Producer] --> |Pub: CREEK.db.wal.namespace.table|nats[NATS JetStream]
producer[Producer] --> |Pub: CREEK.db.schema.namespace.table|nats
nats --> |Sub: CREEK.db.wal.namespace.table|Consumer
The producer works by leveraging Postgres Logical
Replication. It
directly hooks into the Postgres Logical Replication Protocol emitted from
pgoutput via the pglogrepl Go library.
It can then listen to events from the PostgreSQL Write-Ahead Log (WAL) on
specific tables, and emit messages to a NATS JetStream MQ.
Postgres logical replication can be started for a Replication Slot, which corresponds to one consumer of a Postgres Publication. A publication can be defined for specific tables. The replication slot contains information about the current position in the WAL. As such, when restarting the producer, it will continue from the last processed WAL location, and include events that may have happened while the producer was offline.
The messages produced are encoded using the binary Avro data serialization format. This means that messages are encoded in efficient format that allows simple serialization and deserialization. Avro relies on using a schema when both decoding and encoding messages, and the same schema that was used to encode a message must be used when decoding it. Schemas can be uniquely identified by a 64-byte fingerprint.
The creek producer automatically generates Avro schemas based on the columns of the producer PostgreSQL database, and uses them to encode its messages. The producer is responsible for publishing schemas used to encode its messages, and persisting the schemas in order to be able to provide it to clients that request the schema.
The creek producer publishes WAL events for each table on the topic
[creek-ns].[db].wal.[ns].[table], where creek-ns is a global namespace for
creek (by default CREEK), db is the database name, ns is the Postgres
namespace (aka. schema) for the table, and table is the name of the table.
Messages are encoded using Avro, and the messages will have differing schemas.
For example, a message for a table with the following Postgres schema:
CREATE TABLE test (
id int PRIMARY KEY,
name text,
at timestamptz
);Will have the following corresponding Avro schema:
View schema
{
"name": "publish_message",
"type": "record",
"fields": [
{
"name": "fingerprint",
"type": "string"
},
{
"name": "source",
"type": {
"name": "source",
"type": "record",
"fields": [
{
"name": "name",
"type": "string"
},
{
"name": "tx_at",
"type": {
"type": "long",
"logicalType": "timestamp-micros"
}
},
{
"name": "db",
"type": "string"
},
{
"name": "schema",
"type": "string"
},
{
"name": "table",
"type": "string"
},
{
"name": "tx_id",
"type": "long"
},
{
"name": "last_lsn",
"type": "string"
},
{
"name": "lsn",
"type": "string"
}
]
}
},
{
"name": "op",
"type": {
"name": "op",
"type": "enum",
"symbols": [
"c",
"u",
"u_pk",
"d",
"t",
"r"
]
}
},
{
"name": "sent_at",
"type": {
"type": "long",
"logicalType": "timestamp-micros"
}
},
{
"name": "before",
"type": [
"null",
{
"name": "integration_tests",
"type": "record",
"fields": [
{
"name": "id",
"type": {
"type": "string",
"logicalType": "uuid"
},
"pgKey": true,
"pgType": "uuid"
}
]
}
]
},
{
"name": "after",
"type": [
"null",
{
"name": "integration_tests",
"type": "record",
"fields": [
{
"name": "id",
"type": {
"type": "string",
"logicalType": "uuid"
},
"pgKey": true,
"pgType": "uuid"
},
{
"name": "data",
"type": [
"null",
"string"
],
"pgKey": false,
"pgType": "text"
}
]
}
]
}
]
}The creek producer publishes the Avro schemas for each table on the topic
[creek-ns].[db].schema.[ns].[table], where creek-ns is a global namespace
for creek (by default CREEK), db is the database name, ns is the Postgres
namespace for the table, and table is the name of the table. These messages
are sent as plain JSON with the following structure:
{
"fingerprint": "Sykce18MgAQ=", // Base64 url-encoded fingerprint
"schema": "...",
"source": "namespace.table",
"created_at": "YYYY..."
}In addition, the producer persists schemas in the database that it is connected
to. Clients can request this schema using NATS Request-Reply. A client issues
a message to [creek-ns]._schemas with the fingerprint of the schemas, and
will (if available) receive the schema from a producer that has the schema.
The WAL does not contain all data in the database, so in order to be able to get
a consistent view of the database, we need to be able to take snapshots of the
data. A snapshot is taken by the producer. Each snapshot taken will be written
to a separate topic, with the name
[creek-ns].[db].snap.[ns].[table].[ts]_[id]. Here, creek-ns refers to the
global namespace for creek (by default CREEK), db is the database name, ns
is the Postgres namespace for the table, table is the name of the table, ts
is a timestamp of when the snapshot was taken in the form YYYYMMDDHHMMSS_MS,
and id is a 4 character id of the snapshot.
On each snapshot topic, the first message will be a snapshot header containing a JSON record in the following form:
{
"fingerprint": "Sykce18MgAQ=", // Base64 url-encoded fingerprint
"schema": "...",
"tx_id": 6550070, // Postgres transaction id
"lsn": "54/D8213EB8", // Postgres WAL log sequence number (lsn)
"at": "YYYY...", // Timestamp
"approx_rows": 2312
}Following will be 0x45 0x4f 0x46
(EOF).
Clients can request a snapshot using NATS Request-Reply. Clients send a JSON
message on the channel [creek-ns]._snapshot in the following form:
{
"database": "db",
"namespace": "namespace",
"table": "table"
}If the database, namespace, and table exists, a producer will respond with a topic on which the snapshot will written to. The client can now begin reading from this channel.
The producer database requires a user with replication permission, access
allowed in pg_hba.conf, and logical replication enabled in postgresql.conf.
Add a replication line to your pg_hba.conf:
host replication [database] [ip address]/32 md5
Make sure that the following is set in your postgresql.conf:
wal_level=logical
max_wal_senders=5
max_replication_slots=5
Also, it is a (very) good idea to set a max size of the WAL size, otherwise it will grow to infinity when the producer is offline. This option only exists since Postgres 13.
max_slot_wal_keep_size=16GB
The producer is configured using the following environment variables:
PG_URI
PG_PUBLICATION_NAME
PG_PUBLICATION_SLOT
PG_MESSAGE_TIMEOUT
PG_TABLES
NATS_NAMESPACE
NATS_URI
NATS_TIMEOUT
NATS_MAX_PENDING
LOG_LEVEL
PROMETHEUS_PORT
It is also possible to add tables to listen to while the producer is running using a PostgreSQL API. On the same database as the producer is connected to:
-- Add table to listen to
SELECT _creek.add_table('publication_name', 'namespace.table');
-- Remove a table to listen to
SELECT _creek.remove_table('publication_name', 'namespace.table');This project includes a client library for consumers written in Go. Refer to the documentation for more information. Example usage of the client:
package main
import (
"context"
"encoding/json"
"fmt"
"github.com/modfin/creek"
"github.com/nats-io/nats.go"
)
func main() {
conn, err := creek.NewClient(nats.DefaultURL, "CREEK").Connect()
if err != nil {
panic("failed to connect to creek")
}
stream, err := conn.StreamWAL(context.Background(), "db", "namespace.table")
if err != nil {
panic("failed to to stream WAL")
}
for {
msg, err := stream.Next(context.Background())
if err != nil {
panic(fmt.Errorf("failed to get next wal message: %w", err))
}
b, _ := json.Marshal(msg)
fmt.Println(string(b))
}
}There is currently no authentication built into Creek. You will probably want to enable authentication to your NATS cluster. Also, be careful with what tables you export from the producer, since all clients connected to nats will be able to stream WAL events and request full snapshots of this table which will be visible to all clients, even if it might contain sensitive data.
The producer produces Prometheus metrics that are available on
ip:PROMETHEUS_PORT/metrics.
Due to the scope of the project, not all Postgres types are supported. Refer to the pgtype-avro README for a full list of supported types.