pgdog Documentation

1. Project Overview & Quickstart (pgdogdev/pgdog) Copy Chapter

File: README.md Copy Chapter

PgDog is a proxy for scaling PostgreSQL. It supports connection pooling, load balancing queries and sharding entire databases. Written in Rust, PgDog is fast, secure and can manage thousands of connections on commodity hardware.

Documentation Copy Chapter

📘 PgDog documentation can be found here. Any questions? Chat with us on Discord.

Quick start Copy Chapter

Kubernetes Copy Section

Helm chart is here. To install it, run:

helm repo add pgdogdev https://helm.pgdog.dev
helm install pgdog pgdogdev/pgdog

AWS Copy Section

If you're using AWS RDS, you can deploy PgDog using one of two supported methods:

1. Helm chart with EKS, or a self-hosted Kubernetes cluster
2. Terraform module to deploy PgDog on ECS

Try in Docker Copy Section

You can try PgDog quickly using Docker. Install Docker Compose and run:

docker-compose up

Once started, you can connect to PgDog with psql or any other PostgreSQL client:

PGPASSWORD=postgres psql -h 127.0.0.1 -p 6432 -U postgres

The demo comes with 3 shards and 2 sharded tables:

INSERT INTO users (id, email) VALUES (1, '[email protected]');
INSERT INTO payments (id, user_id, amount) VALUES (1, 1, 100.0);

SELECT * FROM users WHERE id = 1;
SELECT * FROM payments WHERE user_id = 1;

Features Copy Chapter

📘 Configuration

All PgDog features are configurable and can be turned on and off. PgDog requires 2 configuration files to operate:

1. pgdog.toml: hosts, sharding configuration, and other settings
2. users.toml: usernames and passwords

Example Copy Section

Most options have reasonable defaults, so a basic configuration for a single user
and database running on the same machine is pretty short:

pgdog.toml

[general]
port = 6432
default_pool_size = 10

[[databases]]
name = "pgdog"
host = "127.0.0.1"

users.toml

[[users]]
name = "alice"
database = "pgdog"
password = "hunter2"

If a database in pgdog.toml doesn't have a user in users.toml, the connection pool for that database will not be created and users won't be able to connect.

If you'd like to try it out locally, create the database and user like so:

CREATE DATABASE pgdog;
CREATE USER pgdog PASSWORD 'pgdog' LOGIN;

Transaction pooling Copy Section

📘 Transactions

Like PgBouncer, PgDog supports transaction (and session) pooling, allowing
thousands of clients to use just a few PostgreSQL server connections.

Unlike PgBouncer, PgDog can parse and handle SET statements and startup options, ensuring session state is set correctly when sharing server connections between clients with different parameters.

PgDog also has more advanced connection recovery options, like automatic abandoned transaction rollbacks and connection re-synchronization to avoid churning server connections during an application crash.

Load balancer Copy Section

📘 Load balancer

PgDog is an application layer (OSI Level 7) load balancer for PostgreSQL. It understands the Postgres protocol, can proxy multiple replicas (and primary) and distributes transactions evenly between databases. The load balancer supports 3 strategies: round robin, random and least active connections.

Example

The load balancer is enabled automatically when a database has more than one host:

[[databases]]
name = "prod"
host = "10.0.0.1"
role = "primary"

[[databases]]
name = "prod"
host = "10.0.0.2"
role = "replica"

Health checks

📘 Healthchecks

PgDog maintains a real-time list of healthy hosts. When a database fails a health check, it's removed from the active rotation and queries are re-routed to other replicas. This works like an HTTP load balancer, except it's for your database.

Health checks maximize database availability and protect against bad network connections, temporary hardware failures or misconfiguration.

Single endpoint

📘 Single endpoint

PgDog uses pg_raw_parse, which includes the PostgreSQL native parser. By parsing queries, PgDog can detect writes (e.g. INSERT, UPDATE, CREATE TABLE, etc.) and send them to the primary, leaving the replicas to serve reads (SELECT). This allows applications to connect to the same PgDog deployment for both reads and writes.

Transactions

📘 Load balancer & transactions

Transactions can execute multiple statements, so in a primary & replica configuration, PgDog routes them to the primary. Clients can indicate a transaction is read-only, in which case PgDog will send it to a replica:

BEGIN READ ONLY;
-- This goes to a replica.
SELECT * FROM users LIMIT 1;
COMMIT;

Failover

📘 Failover

PgDog monitors Postgres replication state and can automatically redirect writes to a different database if a replica is promoted. This doesn't replace tools like Patroni that actually orchestrate failovers. You can use PgDog alongside Patroni (or AWS RDS or other managed Postgres host), to gracefully failover live traffic.

Example

To enable failover, set all database role attributes to auto and enable replication monitoring (lsn_check_delay setting):

[general]
lsn_check_delay = 0

[[databases]]
name = "prod"
host = "10.0.0.1"
role = "auto"

[[databases]]
name = "prod"
host = "10.0.0.2"
role = "auto"

Authentication Copy Section

📘 Authentication

PgDog supports five authentication methods:

1. Password-based
2. AWS RDS IAM
3. Azure Workload Identity
4. HashiCorp Vault dynamic credentials
5. HashiCorp Vault static role credentials

Password-based authentication

Password-based authentication allows for clients to authenticate to PgDog and for PgDog to authenticate to PostgreSQL. It currently supports the following password hashing algorithms:

• SCRAM-SHA-256
• MD5
• Plain

RDS IAM backend authentication

PgDog can keep client-to-PgDog authentication unchanged while using AWS RDS IAM tokens for PgDog-to-PostgreSQL authentication on a per-user basis.

Example

[[users]]
name = "alice"
database = "pgdog"
password = "client-password"
server_auth = "rds_iam"
# Optional; PgDog infers region from *.region.rds.amazonaws.com(.cn) hostnames when omitted.
# server_iam_region = "us-east-1"

When any user has server_auth = "rds_iam", the following settings must be configured as well:

• tls_verify must not be "disabled".
• passthrough_auth must be "disabled".

Azure Workload Identity authentication

PgDog can also use Azure Workload Identity for PgDog-to-PostgreSQL authentication, while keeping client-to-PgDog authentication unchanged. This is configured on a per-user basis, similarly to RDS IAM:

Example

[[users]]
name = "alice"
database = "pgdog"
password = "client-password"
server_auth = "azure_workload_identity"

When any user has server_auth = "azure_workload_identity", the following settings must be configured as well:

• tls_verify must not be "disabled".
• passthrough_auth must be "disabled".

HashiCorp Vault dynamic role authentication

PgDog can fetch dynamic database credentials (username and password) from HashiCorp Vault's database secrets engine, while keeping client-to-PgDog authentication unchanged. Credentials are cached and rotated automatically after a configured percentage of the Vault lease has elapsed.

Example

In users.toml:

[[users]]
name = "alice"
database = "pgdog"
password = "client-password"
server_auth = "vault_dynamic"
server_vault_path = "database/creds/pgdog"
# Refresh credentials after 80% of the lease has elapsed (default).
# vault_refresh_percent = 80

In pgdog.toml:

[vault]
url = "https://vault.internal:8200"
auth_method = "kubernetes" # or "approle"
kubernetes_role = "pgdog"

PgDog logs into Vault with Kubernetes auth (using the pod's service account JWT) or AppRole (approle_role_id plus approle_secret_id_file or the VAULT_SECRET_ID environment variable).

When any user has server_auth = "vault_dynamic" or "vault_static", the following settings must be configured as well:

• tls_verify must not be "disabled".
• passthrough_auth must be "disabled".

HashiCorp Vault static role authentication

Unlike dynamic credentials, a Vault static database role has a fixed username and only its password rotates, on a schedule Vault manages. PgDog supports two independent uses of a static role, they don't need to point at the same role, and each has its own username setting:

• vault_path: verify the password a client sends to PgDog against Vault's current password for the role, instead of a statically configured password.
• server_auth = "vault_static" with server_vault_path: use the role's Vault-managed password for PgDog-to-PostgreSQL connections. Unlike vault_dynamic, PgDog doesn't take the username from Vault, it connects as server_user or name, if server_user isn't set.

Example

In users.toml, for a client authenticating as alice (verified against a static role registered under that same name) while PgDog connects to Postgres as pgdog_service (a separate static role):

[[users]]
name = "alice"
database = "pgdog"
vault_path = "database/static-creds/alice"
server_user = "pgdog_service"
server_auth = "vault_static"
server_vault_path = "database/static-creds/pgdog-service"

In pgdog.toml, the same [vault] section used for dynamic credentials applies.

Both settings are optional and independent: set only vault_path to verify client passwords while keeping any other backend authentication method, or only server_auth = "vault_static" to use a static role for backend connections while clients authenticate with a regular password.

Sharding Copy Section

📘 Sharding

PgDog is able to manage databases with multiple shards. By using the PostgreSQL parser, PgDog extracts sharding keys and determines the best routing strategy for each query.

For cross-shard queries, PgDog assembles and transforms results in memory, sending all rows to the client as if they are coming from a single database.

Example

Configuring multiple hosts for the same database with different shard numbers (shard setting) enables sharding:

[[databases]]
name = "prod"
host = "10.0.0.1"
shard = 0

[[databases]]
name = "prod"
host = "10.0.0.2"
shard = 1

Note: read below for how to configure query routing. At least one sharded table is required for sharding to work as expected.

Sharding functions

📘 Sharding functions

PgDog has two main sharding algorithms:

1. PostgreSQL partition functions (HASH, LIST, RANGE)
2. Using schemas

Partition-based sharding

Partition-based sharding functions are taken directly from Postgres source code. This choice intentionally allows to shard data both with PgDog and with Postgres foreign tables and postgres_fdw.

Examples

The PARTITION BY HASH algorithm is used by default when configuring sharded tables:

[[sharded_tables]]
database = "prod"
column = "user_id"

List-based sharding (same as PARTITION BY LIST in Postgres) can be configured as follows:

# Sharded table definition still required.
[[sharded_tables]]
database = "prod"
column = "user_id"

# Value-specific shard mappings.
[[sharded_mapping]]
database = "prod"
column = "user_id"
values = [1, 2, 3, 4]
shard = 0

[[sharded_mapping]]
database = "prod"
column = "user_id"
values = [5, 6, 7, 8]
shard = 1

For range-based sharding, replace the values setting with a range, for example:

start = 0 # include
end = 5 # exclusive

Schema-based sharding

📘 Schema-based sharding

Schema-based sharding works on the basis of PostgreSQL schemas. Tables under the same schema are placed on the same shard and all queries that refer to those tables are routed to that shard automatically.

Example

Configuring sharded schemas uses a different configuration from sharded tables:

[[sharded_schemas]]
database = "prod"
name = "customer_a"
shard = 0

[[sharded_schemas]]
database = "prod"
name = "customer_b"
shard = 1

Queries that refer tables in schema customer_a will be sent to shard 0. For example, a query that refers to a table by its fully-qualified name will be sent to one shard only:

INSERT INTO customer_a.orders (id, user_id, amount)
VALUES ($1, $2, $3);

Alternatively, the schema name can be specified in the search_path session variable:

SET search_path TO public, customer_a;
-- All subsequent queries will be sent to shard 0.
SELECT * FROM orders LIMIT 1;

You can also set the search_path for the duration of a single transaction, using SET LOCAL, ensuring only that transaction is sent to the desired shard:

-- The entire transaction will be sent to shard 1.
BEGIN;
SET LOCAL search_path TO public, customer_b;
SELECT * FROM orders LIMIT 1;
COMMIT;

Direct-to-shard queries

📘 Direct-to-shard queries

Queries that contain a sharding key are sent to one database only. This is the best case scenario for sharded databases, since the load is uniformly distributed across the cluster.

Example:

-- user_id is the sharding key.
SELECT * FROM users WHERE user_id = $1;

Cross-shard queries

• 📘 Cross-shard queries
• 📘 SELECT
• 📘 INSERT
• 📘 UPDATE and DELETE
• 📘 DDL

Queries with multiple sharding keys or without one are sent to all databases and results are post-processed and assembled in memory. PgDog then sends the final result to the client.

Currently, support for certain SQL features in cross-shard queries is limited. However, the list of supported ones keeps growing:

Using `COPY`

📘 Copy

PgDog has a text, CSV & binary parser and can split rows sent via COPY command between all shards automatically. This allows clients to ingest data into sharded PostgreSQL without preprocessing

Example

COPY orders (id, user_id, amount) FROM STDIN CSV HEADER;

Columns must be specified in the COPY statement, so PgDog can infer the sharding key automatically, but are optional in the data file.

Consistency (two-phase commit)

📘 Two-phase commit

To make sure cross-shard writes are atomic, PgDog supports Postgres' two-phase transactions. When enabled, PgDog handles COMMIT statements sent by clients by executing the 2pc exchange on their behalf:

PREPARE TRANSACTION '__pgdog_unique_id';
COMMIT PREPARED '__pgdog_unique_id';

In case the client disconnects or Postgres crashes, PgDog will automatically rollback the transaction if it's in phase I and commit it if it's in phase II.

Unique identifiers

📘 Unique IDs

While applications can use UUID (v4 and now v7) to generate unique primary keys, PgDog supports creating unique BIGINT identifiers, without using a sequence:

SELECT pgdog.unique_id();

This uses a timestamp-based algorithm, can produce millions of unique numbers per second and doesn't require an expensive cross-shard index to guarantee uniqueness.

Shard key updates

PgDog supports changing the sharding key for a row online. Under the hood, it will execute 3 statements to make it happen:

1. SELECT to get the entire row from its original shard
2. INSERT to write the new, changed row to the new shard
3. DELETE to remove it from the old shard

This happens automatically, and the client can retrieve the new row as normal:

UPDATE orders SET user_id = 5 WHERE user_id = 1 RETURNING *;
-- This will return the new row

Note: Only one row can be updated at a time and if a query attempts to update multiple, PgDog will abort the transaction.

To enable shard key updates, add this to pgdog.toml:

[rewrite]
enabled = true
shard_key = "rewrite" # options: ignore (possible data loss), error (block shard key update)

Multi-tuple inserts

PgDog can handle multi-tuple INSERT queries by sending each tuple to the right shard, e.g.:

INSERT INTO payments
    (id, user_id, amount) -- user_id is the sharding key
VALUES
(pgdog.unique_id(), 1, 25.00), -- Tuples go to different shards
(pgdog.unique_id(), 5, 55.0); -- Each tuple gets a unique primary key because unique ID function is invoked twice

This happens automatically, if enabled:

[rewrite]
enabled = true
split_inserts = "rewrite" # other options: ignore, error

Re-sharding

• 📘 Re-sharding
• 📘 Schema sync
• 📘 Data sync

PgDog understands the PostgreSQL logical replication protocol and can orchestrate data splits between databases, in the background and without downtime. This allows to shard existing databases and add more shards to existing clusters in production, without impacting database operations.

The re-sharding process is done in 5 steps:

1. Create new empty cluster with the desired number of shards
2. Configure it in pgdog.toml and run schema-sync command to copy table schemas to the new databases
3. Run data-sync command to copy and re-shard table data with logical replication (tables are copied in parallel)
4. While keeping previous command running (it streams row updates in real-time), run schema-sync --data-sync-complete to create secondary indexes on the new databases (much faster to do this after data is copied)
5. Cutover traffic to new cluster with MAINTENANCE ON, RELOAD, MAINTENANCE OFF command sequence

Cutover can be done atomically with multiple PgDog containers because RELOAD doesn't resume traffic, MAINTENANCE OFF does, so the config is the same in all containers before queries are resumed. No complex synchronization tooling like etcd or Zookeeper is required.

Monitoring Copy Section

📘 Metrics

PgDog exposes both the standard PgBouncer-style admin database, an OpenMetrics endpoint and can push metrics to an OTEL endpoint. The admin database isn't 100% compatible,
so we recommend you use either OpenMetrics or OTEL ingestion for monitoring.

We include two examples:

• Datadog configuration and dashboard
• Graphana + Prometheus configuration and dashboard

Running PgDog locally Copy Chapter

Install the latest version of the Rust compiler from rust-lang.org.
Clone this repository and build the project in release mode:

cargo build --release

It's important to use the release profile if you're deploying to production or want to run
performance benchmarks.

Try sharding

Sharded database clusters are set in the config. For example, to set up a 2 shard cluster, you can:

pgdog.toml

[[databases]]
name = "pgdog_sharded"
host = "127.0.0.1"
database_name = "shard_0"
shard = 0

[[databases]]
name = "pgdog_sharded"
host = "127.0.0.1"
database_name = "shard_1"
shard = 1

[[sharded_tables]]
database = "pgdog_sharded"
column = "user_id"

Don't forget to configure a user:

users.toml

[[users]]
database = "pgdog_sharded"
name = "pgdog"
password = "pgdog"

And finally, to make it work locally, create the required databases:

CREATE DATABASE shard_0;
CREATE DATABASE shard_1;

GRANT ALL ON DATABASE shard_0 TO pgdog;
GRANT ALL ON DATABASE shard_1 TO pgdog;

Start PgDog Copy Section

Running PgDog can be done with Cargo:

cargo run --release

Command-line options

PgDog supports several command-line options:

• -c, --config <CONFIG>: Path to the configuration file (default: "pgdog.toml")
• -u, --users : Path to the users.toml file (default: "users.toml")
• -d, --database_url <DATABASE_URL>: Connection URL(s). Can be specified multiple times to add multiple database connections. When provided, these URLs override database configurations from the config file.

Example using database URLs directly:

cargo run --release -- -d postgres://user:pass@localhost:5432/db1 -d postgres://user:pass@localhost:5433/db2

You can connect to PgDog with psql or any other PostgreSQL client:

psql postgres://pgdog:[email protected]:6432/pgdog

🚦 Status 🚦 Copy Chapter

PgDog is used in production and at scale. Most features are stable, while some are experimental. Check documentation for more details. New sharding features are added almost weekly.

Performance Copy Chapter

📘 Architecture & benchmarks

PgDog is heavily optimized for performance. We use Rust, Tokio, bytes crate to avoid unnecessary memory allocations, and profile for performance regressions on a regular basis.

License Copy Chapter

PgDog is free and open source software, licensed under the AGPL v3. While often misunderstood, this license is very permissive
and allows the following without any additional requirements from you or your organization:

• Internal use
• Private modifications for internal use without sharing any source code

You can freely use PgDog to power your PostgreSQL databases without having to
share any source code, including proprietary work product or any PgDog modifications you make.

AGPL was written specifically for organizations that offer PgDog as a public service (e.g. database cloud providers) and require
those organizations to share any modifications they make to PgDog, including new features and bug fixes.

Contributions Copy Chapter

Please read our Contribution Guidelines.

File: examples/pgbouncer_benchmark/README.md Copy Chapter

Setup Copy Section

1. Make sure you don't have anything running on ports 6433 and 6432.
2. (Optional) Delete all previous images to make sure to pull latest from Hub: docker rm -vf $(docker ps -aq) and docker rmi -f $(docker images -aq)
3. docker-compose up

Run the benchmark Copy Section

export PGHOST=127.0.0.1
export PGPASSWORD=postgres
export PGUSER=postgres
export PGDATABASE=postgres

PgBouncer Copy Section

export PGPORT=6432
pgbench -i
pgbench -c 10 -t 100000

PgDog Copy Section

export PGPORT=6433
pgbench -i
pgbench -c 10 -t 100000

File: examples/datadog/README.md Copy Chapter

Datadog integration

PgDog can export metrics to Datadog via two mechanisms:

1. OpenMetrics collector
2. OTLP (OTEL) ingestion endpoint

Both export the same metrics, at a configurable interval of your choice.

OpenMetrics Copy Chapter

PgDog exports a lot of metrics via an OpenMetrics endpoint. You can enable the endpoint
by specifying the port number in pgdog.toml:

# pgdog.toml
[general]
openmetrics_port = 9090

The endpoint will run on http://0.0.0.0:9090. A sample config is included in openmetrics.d/conf.yaml.

OLTP/OTEL Copy Chapter

PgDog can push OTEL metrics to a configurable endpoint. Datadog supports ingesting metrics this way and we support it natively:

# pgdog.toml
[otel]
datadog_api_key = "xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx"
endpoint = "https://otlp.us5.datadoghq.com/v1/metrics"

By default, PgDog will push metrics to the endpoint every 10 seconds.

Dashboard Copy Chapter

We have a pre-build Datadog dashboard you can import directly into your stack. See dashboard.json.

File: pgdog-plugin/README.md Copy Chapter

PgDog plugins

PgDog plugin system is based around shared libraries loaded at runtime. The plugins currently can only be
written in Rust. This is because PgDog passes Rust-specific data types to plugin functions, and those cannot
be easily made C ABI-compatible.

This crate implements the bridge between PgDog and plugins, making sure data types can be safely passed through the FFI.

Automatic checks include:

• Rust compiler version check
• pgdog-plugin crate version check

This crate should be linked at compile time against your plugins.

Writing plugins Copy Chapter

See crate documentation and pgdog tutorial for guides and examples. Example plugins are available in GitHub as well.

License Copy Chapter

This library is distributed under the MIT license. See LICENSE for details.

File: plugins/README.md Copy Chapter

PgDog plugins

This directory contains plugins that ship with PgDog and are built by original author(s) or by the community. You can use these as-is or modify them to your needs.

Plugins Copy Chapter

`pgdog-example-plugin` Copy Section

Example plugin that can be used as reference by the community. It currently records
when a write was made to a table and, for the next 5 seconds after the write, redirects
all SELECT queries that touch table to the primary.

It's a simple workaround for Postgres replica lag, if you're using batch writes.

2. Official Technical Reference & Guides (pgdogdev/docs) Copy Chapter

File: README.md Copy Chapter

PgDog Documentation

This repository contains the documentation hosted on https://docs.pgdog.dev.

Contributions Copy Chapter

Contributions are welcome. Please open a pull request / issue with requested changes. Once the PR is merged,
you should see changes in production within a few minutes.

File: docs/index.md Copy Chapter

icon: material/home Copy Chapter

Introduction to PgDog

PgDog is a connection pooler, load balancer and database sharder for PostgreSQL. Written in Rust, PgDog is fast, reliable and scales Postgres databases without requiring changes to your application.

Getting started Copy Chapter

PgDog is an open source project. You can download its code from our repository in GitHub. If you're deploying PgDog to your cloud (or on prem), you can use our Docker image (and Helm chart), the compiled binaries (incl. a .deb package), or build it from source.

Docker image Copy Section

Every commit in the main branch, and weekly tagged releases, have corresponding images in our Docker repository, for example:

docker run ghcr.io/pgdogdev/pgdog:v0.1.48

You can read more about how to deploy PgDog here.

Why PgDog Copy Chapter

PostgreSQL is a process-based, single-primary database. As such, it has hard limits on how many clients can connect, how many queries a single server can execute, and how much data it can write at any given time.

PgDog helps you work around these limits. It's a single binary, deployed between your application and the database, that speaks both the protocol applications use to talk to Postgres and the replication protocol Postgres uses internally. This lets PgDog do its job transparently, without changes to Postgres or the applications that query it.

Connection pooler Copy Chapter

Like PgBouncer or RDS Proxy, PgDog is a connection pooler: it multiplexes thousands of application connections over just a handful of Postgres server connections. This effectively removes the limit on how many clients a PostgreSQL database can serve at once.

Unlike those proxies, PgDog handles features that usually force a pooler to pin or reset connections. It supports SET statements, LISTEN/NOTIFY, and advisory locks without breaking connection state, so your application keeps working as if it were talking to Postgres directly.

PgDog is also multithreaded, so a single instance can serve many more clients while still relying on the same small number of Postgres connections.

You can read more about how the connection pooler works here.

Load balancer Copy Chapter

If your database has read replicas, PgDog can distribute read queries across them using one of several built-in load balancing algorithms. This lets you scale reads simply by adding replicas, with no application changes and no extra infrastructure like HAProxy or Patroni.

You can read more about how PgDog load balances queries here.

Sharding PostgreSQL Copy Chapter

Unlike NoSQL databases, PostgreSQL can serve INSERT, UPDATE, and DELETE queries from only one server. Once that server's capacity is exceeded, applications have to find new and creative ways to reduce their load on the database, such as batching requests or deferring workloads to run overnight.

At the same time, database operators face rising operating costs from vacuums that fall behind schedule, table bloat and downtime. Incidents become frequent, and engineers end up more focused on keeping the database from breaking than on building new features.

The solution to an overextended database is sharding: splitting all tables and indices equally between multiple machines. For example, if your primary database is 750 GB, splitting it into 3 shards will produce 3 databases of 250 GB each. As databases get smaller, vacuums start to catch up, indices fit into memory again, and queries run faster with reliable performance.

As shards accumulate more data and grow, they can be split again, scaling PostgreSQL horizontally. Sharded databases can grow into petabytes (thousands of TB) while serving both OLTP and OLAP workloads.

PgDog operates on the application layer of the stack: it speaks PostgreSQL and understands not only the queries sent by applications but also the logical replication protocol used by the server. This allows it to automatically route queries while moving data between machines to create more capacity.

This documentation provides a detailed overview of all PgDog features, along with reference material for production operations.

Read more Copy Chapter

{{ next_steps_links([
("Installation", "/installation/", "Deploy PgDog with Helm on Kubernetes, run it on AWS ECS with Terraform, with Docker, with pre-built binaries, or by building from source."),
("Connection pooler", "/features/connection-pooler/", "Multiplex thousands of application connections over a small number of PostgreSQL server connections."),
("Load balancer", "/features/load-balancer/", "Distribute read queries across replicas and send write queries to the primary database."),
("Sharding", "/features/sharding/", "Scale PostgreSQL horizontally with query routing, data migration, and schema management."),
]) }}