How to Migrate from ClickHouse to Redshift in PostgreSQL

How do I migrate data from ClickHouse to Amazon Redshift?

Move data, schema, and workloads from ClickHouse to Amazon Redshift with minimal downtime.

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Description

Why migrate from ClickHouse to Redshift?

Redshift integrates natively with AWS tooling, supports standard PostgreSQL syntax, and scales elastically.Teams looking for managed infrastructure, tight IAM security, and BI-friendly SQL often move from ClickHouse.

What are the end-to-end steps?

1) Extract ClickHouse DDL & data
2) Map data types to Redshift
3) Create tables in Redshift
4) Load data with COPY
5) Validate counts & checksums
6) Redirect applications

How do I export ClickHouse tables?

Use clickhouse-client to dump each table to compressed CSV or Parquet: clickhouse-client --query "SELECT * FROM ecommerce.Customers FORMAT Parquet" | gzip > customers.parquet.gz

How do I generate Redshift-compatible DDL?

Query ClickHouse system.columns, convert types (e.g., String → VARCHAR, DateTime64 → TIMESTAMP).Save the output as a .sql file.

What COPY options import fastest?

Load from S3 using parallel gzip files, MAXERROR 0, COMPUPDATE ON, and STATUPDATE ON. Always define DELIMITER, DATEFORMAT, and TIMEFORMAT to avoid implicit casting.

How do I verify data integrity?

After each load, run SELECT COUNT(*), MIN(id), MAX(id) FROM schema.table on both systems. Compare MD5 hashes of sorted primary keys for critical tables.

When can I switch production traffic?

Use Change Data Capture (CDC) or AWS DMS to replicate tail changes.When lag < 1 min, route reads to Redshift, then writes (if any) once confirmed.

Best practices for zero-downtime migrations?

Stage incremental loads nightly, automate validation, and keep ClickHouse as a fallback until week-long parity is proven.

Example: Migrating the Orders table

The syntax and query sections below walk through exporting Orders, creating the Redshift table, and loading data.

Why How to Migrate from ClickHouse to Redshift in PostgreSQL is important

How to Migrate from ClickHouse to Redshift in PostgreSQL Example Usage


-- Export, upload, load, validate in one script

-- ClickHouse export (run on source)
clickhouse-client --query "SELECT * FROM ecommerce.Customers FORMAT Parquet" \
| gzip > customers.parquet.gz && \
aws s3 cp customers.parquet.gz s3://my-bucket/migration/customers/

-- Redshift DDL (run on target)
CREATE TABLE public.Customers (
    id         BIGINT      NOT NULL,
    name       VARCHAR(255),
    email      VARCHAR(255),
    created_at TIMESTAMP
) DISTSTYLE KEY DISTKEY(id) SORTKEY(created_at);

-- Bulk load (run on target)
COPY public.Customers
FROM 's3://my-bucket/migration/customers/'
IAM_ROLE 'arn:aws:iam::123456789012:role/RedshiftCopy'
FORMAT AS PARQUET;

-- Integrity check
SELECT COUNT(*) FROM public.Customers;

How to Migrate from ClickHouse to Redshift in PostgreSQL Syntax


-- 1. Export ClickHouse data to Parquet
a) customers.parquet.gz          -- Customers table
b) orders.parquet.gz             -- Orders table

-- 2. Create matching Redshift table
CREATE TABLE public.Orders (
    id           BIGINT      NOT NULL,
    customer_id  BIGINT      NOT NULL,
    order_date   TIMESTAMP   NOT NULL,
    total_amount DECIMAL(12,2)
) DISTSTYLE KEY DISTKEY(customer_id) SORTKEY(order_date);

-- 3. Load from S3 into Redshift
COPY public.Orders
FROM 's3://my-bucket/migration/orders/'
IAM_ROLE 'arn:aws:iam::123456789012:role/RedshiftCopy'
FORMAT AS PARQUET
COMPUPDATE ON
STATUPDATE ON
MAXERROR 0;

-- 4. Validate row counts
SELECT COUNT(*) FROM clickhouse.ecommerce.Orders;  -- source
SELECT COUNT(*) FROM public.Orders;                -- target

Common Mistakes

Skipping data-type mapping. ClickHouse's Decimal128 will truncate if loaded into Redshift MONEY. Map precisely (Decimal128 → DECIMAL(38,0)) to avoid silent precision loss.
Loading single large files. A lone 200 GB file forces serial COPY. Split exports into many ≤1 GB gzip files so Redshift slices load in parallel, cutting hours to minutes.