How to Migrate from Oracle to Clickhouse in PostgreSQL

How do I migrate data from Oracle to ClickHouse without downtime?

Use schema extraction, data export, and ClickHouse’s INSERT or clickhouse-client to move Oracle tables and data into ClickHouse efficiently.

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Description

Example H2

Example H3

Why migrate Oracle data to ClickHouse?

ClickHouse’s columnar engine delivers sub-second analytics at lower cost. Moving historical Oracle data into ClickHouse speeds dashboards without replacing mission-critical OLTP workloads.

What are the high-level steps?

1) Extract Oracle schema 2) Map data types to ClickHouse 3) Export data (CSV/Parquet) 4) Create ClickHouse tables 5) Load files or stream through clickhouse-client 6) Validate counts & spot-check queries.

How do I extract Oracle DDL?

Run DBMS_METADATA.GET_DDL or expdp with CONTENT=METADATA_ONLY.Save table, index, and constraint definitions to SQL files.

How to map Oracle types to ClickHouse?

NUMBER → Decimal/Int64; VARCHAR2/CLOB → String; DATE/TIMESTAMP → DateTime64; BLOB → String + Codec; RAW → FixedString(N). Adjust precision and nullability manually.

How to create ClickHouse tables?

Edit the generated DDL: replace types, remove Oracle storage clauses, add ENGINE (usually MergeTree or ReplicatedMergeTree) and a primary key (ORDER BY).Example shown below.

How to export Oracle data fast?

Use sqlplus SET MARKUP CSV, sqlcl, or expdp with access_method=direct_path to write compressed CSV files. Each table → separate file.

How to load data into ClickHouse?

Option 1: clickhouse-client --query="INSERT INTO … FORMAT CSV" < file.csv. Option 2: stage files in S3 and use INSERT INTO … SELECT * FROM s3(…). Enable input_format_defaults_for_omitted_fields=1 to handle missing columns.

What validation queries should I run?

Run SELECT count(*), min/max timestamps, and checksum hashes on numeric columns in both databases.Randomly sample rows for deep comparison.

How to keep Oracle & ClickHouse in sync?

Use Oracle GoldenGate → Kafka → ClickHouse, or deploy Debezium Oracle connector to stream CDC events into ClickHouse Materialized Views.

Best practices for production migrations?

Partition large tables by day, compress exports, parallel-load with GNU parallel, disable ClickHouse quorum inserts during bulk load, and run loads in UTC to avoid DST issues.

Why How to Migrate from Oracle to Clickhouse in PostgreSQL is important

How to Migrate from Oracle to Clickhouse in PostgreSQL Example Usage


--Bulk-load Orders from Oracle dump into ClickHouse
cat orders.csv | clickhouse-client --query "INSERT INTO Orders FORMAT CSVWithNames"

How to Migrate from Oracle to Clickhouse in PostgreSQL Syntax


--1. Extract Oracle schema
SELECT DBMS_METADATA.GET_DDL('TABLE','CUSTOMERS') FROM dual;

--2. Sample Oracle → ClickHouse DDL mapping
CREATE TABLE Customers
(
    id           Int64,
    name         String,
    email        String,
    created_at   DateTime64(3)
)
ENGINE = MergeTree
ORDER BY id;

--3. Oracle data export (bash)
expdp system/*** tables=customers directory=DATA_PUMP_DIR dumpfile=customers.dmp logfile=exp.log access_method=direct_path

--4. Convert dump to CSV (optional)
impdp ... remap_schema ... SQLFILE=ddl.sql

--5. Load into ClickHouse
cat customers.csv | clickhouse-client --query "INSERT INTO Customers FORMAT CSV"

Common Mistakes

Ignoring type precision—mapping Oracle NUMBER(38,10) to ClickHouse Float64 loses precision. Always use Decimal for exact values.
Loading without ORDER BY key—importing into MergeTree without a matching ORDER BY causes heavy merges. Pre-sort files on key columns before INSERT.

Frequently Asked Questions (FAQs)

Can I migrate live without full downtime?

Yes. Perform an initial bulk load, then apply CDC streams (GoldenGate, Debezium) until lag is zero. Cut over reads to ClickHouse afterward.

How large can a single CSV file be?

ClickHouse handles multi-GB files, but splitting into 1–2 GB chunks enables parallel loading and easier retry on failure.

What about Oracle PL/SQL logic?

Procedures and triggers don’t translate. Re-implement business logic in the application layer or use ClickHouse Materialized Views for simple aggregations.