How to json Clickhouse in PostgreSQL

How do I use JSONExtract and other ClickHouse JSON functions?

The json Clickhouse command lets you parse, query, and generate JSON data natively inside ClickHouse tables.

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Description

What does json Clickhouse do?

json Clickhouse (a family of JSON functions) enables reading JSON strings, extracting fields, and writing structured JSON directly in SQL. You can store semi-structured events and report on them without ETL.

When should I use json Clickhouse?

Use it when logs, click-streams, or API payloads arrive as JSON. Instead of flattening every key into columns, keep raw JSON, then query specific attributes only when needed.

How do I extract a field from a JSON column?

Use JSONExtract() to pull out a typed value. Supply the JSON column, path, and target type.

Example

SELECT JSONExtract(orders.meta,'$.payment.method','String') AS pay_method FROM Orders;

How do I retrieve nested arrays?

Leverage JSONExtractArrayRaw() or JSONExtractKeysAndValues() to return arrays or key/value pairs for further processing with arrayJoin().

Can I aggregate JSON data?

Yes. Combine groupArray() or groupUniqArray() with JSONExtract() to build JSON-like results or use JSONAgg to output JSON arrays.

Is inserting JSON expensive?

ClickHouse stores JSON as String; ingestion performance remains high. The cost appears at query time because extraction is executed on the fly.

Best practices for json Clickhouse

Store raw JSON plus a few high-cardinality keys as materialized columns for filtering. Use MATERIALIZED columns to pre-compute frequently queried JSON paths and index them with skip indexes.

Performance tip

Create a materialized view to denormalize common JSON attributes into a wide table for dashboards while retaining raw data for ad-hoc analysis.

Why How to json Clickhouse in PostgreSQL is important

How to json Clickhouse in PostgreSQL Example Usage


-- Find total card purchases per customer last month
SELECT
    c.id,
    c.name,
    count()                                 AS card_orders,
    sum(o.total_amount)                     AS total_spent
FROM Customers AS c
JOIN Orders AS o ON o.customer_id = c.id
WHERE JSONExtractString(o.meta,'$.payment.method') = 'card'
  AND o.order_date >= date_trunc('month', now()) - INTERVAL 1 MONTH
GROUP BY c.id, c.name
ORDER BY total_spent DESC;

How to json Clickhouse in PostgreSQL Syntax


-- Extract a scalar value
JSONExtract(json_column, '<json_path>', '<ClickHouse_type>')

-- Extract a string without casting
JSONExtractString(json_column, '<json_path>')

-- Extract an array as raw JSON
JSONExtractArrayRaw(json_column, '<json_path>')

-- Example context
-- "Orders" table has column meta (String) storing JSON like
-- {"payment": {"method": "card"}, "shipping": {"speed": "express"}}

-- Practical extraction
SELECT
    id,
    JSONExtractString(meta, '$.payment.method')    AS payment_method,
    JSONExtractString(meta, '$.shipping.speed')    AS ship_speed
FROM Orders;

Common Mistakes

Using JSONExtractString on non-string values. This returns NULL for numbers or booleans. Fix by choosing JSONExtract(…, 'Int64') or the correct typed variant.
Filtering directly on JSONExtract in large tables, causing slow scans. Fix by adding materialized columns or projections storing extracted fields and indexing them.