PostgreSQL to ClickHouse: Custom Connector Guide

Need real-time analytics but stuck with PostgreSQL's limitations? This guide shows you how to build a custom connector to sync PostgreSQL data with ClickHouse for faster, more efficient analytics. PostgreSQL excels at handling transactions but struggles with large-scale analytics, while ClickHouse is designed for high-speed querying and real-time data ingestion. Here's how you can bridge the gap:

• Why ClickHouse? Its columnar storage and parallel processing make it ideal for analytics.

• PostgreSQL's Challenges: Slower performance for analytics due to row-based storage and index maintenance.

• Custom Connector Benefits: Tailored for real-time needs, flexible for business logic, and cost-efficient compared to off-the-shelf tools.

• Key Tools: Python, Apache Kafka, and Debezium for change data capture (CDC).

• Setup Steps: Configure PostgreSQL for logical replication, optimize ClickHouse schemas, and handle data ingestion with Python.

Quick Tip: If managing ClickHouse feels overwhelming, consider a managed service like Tinybird to simplify scaling, monitoring, and API creation. Keep reading for a detailed walkthrough on building your custom connector and scaling it efficiently.

Leverage Replacing MergeTree for Real-Time Postgres to ClickHouse Sync Using Kafka&Debezium HandsON

Environment Setup

To ensure smooth real-time synchronization, it's crucial to configure your development environment properly. This setup is essential for building a reliable PostgreSQL to ClickHouse connector. You'll need to configure both database systems and install the right tools to handle real-time data synchronization effectively.

Development Requirements

Setting up the development environment involves several key components to manage the data pipeline. Here’s what you’ll need:

• Python 3.8+: Install libraries for database connectivity and data processing. Specifically, you’ll need psycopg2 for PostgreSQL connections and clickhouse-driver for interacting with ClickHouse®.

• Apache Kafka 2.8+: This serves as the messaging backbone for change data capture (CDC).

• Debezium 1.9+: Handles the CDC implementation.

To run Kafka efficiently, make sure your development machine has at least 4 GB of RAM. Additionally, configure your PostgreSQL user with REPLICATION privileges and SELECT access on the tables you plan to sync. For ClickHouse, create a user with INSERT and CREATE TABLE permissions. Avoid using superuser accounts in production to maintain security.

To manage dependencies, use a Python virtual environment. Install the following packages via pip:

• psycopg2-binary

• clickhouse-driver

• kafka-python

• python-dotenv (for configuration management)

These tools form the backbone of your custom connector. Once dependencies are in place and permissions are configured, you can proceed to set up CDC in PostgreSQL.

PostgreSQL Change Data Capture Setup

To enable logical replication in PostgreSQL, modify the postgresql.conf file and restart the server. Here’s what to do:

1. Set wal_level = logical to allow PostgreSQL to capture detailed data modification logs.

2. Adjust max_replication_slots to at least 4 to support multiple CDC connections.

3. Set max_wal_senders to 4 to ensure sufficient processes can send WAL data to subscribers.

Next, create a publication to specify which tables will be monitored for changes. Use the following SQL commands:

• To capture changes from all tables:

  Copy

  CREATE PUBLICATION my_publication FOR ALL TABLES;
  

• To monitor specific tables:

  Copy

  CREATE PUBLICATION my_publication FOR TABLE users, orders, products;
  

Publications act like filters, determining which data gets replicated. After that, set up a replication slot using:

SELECT pg_create_logical_replication_slot('my_slot', 'pgoutput');

Replication slots ensure that WAL segments containing your changes are retained until they’re processed by the connector, preventing data loss even during temporary downtime.

ClickHouse Data Ingestion Setup

Once PostgreSQL CDC is configured, prepare ClickHouse for efficient data ingestion by optimizing table schemas and partitioning. Choose the right engine type based on your use case:

• MergeTree: Ideal for most analytical workloads.

• ReplacingMergeTree: Useful for handling updates by keeping only the latest version of records with the same primary key.

When designing schemas, align them with PostgreSQL while optimizing for columnar storage. For example:

• Use DateTime64 for timestamp fields to maintain microsecond precision.

• Opt for LowCardinality for string fields with limited unique values to save storage and improve query performance.

Partitioning and ordering can also enhance performance. Use:

PARTITION BY toYYYYMM(created_at)
ORDER BY (user_id, created_at)

This setup improves query execution for time-based analytics and creates an implicit index for faster filtering.

To further optimize ingestion performance, adjust these ClickHouse settings:

• Increase max_insert_block_size to 1,048,576 rows.

• Enable async_insert and set async_insert_max_data_size to 100MB for better throughput and automatic batching of smaller inserts.

If real-time aggregations are needed, create materialized views. For instance, a materialized view can maintain running totals or hourly summaries as data arrives. This eliminates the need to recalculate aggregations during queries, significantly speeding up dashboard response times.

Building the Custom Connector

With your environment set up, it’s time to dive into the core logic of the connector. This section explains how to capture, transform, and deliver real-time changes from PostgreSQL to ClickHouse®.

Change Data Capture Basics

Change Data Capture (CDC) works by tapping into PostgreSQL's Write-Ahead Log (WAL) to track INSERT, UPDATE, and DELETE operations as they happen. Debezium acts as the bridge, converting these WAL entries into structured messages that your connector can process.

Here’s how it works: Debezium reads the WAL and produces structured messages containing before and after states for each change. These messages include the operation type (e.g., INSERT, UPDATE) and a timestamp. This dual-state structure is key for handling updates in ClickHouse efficiently.

To configure Debezium, you’ll need a JSON file with details about your database, publication, and the Kafka topic where changes will be sent. Here’s an example configuration:

{
  "name": "postgres-connector",
  "config": {
    "connector.class": "io.debezium.connector.postgresql.PostgresConnector",
    "database.hostname": "localhost",
    "database.port": "5432",
    "database.user": "replicator",
    "database.password": "your_password",
    "database.dbname": "your_database",
    "database.server.name": "postgres-server",
    "publication.name": "my_publication",
    "slot.name": "my_slot"
  }
}

Once set up, Debezium starts publishing change events to Kafka topics. Each table gets its own topic, following the naming pattern {server.name}.{schema}.{table}. Importantly, message ordering is preserved within each partition, ensuring sequential processing of changes for the same record. With these structured events in place, the next step is transforming and batching them for ClickHouse.

Data Ingestion Implementation

To ingest data into ClickHouse, you’ll need to transform the CDC messages into a format ClickHouse can handle. This involves managing the differences between PostgreSQL’s row-based structure and ClickHouse’s columnar design.

Start by creating a Python consumer to read messages from Kafka and process them. Here’s a basic example:

from kafka import KafkaConsumer
from clickhouse_driver import Client
import json

def transform_message(message):
    payload = message['payload']
    operation = payload['op']

    if operation == 'c':  # INSERT
        return payload['after']
    elif operation == 'u':  # UPDATE
        return payload['after']
    elif operation == 'd':  # DELETE
        return payload['before']
    return None

def batch_insert_to_clickhouse(client, table, records):
    if not records:
        return

    columns = list(records[0].keys())
    values = [list(record.values()) for record in records]

    client.execute(
        f"INSERT INTO {table} ({','.join(columns)}) VALUES",
        values
    )

Batch processing is crucial for better performance. Instead of processing one record at a time, group records into batches (e.g., 1,000 rows or a set time interval) before inserting them. This reduces network overhead and takes advantage of ClickHouse’s MergeTree engine for efficient data organization.

For UPDATE operations, you’ll need to insert the new version of the record, as ClickHouse is immutable. If you’re using a ReplacingMergeTree table, include a version column and increment it with each update. For DELETE operations, you can either insert "tombstone" records with a deletion flag or use a separate table to track deletions.

Error Handling and Data Consistency

A robust connector must handle errors gracefully to maintain data consistency. This includes dealing with network issues, schema changes, or system downtime.

Introduce retry logic with exponential backoff to handle temporary failures:

import time
import random

def retry_with_backoff(func, max_retries=5):
    for attempt in range(max_retries):
        try:
            return func()
        except Exception as e:
            if attempt == max_retries - 1:
                raise e

            delay = min(2 ** attempt + random.uniform(0, 1), 60)
            time.sleep(delay)

To avoid duplicate processing, track Kafka offsets in ClickHouse. Create a dedicated table to store the last processed offset for each Kafka partition:

CREATE TABLE connector_offsets (
    topic String,
    partition UInt32,
    offset UInt64,
    updated_at DateTime DEFAULT now()
) ENGINE = ReplacingMergeTree(updated_at)
ORDER BY (topic, partition);

Data validation is another critical step. Use checksums or row counts to ensure the number of records processed matches expectations. For important tables, you can compare running totals in PostgreSQL and ClickHouse to spot discrepancies.

Since PostgreSQL transactions don’t directly map to ClickHouse, you can group messages by transaction ID and timestamp to process them as a unit. For messages that fail repeatedly, consider routing them to a dead letter queue for manual review instead of blocking the pipeline.

Finally, set up monitoring and alerting to keep tabs on the connector’s performance. Track metrics like processing lag, error rates, and throughput. Alerts can notify you if lag exceeds acceptable levels or if error rates spike unexpectedly.

sbb-itb-65dad68

Scaling and Performance Optimization

Once your connector has a solid foundation for data ingestion and error handling, the next step is to focus on scaling. As data volumes grow, your PostgreSQL to ClickHouse® connector must handle the load efficiently without compromising reliability. Scaling this type of connector revolves around three primary areas: parallel processing, performance monitoring, and fault tolerance.

Partitioning and Parallel Processing

Proper Kafka partitioning is crucial to avoid bottlenecks. When configuring your Debezium connector, set up multiple Kafka partitions per topic based on your data volume. The number of partitions should align with the expected message size and complexity. For even distribution, configure partition keys wisely - using a primary key or a hash of multiple columns often works well.

# Example Kafka producer configuration with custom partitioning
producer_config = {
    'bootstrap.servers': 'localhost:9092',
    'partitioner': 'murmur2_random',
    'batch.size': 65536,  # 64KB batches
    'linger.ms': 100      # Wait up to 100ms to fill the batch
}

Scaling horizontally is possible by parallelizing Kafka consumers. Deploying multiple consumer instances - each responsible for specific partitions - can significantly increase throughput.

import threading
from concurrent.futures import ThreadPoolExecutor

class ParallelConnector:
    def __init__(self, num_workers=4):
        self.num_workers = num_workers
        self.executor = ThreadPoolExecutor(max_workers=num_workers)

    def process_partition(self, partition_id):
        consumer = KafkaConsumer(
            group_id=f'clickhouse-connector-{partition_id}',
            enable_auto_commit=False
        )
        # Process messages for this partition

    def start(self):
        for i in range(self.num_workers):
            self.executor.submit(self.process_partition, i)

Optimizing the table design in ClickHouse® is equally important. Using the correct MergeTree engine variant and implementing a Buffer table can streamline data ingestion by batching records in memory before writing them to the main table.

CREATE TABLE events_buffer AS events
ENGINE = Buffer(default, events, 16, 10, 100, 10000, 1000000, 10000000, 100000000);

(The parameters above are examples - adjust them based on your workload and performance needs.)

Monitoring and Performance Tuning

Real-time monitoring is essential for identifying and resolving issues early. Keep an eye on metrics like consumer lag, processing rates, and error frequencies. For instance, you can integrate Prometheus to track critical metrics.

from prometheus_client import Counter, Histogram, Gauge
import time

messages_processed = Counter('connector_messages_processed_total')
processing_duration = Histogram('connector_processing_duration_seconds')
consumer_lag = Gauge('connector_consumer_lag_messages')

def process_with_metrics(message):
    start_time = time.time()
    try:
        # Process message
        messages_processed.inc()
    finally:
        processing_duration.observe(time.time() - start_time)

ClickHouse®'s system tables are a powerful tool for analyzing query performance. You can identify slow queries and optimize them using a simple query:

SELECT 
    query,
    query_duration_ms,
    memory_usage,
    read_rows
FROM system.query_log 
WHERE event_time > now() - INTERVAL 1 HOUR
ORDER BY query_duration_ms DESC
LIMIT 10;

Memory management is also critical at scale. Allocate sufficient memory for Kafka consumers, factoring in batch sizes and processing loads. Additionally, enabling compression for both Kafka messages and ClickHouse® connections can improve throughput while reducing network overhead.

clickhouse_client = Client(
    host='clickhouse-server',
    compression='lz4',  # Enable compression
    settings={
        'max_insert_block_size': 1048576,  # Example setting
        'max_threads': 8
    }
)

Fault Tolerance Implementation

To manage transient network issues gracefully, implement multi-level retries. Avoid retrying for permanent errors, such as data validation failures, and cap retry attempts for transient issues.

class RetryHandler:
    def __init__(self):
        self.transient_errors = (ConnectionError, TimeoutError)
        self.permanent_errors = (ValueError, KeyError)

    def should_retry(self, error, attempt_count):
        if isinstance(error, self.permanent_errors):
            return False
        if isinstance(error, self.transient_errors) and attempt_count < 5:
            return True
        return False

    def get_delay(self, attempt):
        return min(2 ** attempt * 1000, 30000)  # Cap delay at 30 seconds

Using a circuit breaker pattern can prevent cascading failures when downstream systems experience issues. The circuit breaker halts processing temporarily if error rates exceed a set threshold.

class CircuitBreaker:
    def __init__(self, failure_threshold=5, timeout=60):
        self.failure_threshold = failure_threshold
        self.timeout = timeout
        self.failure_count = 0
        self.last_failure_time = None
        self.state = 'CLOSED'  # States: CLOSED, OPEN, HALF_OPEN

    def call(self, func, *args, **kwargs):
        if self.state == 'OPEN':
            if time.time() - self.last_failure_time > self.timeout:
                self.state = 'HALF_OPEN'
            else:
                raise Exception("Circuit breaker is OPEN")

        try:
            result = func(*args, **kwargs)
            if self.state == 'HALF_OPEN':
                self.state = 'CLOSED'
                self.failure_count = 0
            return result
        except Exception as e:
            self.failure_count += 1
            self.last_failure_time = time.time()
            if self.failure_count >= self.failure_threshold:
                self.state = 'OPEN'
            raise e

Health checks and automatic recovery mechanisms are essential for maintaining stability. Create endpoints to verify connectivity with PostgreSQL and ClickHouse®, and restart components if issues are detected.

def health_check():
    checks = {
        'kafka': check_kafka_connectivity(),
        'clickhouse': check_clickhouse_connectivity(),
        'consumer_lag': get_consumer_lag() < 10000  # Adjust threshold as needed
    }
    return all(checks.values()), checks

def auto_recovery_loop():
    while True:
        healthy, details = health_check()
        if not healthy:
            logger.warning(f"Health check failed: {details}")
            restart_unhealthy_components(details)
        time.sleep(30)

Consistency checks are another critical step. Regularly compare record counts and checksums between PostgreSQL and ClickHouse® to catch any discrepancies early. Additionally, ensure a graceful shutdown process during deployments or maintenance to avoid data loss.

import signal
import sys

class GracefulShutdown:
    def __init__(self):
        self.shutdown = False
        signal.signal(signal.SIGINT, self._exit_gracefully)
        signal.signal(signal.SIGTERM, self._exit_gracefully)

    def _exit_gracefully(self, signum, frame):
        self.shutdown = True
        logger.info("Shutdown signal received, finishing current batch...")

    def should_continue(self):
        return not self.shutdown

Tinybird vs. Self-Managed ClickHouse®

Now that we've explored the design of the custom connector, let’s dive into the deployment options for scaling real-time analytics. The key decision here is whether to self-manage ClickHouse® or opt for a managed service like Tinybird. This choice has a direct impact on development pace and long-term costs.

Challenges of Self-Managing ClickHouse®

Managing ClickHouse® on your own comes with its fair share of operational hurdles. It demands a significant level of expertise in DevOps to handle server provisioning, storage, networking, and security. Beyond just technical know-how, it often requires a dedicated team to manage the intricacies of the system.

Scaling is another major pain point. As your data grows, you’ll need to manually handle cluster management, shard rebalancing, and replica synchronization. If traffic spikes, there’s no quick fix - adding resources and reconfiguring the cluster can take hours or even days, potentially affecting performance during critical moments.

Keeping up with updates is another ongoing challenge. ClickHouse frequently releases updates, and applying them requires careful planning, testing, and execution. Beyond that, you’ll be responsible for backups, disaster recovery strategies, and patching security vulnerabilities. Query optimization and database tuning also become continuous tasks as your data and usage evolve.

Monitoring and observability are no walk in the park either. While ClickHouse provides system tables with performance metrics, creating user-friendly dashboards and setting up alerting systems requires additional tools and effort. You’ll need to monitor query performance, resource usage, and data ingestion rates across your cluster.

Finally, costs can be unpredictable. While server expenses are straightforward, the hidden costs - like engineering hours for maintenance, monitoring tools, backup storage, and downtime during incidents - can add up quickly. Many teams underestimate these factors when calculating the total cost of ownership.

Why Tinybird Simplifies Everything

Tinybird takes the complexity out of managing ClickHouse by offering a fully managed service. With Tinybird, infrastructure provisioning, scaling, and maintenance are all handled automatically, so your team can focus on building analytics features instead of wrestling with backend operations.

The platform also enhances the developer experience with its suite of tools. Tinybird’s CLI allows you to develop real-time data pipelines locally before deploying them to production. Schema migrations are seamless, and its AI-powered IDE integration speeds up query development.

One standout feature is Tinybird’s real-time API creation. You can expose ClickHouse queries as REST endpoints without needing to build custom API layers or additional application servers. This simplifies your architecture significantly.

Scaling is automated and happens in just minutes, removing the need for manual intervention during high-traffic periods. Tinybird also provides built-in observability, offering pre-configured dashboards and alerting rules right out of the box. Metrics like query performance, resource usage, and data ingestion rates are tracked automatically, saving you the effort of setting up custom monitoring tools.

For enterprise users, Tinybird includes key compliance and security features, such as SOC2 Type II and HIPAA compliance. Role-based access controls and other security measures are built into the platform, reducing your compliance workload.

Feature Comparison Table

Making the Right Choice

The decision between self-managing ClickHouse and using Tinybird often depends on your team’s resources and priorities. If you have a dedicated team of database engineers and need complete control over your infrastructure, self-managed ClickHouse might be the way to go. On the other hand, if your goal is to focus on building analytics features rather than infrastructure management, Tinybird offers a faster, more streamlined path.

In the context of our PostgreSQL connector, Tinybird’s HTTP ingestion endpoints can replace the need for custom Kafka infrastructure, simplifying data input. Ultimately, the choice between self-managed ClickHouse® and Tinybird builds on earlier considerations of system performance and operational trade-offs.

Conclusion

This guide walked through the steps of building a custom connector to enable real-time analytics. By connecting PostgreSQL to ClickHouse® using CDC, ensuring robust error handling, and optimizing performance, you can achieve consistent and reliable data for your analytics needs.

That said, managing this process yourself requires significant engineering effort. Alternatively, platforms like Tinybird offer a managed solution with features like rapid scaling, built-in observability, and API endpoint generation - all while supporting local development through its CLI. This makes it easier to focus on analytics without the heavy lifting of infrastructure management.

For teams prioritizing speed to market and streamlined analytics, Tinybird’s transparent pricing and managed services present a practical, efficient option.

FAQs

What are the benefits of using a custom connector to sync PostgreSQL data with ClickHouse® for real-time analytics?

Syncing PostgreSQL data with ClickHouse® using a custom connector brings several advantages for real-time analytics:

• Boosted performance: By shifting analytical workloads to ClickHouse®, you can keep PostgreSQL focused on transactional tasks without slowing down.

• High-speed queries on large datasets: ClickHouse® is built to manage huge volumes of data while delivering incredibly fast query responses, making it a go-to choice for real-time analytics.

• Continuous data updates: Leveraging Change Data Capture (CDC) ensures your data stays current, so your dashboards always reflect the latest information.

This approach is ideal for businesses that need rapid analytics and scalable data handling without sacrificing day-to-day operational efficiency.

How can I set up Change Data Capture (CDC) to sync data between PostgreSQL® and ClickHouse® in real time?

To enable real-time data synchronization between PostgreSQL® and ClickHouse®, you can use change data capture (CDC) techniques. This involves capturing changes in PostgreSQL® through logical replication or specialized CDC tools. Tools like PeerDB, ClickPipes, or Redpanda Connect are popular choices for streaming these changes directly into ClickHouse®.

The process typically includes setting up replication slots in PostgreSQL®, deploying a CDC connector, and building a pipeline to handle inserts, updates, and deletes efficiently. These tools are designed to streamline the setup, offering features that ensure your ClickHouse® instance remains synchronized with minimal delay.

What should I consider when choosing between Tinybird and self-managed ClickHouse® for analytics?

When weighing Tinybird against a self-managed ClickHouse® setup, your choice largely depends on how much control, scalability, and maintenance you're ready to take on.

Tinybird is a managed platform designed to simplify real-time analytics. It takes care of deployment, scaling, and maintenance, allowing you to focus on building analytics solutions quickly without getting bogged down by operational tasks.

On the flip side, self-managed ClickHouse gives you complete control over your infrastructure and the flexibility to customize it extensively. However, this approach demands dedicated resources for setup, scaling, and ongoing maintenance, which can increase both complexity and costs.

If you’re looking for a hassle-free, low-maintenance option, Tinybird is a solid pick. But if you value control and are ready to manage the infrastructure yourself, self-managed ClickHouse might be the better fit.