Interactive Guide To Rate Limiting

Sagyam Thapa — Wed, 04 Jun 2025 23:03:47 +0000

Introduction

Rate limiting is a must have strategy in every back-end app. It prevent one user from overusing a resource and degrading the quality of service for other users. Here are some benefits of rate limiting

It presents resource starvation
Reduces server hosting cost
Provides basic protection against DDoS

I have made four interactive app that let’s you play around with common rate limiting algorithms.

Token bucket

Working:

A bucket holds fixed number tokens
Tokens are added to bucket at fixed rate
When a request comes in:
If a token is available, it’s removed from the bucket and the request is allowed.
If no tokens are available, the request is rejected or delayed.
Allows for occasional short burst if tokens are available

I have created an app that let’s you play with leaky bucket algorithm.

Leaky bucket

Working:

Think of it as a bucket leaking at a fixed rate
Incoming requests are added to the bucket
Requests are processed (or “leak”) at a constant rate
If the bucket is full when a new request arrives, the request is dropped
Smooths out bursts; outputs requests at a steady rate

I have created an app that let’s you play with leaky bucket algorithm.

Fixed window counter

Working:

Time is divided into fixed-size windows (e.g., 1 minute) A counter tracks the number of requests per client/IP in the current window
If the count exceeds the limit, further requests are rejected until the next window
Simple and efficient, but allows burst traffic spike at end/start

I have created an app that let’s you play with fixed bucket algorithm.

Sliding window counter

Working:

Keeps a timestamped log of each request
When a request comes in, logs are checked to count how many requests were made in the last X seconds
If under the limit, the request is allowed and logged; otherwise, it’s rejected

I have created an app that let’s you play with fixed bucket algorithm.

Scaling PostgreSQL with Kubernetes

Sagyam Thapa — Tue, 27 May 2025 17:24:51 +0000

A case for vertical scaling

If you have read any article or a book on system design then you probably know what vertical and horizontal scaling is and benefits of horizontal scaling. Before I explain how to setup proper horizontal scaling with Postgres let me make a case when you should not try this.

Simplicity: Single node database means you can run your database out of the box. Although I recommend you run PGTune for a quick preset or visit postconf a full breakdown
Easier backup and recovery: No need to think about state across replicas when creating backups or applying a backup.
No network overhead especially with write heavy operations.
A temporary fix: If need a fix right now, this will provide an instant relief.

Prerequisite

Make sure you have following tools installed.

Following the guide requires you have basic understanding of Kubernetes, CRD, Helm. Nothing deep a quick AI summary will suffice.

Replication

Replication means keeping multiple copies of data on multiple machines connected via network. Here is why you might want to do that:

It keeps you data close to your users.
It acts as a hot backup of a follower goes down.
It helps with scaling if most of your workload is read operation (which is the case for most OLTP)

Here pg-pool acts as load balancer, it distributes read request evenly among followers and mutation request to the leader. Notice that Leader periodically syncs it WAL with it’s followers.

Setup StackGres and enable load balancer

minikube addons enable metallb
minikube tunnel

helm install stackgres-operator stackgres-charts/stackgres-operator\               --namespace stackgres-operator \
--create-namespace

Define CRD for replicated cluster

# replication.yaml

apiVersion: stackgres.io/v1
kind: SGCluster
metadata:
  name: cluster

spec:
  instances: 3 # 1 primary + 2 replicas

  postgres:
    version: "15"

  pods:
    persistentVolume:
      size: "1Gi"

  profile: development

  postgresServices:
    primary:
      type: LoadBalancer
    replicas:
      type: LoadBalancer

Apply the CRD

kubectl apply -f ./replication.yaml
kubectl get pods -w

Get credentials

PG_PASSWORD=$(kubectl -n default get secret cluster --template '{{ printf "%s" (index .data "superuser-password" | base64decode) }}')
echo "The superuser password is: $PG_PASSWORD"

See who is who

kubectl exec -it cluster-0  -c patroni -- patronictl list

Kill the primary

kubectl exec -it cluster-0  -c patroni -- patronictl list

See who is in charge now

Patroni should have elected a new leader by now.

kubectl exec -it cluster-1 -c patroni -- patronictl list

Tell something only to the primary

PRIMARY=$(kubectl exec -it cluster-1 -c patroni -- patronictl list | grep Leader | awk '{print $2}')
kubectl exec -it $PRIMARY -c patroni -- psql -U postgres -c "CREATE TABLE replication_test_table (id SERIAL PRIMARY KEY, data TEXT);"
kubectl exec -it $PRIMARY -c patroni -- psql -U postgres -c "INSERT INTO replication_test_table (data) VALUES ('Spread the word about our lord savior PostgreSQL!');"

Primary tell his followers

kubectl exec -it cluster-0 -c patroni -- psql -U postgres -c "SELECT * FROM replication_test_table;"
kubectl exec -it cluster-1 -c patroni -- psql -U postgres -c "SELECT * FROM replication_test_table;"
kubectl exec -it cluster-2 -c patroni -- psql -U postgres -c "SELECT * FROM replication_test_table;"

As you can see how quickly the word has spread. This is possible because StackGres uses Patroni under the hood to coordinate all the replication.

Partitioning

Partitioning splits the data (table in our case) into smaller, more manageable parts. This is done within a single database instance. Postgres supports this out of the box. It is defined in data definition layer and having multiple replicas for makes a partition highly available. It works best for time-series data, logs, or region-based segmentation.

Types of Partitioning

Range Partitioning – Data is partitioned based on value ranges (e.g., date ranges).
List Partitioning – Partitioning based on a list of values (e.g., regions or categories).
Hash Partitioning – Data is distributed using a hash function (e.g., MOD(user_id, 4)).

Following code create a table orders and derives three tables from it using range, list and hash based partition in a hierarchical way. Order table is split by year, year is further split into regions and region is finally split by hash.

Notice that only hash based partition grantees that all partition are of same size.

Setup StackGres and enable load balancer

helm install stackgres-operator stackgres-charts/stackgres-operator \
    --namespace stackgres-operator \
    --create-namespace

minikube addons enable metallb
minikube tunnel

Get credentials

PG_PASSWORD=$(kubectl -n default get secret cluster --template '{{ printf "%s" (index .data "superuser-password" | base64decode) }}')
echo "The superuser password is: $PG_PASSWORD"

Your database should now be available at postgresql://postgres:<password>:localhost:5432

Now open an SQL Editor like pgAdmin, and run the following.

-- Parent table
CREATE TABLE orders (
    order_id    INT,
    customer_id INT,
    order_date  DATE,
    region      TEXT,
    amount      INT,
    PRIMARY KEY (order_id, order_date, region, customer_id)
) PARTITION BY RANGE (order_date);


-- Range: Year 2024
CREATE TABLE orders_2024 PARTITION OF orders
    FOR VALUES FROM ('2024-01-01') TO ('2025-01-01')
    PARTITION BY LIST (region);

-- Range: Year 2025
CREATE TABLE orders_2025 PARTITION OF orders
    FOR VALUES FROM ('2025-01-01') TO ('2026-01-01')
    PARTITION BY LIST (region);

-- 2024 - US region
CREATE TABLE orders_2024_us PARTITION OF orders_2024
    FOR VALUES IN ('US')
    PARTITION BY HASH (customer_id);

-- 2024 - EU region
CREATE TABLE orders_2024_eu PARTITION OF orders_2024
    FOR VALUES IN ('EU')
    PARTITION BY HASH (customer_id);

-- 2024 - US - Hash partitions
CREATE TABLE orders_2024_us_0 PARTITION OF orders_2024_us FOR VALUES WITH (MODULUS 2, REMAINDER 0);
CREATE TABLE orders_2024_us_1 PARTITION OF orders_2024_us FOR VALUES WITH (MODULUS 2, REMAINDER 1);

-- 2024 - EU - Hash partitions
CREATE TABLE orders_2024_eu_0 PARTITION OF orders_2024_eu FOR VALUES WITH (MODULUS 2, REMAINDER 0);
CREATE TABLE orders_2024_eu_1 PARTITION OF orders_2024_eu FOR VALUES WITH (MODULUS 2, REMAINDER 1);

Bulk insert synthetic data

-- Generate 1000 random orders
INSERT INTO orders (order_id, customer_id, order_date, region, amount)
SELECT 
    -- Generate order IDs between 1000 and 9999
    1000 + floor(random() * 9000)::int AS order_id,
    -- Generate customer IDs between 1000 and 9999
    1000 + floor(random() * 9000)::int AS customer_id,    
    -- Generate dates in 2024 (to fit the 2024 partition)
    DATE '2024-01-01' + (floor(random() * 366)::int * INTERVAL '1 day') AS order_date,    
    -- Randomly select region
    (ARRAY['US', 'EU'])[1 + floor(random() * 2)::int] AS region,    
    -- Generate random amounts between 10 and 1000
    10 + floor(random() * 990)::int AS amount
FROM 
    generate_series(1, 1000) AS i;            -- 1k rows

SELECT * FROM orders
WHERE order_date = '2024-06-10'
  AND region = 'US'

Querying the orders does not require you to know the partition

Sharding with replication

Sharding splits a large database into small pieces called shards. Each shard is then split among multiple machines so that our database can continue to function even if we lose a few machines. Routing of queries to the proper is done by a coordinator, and just like with the replication example we will have pg-pool doing load balancing within a shard.

Types of sharding

Row based: Think of it like splitting a very thick book into many volumes (shards) based on and creating a new volumes just to keep track of table of content (coordinator). Think of a table where the schema of the table is simple but amount of rows and amount write operation has gone crazy. With this method both read/write operation for every shard can scale as needed.
Schema based: Just like last time we are still splitting the book but this time we are taking a few chapters that are related and turning it into a book about a sub topic. Think of how a very thick physics textbook can be split into Optics, Thermodynamics, Quantum Mechanics. Think of a table to large number of columns, but you don’t need the all the columns every time a query is made. So you split the table into shards such that related columns get placed together.

Notice the resiliency of this architecture, not only we have multiple replicas for shards but also for the coordinator. As long as we have a minimum of 3 machines to run our sharded cluster, failure of single machine will not bring the down our database.

Setup StackGres and enable load balancer

helm install stackgres-operator stackgres-charts/stackgres-operator \
    --namespace stackgres-operator \
    --create-namespace

minikube addons enable metallb
minikube tunnel

Define CRD for Sharded Cluster

# shard.yaml
apiVersion: stackgres.io/v1alpha1
kind: SGShardedCluster
metadata:
  name: cluster
spec:
  type: citus
  database: mydatabase
  postgres:
    version: 'latest'
  coordinator:
    instances: 2 # Number of coordinator instances
    pods:
      persistentVolume:
        size: '1Gi'
  shards:
    clusters: 3 # Number of shards
    instancesPerCluster: 3 # 1 primary and 2 replicas
    pods:
      persistentVolume:
        size: '1Gi'
  postgresServices:
    coordinator:
      primary:
        type: LoadBalancer

  profile: development

Apply Citus CRD

kubectl apply -f ./shard.yaml

Get credentials

PG_PASSWORD=$(kubectl -n default get secret cluster --template '{{ printf "%s" (index .data "superuser-password" | base64decode) }}')
echo "The superuser password is: $PG_PASSWORD"

Your database should now be available at postgresql://postgres:<password>:localhost:5432

Now open a app like SQL Editor like pgAdmin, and run the following.

Create some distributed table

CREATE TABLE users (
    id BIGINT PRIMARY KEY,
    name TEXT
);
SELECT create_distributed_table('users', 'id');

CREATE TABLE orders (
    id BIGINT,
    user_id BIGINT,
    product_id BIGINT,
    amount INTEGER,
    PRIMARY KEY (user_id, id)
);
SELECT create_distributed_table('orders', 'user_id');

CREATE TABLE products (
    id BIGINT PRIMARY KEY,
    name TEXT,
    price NUMERIC
);
SELECT create_reference_table('products');

Insert some data

INSERT INTO users (id, name) VALUES
(1, 'Alice'),
(2, 'Bob'),
(3, 'Charlie');

INSERT INTO orders (id, user_id, product_id, amount) VALUES
(1, 1, 1, 2),
(2, 1, 2, 3),
(3, 2, 1, 1),
(4, 3, 3, 5);
INSERT INTO products (id, name, price) VALUES
(1, 'Product A', 10.00),
(2, 'Product B', 20.00),
(3, 'Product C', 30.00);

See how shards are spread

SELECT * FROM citus_shards
WHERE table_name = 'orders'::regclass;

Find which node host which shard

SELECT
  s.shardid,
  n.nodename,
  n.nodeport
FROM pg_dist_shard s
JOIN pg_dist_shard_placement p ON s.shardid = p.shardid
JOIN pg_dist_node n ON p.nodename = n.nodename
WHERE s.logicalrelid = 'orders'::regclass;

Find which has a specific row

SELECT get_shard_id_for_distribution_column('orders', 1);

Join distributed-distributed (co-located)

SELECT
    o.id AS order_id,
    u.name AS customer,
    o.amount
FROM orders o
JOIN users u ON o.user_id = u.id;

This is efficient because orders and users are sharded using the same key (user_id and id)

Join distributed-reference

SELECT
    o.id AS order_id,
    u.name AS customer,
    p.name AS product,
    o.amount
FROM orders o
JOIN users u ON o.user_id = u.id
JOIN products p ON o.product_id = p.id;

This works well because products is replicated across all nodes.

DEV Community: Sagyam Thapa

Interactive Guide To Rate Limiting

Introduction

Token bucket

Working:

Leaky bucket

Working:

Fixed window counter

Working:

Sliding window counter

Working:

Scaling PostgreSQL with Kubernetes

A case for vertical scaling

Prerequisite

Replication

Setup StackGres and enable load balancer

Define CRD for replicated cluster

Apply the CRD

Get credentials

See who is who

Kill the primary

See who is in charge now

Tell something only to the primary

Primary tell his followers

Partitioning

Types of Partitioning

Setup StackGres and enable load balancer

Get credentials

Bulk insert synthetic data

Sharding with replication

Types of sharding

Setup StackGres and enable load balancer

Define CRD for Sharded Cluster

Apply Citus CRD

Get credentials

Create some distributed table

Insert some data

See how shards are spread

Find which node host which shard

Find which has a specific row

Join distributed-distributed (co-located)

Join distributed-reference

References