From 82c27ed080bc61fdb3d534028a1dc00b34144eed Mon Sep 17 00:00:00 2001 From: niebayes Date: Thu, 28 May 2026 10:27:48 +0000 Subject: [PATCH 1/2] add docs for lookup join --- dir.yaml | 3 + en_US/streaming/computation.md | 357 +++++++++++++++++++++++++++++++++ zh_CN/streaming/computation.md | 357 +++++++++++++++++++++++++++++++++ 3 files changed, 717 insertions(+) create mode 100644 en_US/streaming/computation.md create mode 100644 zh_CN/streaming/computation.md diff --git a/dir.yaml b/dir.yaml index 4782887e..2c18dd2a 100644 --- a/dir.yaml +++ b/dir.yaml @@ -360,6 +360,9 @@ - title_en: Use cases title_cn: 应用示例 path: streaming/use_cases + - title_en: Computation + title_cn: 计算能力 + path: streaming/computation - title_en: Formats title_cn: Formats path: streaming/format diff --git a/en_US/streaming/computation.md b/en_US/streaming/computation.md new file mode 100644 index 00000000..9b31c849 --- /dev/null +++ b/en_US/streaming/computation.md @@ -0,0 +1,357 @@ +--- +title: "Datalayers Streaming - Computation" +description: "Describes the computation capabilities supported by Datalayers streaming, including projection, filtering, and Lookup Join, with SQL examples and expected results." +--- + +# Streaming - Computation + +This document introduces the computation capabilities currently supported by Datalayers streaming and provides `CREATE PIPELINE` SQL examples with expected query results for each. + +## Supported Capabilities + +| Capability | Description | +| --- | --- | +| Projection | Select specific columns from the source to the sink | +| Filtering | Apply conditions on events using `WHERE` | +| Lookup Join | Join a streaming source with an internal Datalayers dimension table (TimeSeries) using `INNER JOIN` or `LEFT JOIN` | + +> Note: The current version only supports TimeSeries tables as dimension tables. Relational tables are not yet supported. + +## Environment Setup + +The following examples are based on a Kafka source. Please complete the basic environment setup described in [Quick Start](/streaming/quick-start), including: + +1. **Start Kafka** + + ```bash + docker pull confluentinc/cp-kafka:7.7.1 + docker run -d --name dl-kafka \ + -p 9092:9092 \ + -e KAFKA_NODE_ID=1 \ + -e KAFKA_PROCESS_ROLES='broker,controller' \ + -e KAFKA_LISTENERS='PLAINTEXT://0.0.0.0:9092,CONTROLLER://0.0.0.0:9093' \ + -e KAFKA_ADVERTISED_LISTENERS='PLAINTEXT://127.0.0.1:9092' \ + -e KAFKA_LISTENER_SECURITY_PROTOCOL_MAP='CONTROLLER:PLAINTEXT,PLAINTEXT:PLAINTEXT' \ + -e KAFKA_CONTROLLER_QUORUM_VOTERS='1@127.0.0.1:9093' \ + -e KAFKA_CONTROLLER_LISTENER_NAMES='CONTROLLER' \ + -e KAFKA_INTER_BROKER_LISTENER_NAME='PLAINTEXT' \ + -e CLUSTER_ID='MkU3OEVBNTcwNTJENDM2Qk' \ + confluentinc/cp-kafka:7.7.1 + ``` + +2. **Launch dlsql** + + ```bash + dlsql -h 127.0.0.1 -P 8360 -u admin -p public + ``` + +3. **Create database** + + ```sql + CREATE DATABASE stream_demo; + USE stream_demo; + ``` + +### Shared Kafka Topic + +All examples share the same Kafka topic `topic_compute_demo`. Create the topic: + +```bash +docker exec -it dl-kafka kafka-topics \ + --bootstrap-server 127.0.0.1:9092 \ + --create --topic topic_compute_demo \ + --partitions 1 --replication-factor 1 +``` + +### Shared Source + +Create a shared source for all subsequent pipeline examples: + +```sql +CREATE SOURCE src_events ( + ts TIMESTAMP(9), + order_id STRING, + customer_id STRING, + product STRING, + quantity INT32, + price FLOAT64, + WATERMARK FOR ts +) WITH ( + connector='kafka', + brokers='127.0.0.1:9092', + topic='topic_compute_demo', + offset='earliest', + format='json' +); +``` + +### Shared Sink Table + +All examples share the same sink table (truncate or recreate between examples): + +```sql +CREATE TABLE sink_t ( + ts TIMESTAMP(9), + order_id STRING, + customer_id STRING, + product STRING, + quantity INT32, + price FLOAT64, + -- extra columns used by lookup join examples + customer_name STRING, + customer_tier STRING, + TIMESTAMP KEY(ts) +) ENGINE=TimeSeries +PARTITION BY HASH(order_id) PARTITIONS 1; +``` + +### Publish Test Data + +```bash +docker exec -it dl-kafka kafka-console-producer \ + --bootstrap-server 127.0.0.1:9092 \ + --topic topic_compute_demo +``` + +Enter the following JSON data: + +```json +{"ts":"2025-01-01T00:00:01Z","order_id":"ord-1","customer_id":"cust-a","product":"widget","quantity":10,"price":9.99} +{"ts":"2025-01-01T00:00:02Z","order_id":"ord-2","customer_id":"cust-b","product":"gadget","quantity":5,"price":19.99} +{"ts":"2025-01-01T00:00:03Z","order_id":"ord-3","customer_id":"cust-a","product":"doodad","quantity":2,"price":4.99} +{"ts":"2025-01-01T00:00:04Z","order_id":"ord-4","customer_id":"cust-c","product":"widget","quantity":1,"price":9.99} +``` + +Press `Ctrl-D` after input. + +--- + +## Projection + +Projection selects a subset of columns from the source to send to the sink. It can also compute derived columns via expressions. + +### Example: Select Specific Columns + +```sql +CREATE PIPELINE p_projection +SINK TO sink_t +AS +SELECT ts, order_id, customer_id, product +FROM src_events; +``` + +### Query Results + +```sql +SELECT order_id, customer_id, product FROM sink_t ORDER BY order_id; +``` + +```text ++---------+-------------+---------+ +| order_id| customer_id | product | ++---------+-------------+---------+ +| ord-1 | cust-a | widget | +| ord-2 | cust-b | gadget | +| ord-3 | cust-a | doodad | +| ord-4 | cust-c | widget | ++---------+-------------+---------+ +``` + +### Cleanup + +```sql +ALTER PIPELINE p_projection STOP; +DROP PIPELINE p_projection; +``` + +--- + +## Filtering + +Use the `WHERE` clause to keep only events that satisfy a condition. + +### Example: Filter Orders with Quantity > 2 and Price > 5.0 + +```sql +CREATE PIPELINE p_filter +SINK TO sink_t +AS +SELECT ts, order_id, customer_id, product, quantity, price +FROM src_events +WHERE quantity > 2 AND price > 5.0; +``` + +### Query Results + +```sql +SELECT order_id, customer_id, quantity, price FROM sink_t ORDER BY order_id; +``` + +```text ++---------+-------------+----------+-------+ +| order_id| customer_id | quantity | price | ++---------+-------------+----------+-------+ +| ord-1 | cust-a | 10 | 9.99 | +| ord-2 | cust-b | 5 | 19.99 | ++---------+-------------+----------+-------+ +``` + +`ord-3` has `price` 4.99 (not > 5.0), so it is filtered out. `ord-4` has `quantity` 1 (not > 2), so it is also filtered out. + +### Cleanup + +```sql +ALTER PIPELINE p_filter STOP; +DROP PIPELINE p_filter; +``` + +--- + +## Lookup Join + +Lookup Join allows joining a streaming source with an internal Datalayers dimension table, using fields from the source to look up corresponding attributes in the dimension table and appending the matched columns to the output. + +### Supported Features + +| Feature | Support | +| --- | --- | +| JOIN Types | `INNER JOIN`, `LEFT JOIN` | +| Dimension Table Type | TimeSeries table | +| Join Condition | Equi-join (`=`), right-side column must be a dimension table primary-key column | +| Composite Keys | Supported (the join condition column just needs to be part of the primary key) | +| Multi-row Matches | Standard SQL join behavior: each matching row in the dimension table combines with the source row | + +> Currently **not supported**: +> - Non-equi joins +> - `RIGHT JOIN` / `FULL OUTER JOIN` +> - Relational tables as dimension tables +> - External systems (MySQL, Redis, etc.) as dimension tables + +### Dimension Table Setup + +Create a customer dimension table with `customer_id`, `customer_name`, and `tier`. + +```sql +CREATE TABLE dim_customers ( + customer_id STRING, + customer_name STRING, + tier STRING, + ts TIMESTAMP(9), + TIMESTAMP KEY(ts), + PRIMARY KEY (customer_id, ts) +) ENGINE=TimeSeries +PARTITION BY HASH(customer_id) PARTITIONS 1; +``` + +Insert dimension data: + +```sql +INSERT INTO dim_customers (ts, customer_id, customer_name, tier) VALUES +('2025-01-01T00:00:00Z', 'cust-a', 'Alice', 'Gold'), +('2025-01-01T00:00:00Z', 'cust-b', 'Bob', 'Silver'); +-- cust-c has no dimension record — used to demonstrate NULL-fill in LEFT JOIN +``` + +### INNER JOIN Example + +Only matched orders are emitted; unmatched orders are dropped: + +```sql +CREATE PIPELINE p_lookup_inner +SINK TO sink_t +AS +SELECT + e.ts, + e.order_id, + e.customer_id, + e.product, + e.quantity, + e.price, + c.customer_name, + c.tier +FROM src_events e +INNER JOIN dim_customers c +ON e.customer_id = c.customer_id; +``` + +Query results: + +```sql +SELECT order_id, customer_id, customer_name, tier FROM sink_t ORDER BY order_id; +``` + +```text ++---------+-------------+---------------+-------+ +| order_id| customer_id | customer_name | tier | ++---------+-------------+---------------+-------+ +| ord-1 | cust-a | Alice | Gold | +| ord-2 | cust-b | Bob | Silver| +| ord-3 | cust-a | Alice | Gold | ++---------+-------------+---------------+-------+ +``` + +`ord-4` (`cust-c`) has no matching record in the dimension table and is dropped by `INNER JOIN`. + +### LEFT JOIN Example + +LEFT JOIN preserves all source events. When no dimension record matches, the dimension columns are NULL: + +```sql +CREATE PIPELINE p_lookup_left +SINK TO sink_t +AS +SELECT + e.ts, + e.order_id, + e.customer_id, + e.product, + e.quantity, + e.price, + c.customer_name, + c.tier +FROM src_events e +LEFT JOIN dim_customers c +ON e.customer_id = c.customer_id; +``` + +Query results: + +```sql +SELECT order_id, customer_id, customer_name, tier FROM sink_t ORDER BY order_id; +``` + +```text ++---------+-------------+---------------+-------+ +| order_id| customer_id | customer_name | tier | ++---------+-------------+---------------+-------+ +| ord-1 | cust-a | Alice | Gold | +| ord-2 | cust-b | Bob | Silver| +| ord-3 | cust-a | Alice | Gold | +| ord-4 | cust-c | | | ++---------+-------------+---------------+-------+ +``` + +`ord-4` (`cust-c`) is kept, but `customer_name` and `tier` are NULL. + +### Cleanup + +```sql +ALTER PIPELINE p_lookup_inner STOP; +DROP PIPELINE p_lookup_inner; +ALTER PIPELINE p_lookup_left STOP; +DROP PIPELINE p_lookup_left; +TRUNCATE TABLE sink_t; +``` + +--- + +## Limitations & Roadmap + +| Capability | Status | Roadmap | +| --- | --- | --- | +| Relational dimension tables | Not supported | Future release | +| External dimension tables (MySQL, Redis, etc.) | Not supported | Future release via `CREATE SOURCE ... WITH (lookup='true')` | +| `RIGHT JOIN` / `FULL OUTER JOIN` | Not supported | Under evaluation | +| Non-equi joins | Not supported | Under evaluation | +| CDC dimension table updates | Not supported | Future TTL-based or CDC pipeline support | +| Lookup Cache | Not enabled | Will be enabled in a future release to reduce redundant queries | diff --git a/zh_CN/streaming/computation.md b/zh_CN/streaming/computation.md new file mode 100644 index 00000000..4909c638 --- /dev/null +++ b/zh_CN/streaming/computation.md @@ -0,0 +1,357 @@ +--- +title: "Datalayers 流计算 - 计算能力" +description: "介绍 Datalayers 流计算支持的计算功能，包括投影、过滤、Lookup Join，并提供 SQL 示例与预期结果。" +--- + +# 流计算 - 计算能力 + +本文介绍 Datalayers 流计算当前支持的计算能力，并给出每种能力的 `CREATE PIPELINE` SQL 示例及预期的查询结果。 + +## 支持的能力 + +| 能力 | 说明 | +| --- | --- | +| 投影（Projection） | 从 source 中选择特定列输出 | +| 过滤（Filter） | 使用 `WHERE` 子句对事件进行条件过滤 | +| Lookup Join | 将流式 source 与 Datalayers 内部维表（TimeSeries）进行 `INNER JOIN` 或 `LEFT JOIN` | + +> 注意：当前版本仅支持 TimeSeries 表作为维表，暂不支持 Relational 表。 + +## 环境准备 + +以下示例均基于 Kafka source。请确保已完成 [快速开始](/streaming/quick-start) 中的基础环境准备，包括： + +1. **启动 Kafka** + + ```bash + docker pull confluentinc/cp-kafka:7.7.1 + docker run -d --name dl-kafka \ + -p 9092:9092 \ + -e KAFKA_NODE_ID=1 \ + -e KAFKA_PROCESS_ROLES='broker,controller' \ + -e KAFKA_LISTENERS='PLAINTEXT://0.0.0.0:9092,CONTROLLER://0.0.0.0:9093' \ + -e KAFKA_ADVERTISED_LISTENERS='PLAINTEXT://127.0.0.1:9092' \ + -e KAFKA_LISTENER_SECURITY_PROTOCOL_MAP='CONTROLLER:PLAINTEXT,PLAINTEXT:PLAINTEXT' \ + -e KAFKA_CONTROLLER_QUORUM_VOTERS='1@127.0.0.1:9093' \ + -e KAFKA_CONTROLLER_LISTENER_NAMES='CONTROLLER' \ + -e KAFKA_INTER_BROKER_LISTENER_NAME='PLAINTEXT' \ + -e CLUSTER_ID='MkU3OEVBNTcwNTJENDM2Qk' \ + confluentinc/cp-kafka:7.7.1 + ``` + +2. **进入 dlsql** + + ```bash + dlsql -h 127.0.0.1 -P 8360 -u admin -p public + ``` + +3. **创建数据库** + + ```sql + CREATE DATABASE stream_demo; + USE stream_demo; + ``` + +### 公共 Kafka Topic + +本文所有示例共用同一个 Kafka topic `topic_compute_demo`。创建 topic： + +```bash +docker exec -it dl-kafka kafka-topics \ + --bootstrap-server 127.0.0.1:9092 \ + --create --topic topic_compute_demo \ + --partitions 1 --replication-factor 1 +``` + +### 公共 Source + +创建一个公共 source，后续各个示例会在此基础上创建 pipeline： + +```sql +CREATE SOURCE src_events ( + ts TIMESTAMP(9), + order_id STRING, + customer_id STRING, + product STRING, + quantity INT32, + price FLOAT64, + WATERMARK FOR ts +) WITH ( + connector='kafka', + brokers='127.0.0.1:9092', + topic='topic_compute_demo', + offset='earliest', + format='json' +); +``` + +### 公共 Sink Table + +所有示例共享同一个 sink table（每次示例使用前会清空或删除重建）： + +```sql +CREATE TABLE sink_t ( + ts TIMESTAMP(9), + order_id STRING, + customer_id STRING, + product STRING, + quantity INT32, + price FLOAT64, + -- 以下是 lookup join 示例可能新增的列 + customer_name STRING, + customer_tier STRING, + TIMESTAMP KEY(ts) +) ENGINE=TimeSeries +PARTITION BY HASH(order_id) PARTITIONS 1; +``` + +### 发布测试数据 + +```bash +docker exec -it dl-kafka kafka-console-producer \ + --bootstrap-server 127.0.0.1:9092 \ + --topic topic_compute_demo +``` + +输入以下 JSON 数据： + +```json +{"ts":"2025-01-01T00:00:01Z","order_id":"ord-1","customer_id":"cust-a","product":"widget","quantity":10,"price":9.99} +{"ts":"2025-01-01T00:00:02Z","order_id":"ord-2","customer_id":"cust-b","product":"gadget","quantity":5,"price":19.99} +{"ts":"2025-01-01T00:00:03Z","order_id":"ord-3","customer_id":"cust-a","product":"doodad","quantity":2,"price":4.99} +{"ts":"2025-01-01T00:00:04Z","order_id":"ord-4","customer_id":"cust-c","product":"widget","quantity":1,"price":9.99} +``` + +输入完毕后按 `Ctrl-D`。 + +--- + +## 投影（Projection） + +投影用于选择 source 的部分列输出到 sink，也可以通过表达式计算新列。 + +### 示例：选择部分列 + +```sql +CREATE PIPELINE p_projection +SINK TO sink_t +AS +SELECT ts, order_id, customer_id, product +FROM src_events; +``` + +### 查询结果 + +```sql +SELECT order_id, customer_id, product FROM sink_t ORDER BY order_id; +``` + +```text ++---------+-------------+---------+ +| order_id| customer_id | product | ++---------+-------------+---------+ +| ord-1 | cust-a | widget | +| ord-2 | cust-b | gadget | +| ord-3 | cust-a | doodad | +| ord-4 | cust-c | widget | ++---------+-------------+---------+ +``` + +### 清理 + +```sql +ALTER PIPELINE p_projection STOP; +DROP PIPELINE p_projection; +``` + +--- + +## 过滤（Filter） + +使用 `WHERE` 子句仅保留满足条件的事件。 + +### 示例：过滤数量大于 2 且价格大于 5.0 的订单 + +```sql +CREATE PIPELINE p_filter +SINK TO sink_t +AS +SELECT ts, order_id, customer_id, product, quantity, price +FROM src_events +WHERE quantity > 2 AND price > 5.0; +``` + +### 查询结果 + +```sql +SELECT order_id, customer_id, quantity, price FROM sink_t ORDER BY order_id; +``` + +```text ++---------+-------------+----------+-------+ +| order_id| customer_id | quantity | price | ++---------+-------------+----------+-------+ +| ord-1 | cust-a | 10 | 9.99 | +| ord-2 | cust-b | 5 | 19.99 | ++---------+-------------+----------+-------+ +``` + +注意：`ord-3` 虽然 `price` 满足条件，但 `quantity` 为 2（不大于 2），因此被过滤；`ord-4` 的 `quantity` 为 1 且 `price` 为 9.99，同样被过滤。 + +### 清理 + +```sql +ALTER PIPELINE p_filter STOP; +DROP PIPELINE p_filter; +``` + +--- + +## Lookup Join + +Lookup Join 允许将流式 source 与 Datalayers 内部的维表进行关联，用 source 中的字段去维表中查找对应的属性，并将查到的列拼接到输出中。 + +### 支持情况 + +| 特性 | 支持 | +| --- | --- | +| JOIN 类型 | `INNER JOIN`、`LEFT JOIN` | +| 维表类型 | TimeSeries 表 | +| JOIN 条件 | 等值连接（`=`），右表列为维表主键列 | +| 复合主键 | 支持（只要 JOIN 条件中的右表列是主键的一部分即可） | +| 多行匹配 | 按 SQL 标准行为处理：维表中多行匹配同一 key 时，每个匹配行都与 source 行组合输出多条结果 | + +> 当前版本**不支持**： +> - 非等值连接 +> - `RIGHT JOIN` / `FULL OUTER JOIN` +> - Relational 表作为维表 +> - 外部系统（MySQL、Redis 等）作为维表 + +### 维表准备 + +创建一个客户维表，包含 `customer_id`、`customer_name` 和 `tier`。 + +```sql +CREATE TABLE dim_customers ( + customer_id STRING, + customer_name STRING, + tier STRING, + ts TIMESTAMP(9), + TIMESTAMP KEY(ts), + PRIMARY KEY (customer_id, ts) +) ENGINE=TimeSeries +PARTITION BY HASH(customer_id) PARTITIONS 1; +``` + +插入维表数据： + +```sql +INSERT INTO dim_customers (ts, customer_id, customer_name, tier) VALUES +('2025-01-01T00:00:00Z', 'cust-a', 'Alice', 'Gold'), +('2025-01-01T00:00:00Z', 'cust-b', 'Bob', 'Silver'); +-- cust-c 没有维表记录，用于展示 LEFT JOIN 的 NULL 填充行为 +``` + +### INNER JOIN 示例 + +仅输出在维表中匹配到的订单，不匹配的订单被丢弃： + +```sql +CREATE PIPELINE p_lookup_inner +SINK TO sink_t +AS +SELECT + e.ts, + e.order_id, + e.customer_id, + e.product, + e.quantity, + e.price, + c.customer_name, + c.tier +FROM src_events e +INNER JOIN dim_customers c +ON e.customer_id = c.customer_id; +``` + +查询结果： + +```sql +SELECT order_id, customer_id, customer_name, tier FROM sink_t ORDER BY order_id; +``` + +```text ++---------+-------------+---------------+-------+ +| order_id| customer_id | customer_name | tier | ++---------+-------------+---------------+-------+ +| ord-1 | cust-a | Alice | Gold | +| ord-2 | cust-b | Bob | Silver| +| ord-3 | cust-a | Alice | Gold | ++---------+-------------+---------------+-------+ +``` + +`ord-4`（`cust-c`）在维表中没有记录，被 `INNER JOIN` 丢弃。 + +### LEFT JOIN 示例 + +LEFT JOIN 会保留所有 source 事件，维表中没有匹配时，维表列填充为 `NULL`： + +```sql +CREATE PIPELINE p_lookup_left +SINK TO sink_t +AS +SELECT + e.ts, + e.order_id, + e.customer_id, + e.product, + e.quantity, + e.price, + c.customer_name, + c.tier +FROM src_events e +LEFT JOIN dim_customers c +ON e.customer_id = c.customer_id; +``` + +查询结果： + +```sql +SELECT order_id, customer_id, customer_name, tier FROM sink_t ORDER BY order_id; +``` + +```text ++---------+-------------+---------------+-------+ +| order_id| customer_id | customer_name | tier | ++---------+-------------+---------------+-------+ +| ord-1 | cust-a | Alice | Gold | +| ord-2 | cust-b | Bob | Silver| +| ord-3 | cust-a | Alice | Gold | +| ord-4 | cust-c | | | ++---------+-------------+---------------+-------+ +``` + +`ord-4`（`cust-c`）被保留，但 `customer_name` 和 `tier` 为空（NULL）。 + +### 清理 + +```sql +ALTER PIPELINE p_lookup_inner STOP; +DROP PIPELINE p_lookup_inner; +ALTER PIPELINE p_lookup_left STOP; +DROP PIPELINE p_lookup_left; +TRUNCATE TABLE sink_t; +``` + +--- + +## 限制与后续规划 + +| 能力 | 当前状态 | 后续规划 | +| --- | --- | --- | +| Relational 维表 | 不支持 | 后续版本支持 | +| 外部维表（MySQL、Redis 等） | 不支持 | 后续版本通过 `CREATE SOURCE ... WITH (lookup='true')` 支持 | +| `RIGHT JOIN` / `FULL OUTER JOIN` | 不支持 | 评估中 | +| 非等值 JOIN | 不支持 | 评估中 | +| CDC 维表更新 | 不支持 | 后续通过 TTL 或 CDC 管道支持 | +| Lookup Cache | 未启用 | 后续版本开启，减少重复查询 | From 7f4a6c89c590bfd2489b9dea90734ab091752375 Mon Sep 17 00:00:00 2001 From: niebayes Date: Thu, 28 May 2026 10:35:22 +0000 Subject: [PATCH 2/2] fix link --- en_US/streaming/computation.md | 2 +- zh_CN/streaming/computation.md | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/en_US/streaming/computation.md b/en_US/streaming/computation.md index 9b31c849..78940d5c 100644 --- a/en_US/streaming/computation.md +++ b/en_US/streaming/computation.md @@ -19,7 +19,7 @@ This document introduces the computation capabilities currently supported by Dat ## Environment Setup -The following examples are based on a Kafka source. Please complete the basic environment setup described in [Quick Start](/streaming/quick-start), including: +The following examples are based on a Kafka source. Please complete the basic environment setup described in [Quick Start](./quick-start.md), including: 1. **Start Kafka** diff --git a/zh_CN/streaming/computation.md b/zh_CN/streaming/computation.md index 4909c638..b90de58a 100644 --- a/zh_CN/streaming/computation.md +++ b/zh_CN/streaming/computation.md @@ -19,7 +19,7 @@ description: "介绍 Datalayers 流计算支持的计算功能，包括投影、 ## 环境准备 -以下示例均基于 Kafka source。请确保已完成 [快速开始](/streaming/quick-start) 中的基础环境准备，包括： +以下示例均基于 Kafka source。请确保已完成 [快速开始](./quick-start.md) 中的基础环境准备，包括： 1. **启动 Kafka**