Document common patterns working with JSON data replicated from MongoDB CDC Connector

docs/integrations/data-ingestion/clickpipes/mongodb/quickstart.md

@@ -0,0 +1,311 @@
+---
+title: 'Working with JSON in ClickHouse'
+sidebar_label: 'Working with JSON'
+slug: /integrations/clickpipes/mongodb/quickstart
+description: 'Common patterns for working with JSON data replicated from MongoDB to ClickHouse via ClickPipes'
+---
+
+# Working with JSON in ClickHouse
+
+This guide provides common patterns for working with JSON data replicated from MongoDB to ClickHouse via ClickPipes.
+
+Suppose we created a collection `t1` in MongoDB to track customer orders:
+
+```javascript
+db.t1.insertOne({
+  "order_id": "ORD-001234",
+  "customer_id": 98765,
+  "status": "completed",
+  "total_amount": 299.97,
+  "order_date": new Date(),
+  "shipping": {
+    "method": "express",
+    "city": "Seattle",
+    "cost": 19.99
+  },
+  "items": [
+    {
+      "category": "electronics",
+      "price": 149.99
+    },
+    {
+      "category": "accessories",
+      "price": 24.99
+    }
+  ]
+})
+```
+
+MongoDB CDC Connector replicates MongoDB documents to ClickHouse using the native JSON data type. The replicated table `t1` in ClickHouse will contain the following row:
+
+```shell
+Row 1:
+──────
+_id:                "68a4df4b9fe6c73b541703b0"
+_full_document:     {"_id":"68a4df4b9fe6c73b541703b0","customer_id":"98765","items":[{"category":"electronics","price":149.99},{"category":"accessories","price":24.99}],"order_date":"2025-08-19T20:32:11.705Z","order_id":"ORD-001234","shipping":{"city":"Seattle","cost":19.99,"method":"express"},"status":"completed","total_amount":299.97}
+_peerdb_synced_at:  2025-08-19 20:50:42.005000000
+_peerdb_is_deleted: 0
+_peerdb_version:    0
+```
+
+## Table schema {#table-schema}
+
+The replicated tables use this standard schema:
+
+```shell
+┌─name───────────────┬─type──────────┐
+│ _id                │ String        │
+│ _full_document     │ JSON          │
+│ _peerdb_synced_at  │ DateTime64(9) │
+│ _peerdb_version    │ Int64         │
+│ _peerdb_is_deleted │ Int8          │
+└────────────────────┴───────────────┘
+```
+
+- `_id`: Primary key from MongoDB
+- `_full_document`: MongoDB document replicated as JSON data type
+- `_peerdb_synced_at`: Records when the row was last synced
+- `_peerdb_version`: Tracks the version of the row; incremented when the row is updated or deleted
+- `_peerdb_is_deleted`: Marks whether the row is deleted
+
+### ReplacingMergeTree table engine {#replacingmergetree-table-engine}
+
+ClickPipes maps MongoDB collections into ClickHouse using the `ReplacingMergeTree` table engine family. With this engine, updates are modeled as inserts with a newer version (`_peerdb_version`) of the document for a given primary key (`_id`), enabling efficient handling of updates, replaces, and deletes as versioned inserts. 
+
+`ReplacingMergeTree` clears out duplicates asynchronously in the background. To guarantee the absence of duplicates for the same row, use the [`FINAL` modifier](https://clickhouse.com/docs/sql-reference/statements/select/from#final-modifier). For example:
+
+```sql
+SELECT * FROM t1 FINAL;
+```
+
+### Handling deletes {#handling-deletes}
+
+Deletes from MongoDB are propagated as new rows marked as deleted using the `_peerdb_is_deleted` column. You typically want to filter these out in your queries:
+
+```sql
+SELECT * FROM t1 FINAL WHERE _peerdb_is_deleted = 0;
+```
+
+You can also create a row-level policy to automatically filter out deleted rows instead of specifying the filter in each query:
+
+```sql
+CREATE ROW POLICY policy_name ON t1
+FOR SELECT USING _peerdb_is_deleted = 0;
+```
+
+## Querying JSON data {#querying-json-data}
+
+You can directly query JSON fields using dot syntax:
+
+```sql title="Query"
+SELECT
+    _full_document.order_id,
+    _full_document.shipping.method
+FROM t1;
+```
+
+```shell title="Result"
+┌─_full_document.order_id─┬─_full_document.shipping.method─┐
+│ ORD-001234              │ express                        │
+└─────────────────────────┴────────────────────────────────┘
+```
+
+### Dynamic type {#dynamic-type}
+
+In ClickHouse, each field in JSON has `Dynamic` type. Dynamic type allows ClickHouse to store values of any type without knowing the type in advance. You can verify this with the `toTypeName` function:
+
+```sql title="Query"
+SELECT toTypeName(_full_document.customer_id) AS type FROM t1;
+```
+
+```shell title="Result"
+┌─type────┐
+│ Dynamic │
+└─────────┘
+```
+
+To examine the underlying data type(s) for a field, you can check with the `dynamicType` function. Note that it's possible to have different data types for the same field name in different rows:
+
+```sql title="Query"
+SELECT dynamicType(_full_document.customer_id) AS type FROM t1;
+```
+
+```shell title="Result"
+┌─type──┐
+│ Int64 │
+└───────┘
+```
+
+[Regular functions](https://clickhouse.com/docs/sql-reference/functions/regular-functions) work for dynamic type just like they do for regular columns:
+
+**Example 1: Date parsing**
+
+```sql title="Query"
+SELECT parseDateTimeBestEffortOrNull(_full_document.order_date) AS order_date FROM t1;
+```
+
+```shell title="Result"
+┌─order_date──────────┐
+│ 2025-08-19 20:32:11 │
+└─────────────────────┘
+```
+
+**Example 2: Conditional logic**
+
+```sql title="Query"
+SELECT multiIf(
+    _full_document.total_amount < 100, 'less_than_100',
+    _full_document.total_amount < 1000, 'less_than_1000',
+    '1000+') AS spendings
+FROM t1;
+```
+
+```shell title="Result"
+┌─spendings──────┐
+│ less_than_1000 │
+└────────────────┘
+```
+
+**Example 3: Array operations**
+
+```sql title="Query"
+SELECT length(_full_document.items) AS item_count FROM t1;
+```
+
+```shell title="Result"
+┌─item_count─┐
+│          2 │
+└────────────┘
+```
+
+### Field casting {#field-casting}
+
+[Aggregation functions](https://clickhouse.com/docs/sql-reference/aggregate-functions/combinators) in ClickHouse don't work with dynamic type directly. For example, if you attempt to directly use the `sum` function on a dynamic type, you get the following error:
+
+```sql
+SELECT sum(_full_document.shipping.cost) AS shipping_cost FROM t1;
+-- DB::Exception: Illegal type Dynamic of argument for aggregate function sum. (ILLEGAL_TYPE_OF_ARGUMENT)
+```
+
+To use aggregation functions, cast the field to the appropriate type with the `CAST` function or `::` syntax:
+
+```sql title="Query"
+SELECT sum(_full_document.shipping.cost::Float32) AS shipping_cost FROM t1;
+```
+
+```shell title="Result"
+┌─shipping_cost─┐
+│         19.99 │
+└───────────────┘
+```
+
+:::note
+Casting from dynamic type to the underlying data type (determined by `dynamicType`) is very performant, as ClickHouse already stores the value in its underlying type internally.
+:::
+
+## Flattening JSON {#flattening-json}
+
+### Normal view {#normal-view}
+
+You can create normal views on top of the JSON table to encapsulate flattening/casting/transformation logic in order to query data similar to a relational table. Normal views are lightweight as they only store the query itself, not the underlying data. For example:
+
+```sql
+CREATE VIEW v1 AS
+SELECT
+    CAST(_full_document._id, 'String') AS object_id,
+    CAST(_full_document.order_id, 'String') AS order_id,
+    CAST(_full_document.customer_id, 'Int64') AS customer_id,
+    CAST(_full_document.status, 'String') AS status,
+    CAST(_full_document.total_amount, 'Decimal64(2)') AS total_amount,
+    CAST(parseDateTime64BestEffortOrNull(_full_document.order_date, 3), 'DATETIME(3)') AS order_date,
+    CAST(_full_document.shipping.method, 'String') AS shipping_method,
+    CAST(_full_document.shipping.city, 'String') AS shipping_city,
+    CAST(_full_document.shipping.cost, 'Decimal64(2)') AS shipping_cost,
+    _full_document.items AS items
+FROM t1 FINAL
+WHERE _peerdb_is_deleted = 0;
+```
+
+This view will have the following schema:
+
+```shell
+┌─name────────────┬─type───────────┐
+│ object_id       │ String         │
+│ order_id        │ String         │
+│ customer_id     │ Int64          │
+│ status          │ String         │
+│ total_amount    │ Decimal(18, 2) │
+│ order_date      │ DateTime64(3)  │
+│ shipping_method │ String         │
+│ shipping_city   │ String         │
+│ shipping_cost   │ Decimal(18, 2) │
+│ items           │ Dynamic        │
+└─────────────────┴────────────────┘
+```
+
+You can now query the view similar to how you would query a flattened table:
+
+```sql
+SELECT
+    customer_id,
+    sum(total_amount)
+FROM v1
+WHERE shipping_city = 'Seattle'
+GROUP BY customer_id
+ORDER BY customer_id DESC
+LIMIT 10;
+```
+
+### Refreshable materialized view {#refreshable-materialized-view}
+
+You can also create [Refreshable Materialized Views](https://clickhouse.com/docs/materialized-view/refreshable-materialized-view), which enable you to schedule query execution for deduplicating rows and storing the results in a flattened destination table. With each scheduled refresh, the destination table is replaced with the latest query results.
+
+The key advantage of this method is that the query using the `FINAL` keyword runs only once during the refresh, eliminating the need for subsequent queries on the destination table to use `FINAL`.
+
+However, a drawback is that the data in the destination table is only as up-to-date as the most recent refresh. For many use cases, refresh intervals ranging from several minutes to a few hours provide a good balance between data freshness and query performance.
+
+```sql
+CREATE TABLE flattened_t1 (
+    `_id` String,
+    `order_id` String,
+    `customer_id` Int64,
+    `status` String,
+    `total_amount` Decimal(18, 2),
+    `order_date` DateTime64(3),
+    `shipping_method` String,
+    `shipping_city` String,
+    `shipping_cost` Decimal(18, 2),
+    `items` Dynamic
+)
+ENGINE = ReplacingMergeTree()
+PRIMARY KEY _id
+ORDER BY _id;
+
+CREATE MATERIALIZED VIEW mv1 REFRESH EVERY 1 HOUR TO flattened_t1 AS
+SELECT 
+    CAST(_full_document._id, 'String') AS _id,
+    CAST(_full_document.order_id, 'String') AS order_id,
+    CAST(_full_document.customer_id, 'Int64') AS customer_id,
+    CAST(_full_document.status, 'String') AS status,
+    CAST(_full_document.total_amount, 'Decimal64(2)') AS total_amount,
+    CAST(parseDateTime64BestEffortOrNull(_full_document.order_date, 3), 'DATETIME(3)') AS order_date,
+    CAST(_full_document.shipping.method, 'String') AS shipping_method,
+    CAST(_full_document.shipping.city, 'String') AS shipping_city,
+    CAST(_full_document.shipping.cost, 'Decimal64(2)') AS shipping_cost,
+    _full_document.items AS items
+FROM t1 FINAL
+WHERE _peerdb_is_deleted = 0;
+```
+
+You can now query the table `flattened_t1` directly without the `FINAL` modifier:
+
+```sql
+SELECT
+    customer_id,
+    sum(total_amount)
+FROM flattened_t1
+WHERE shipping_city = 'Seattle'
+GROUP BY customer_id
+ORDER BY customer_id DESC
+LIMIT 10;
+```

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