a few minor adjustments

Author: xuyangzhong

website/docs/engine-flink/deltajoins.md

@@ -8,14 +8,14 @@ sidebar_position: 6
 Beginning with **Apache Flink 2.1**, a new operator called **Delta Join** was introduced.  
 Compared to traditional streaming joins, the delta join operator significantly reduces the amount of state that needs to be maintained during execution. This improvement helps mitigate several common issues associated with large state sizes, including:
 
-- Excessive memory and storage consumption
+- Excessive computing resource and storage consumption
 - Long checkpointing durations
 - Extended recovery times after failures or restarts
 
 Starting with **Apache Fluss 0.8**, streaming join jobs running on **Flink 2.1 or later** will be automatically optimized into **delta joins** whenever applicable. This optimization happens transparently at query planning time, requiring no manual configuration.
 
 ## How Delta Join Works
-Traditional streaming joins in Flink require maintaining both input sides entirely in state to match updates across streams. Delta join, by contrast, uses a **prefix-based lookup mechanism** that only retains *relevant subsets* of one table’s data in state. This drastically reduces memory pressure and improves performance for many streaming analytics and enrichment workloads.
+Traditional streaming joins in Flink require maintaining both input sides entirely in state to match records across streams. Delta join, by contrast, uses a **prefix-based lookup mechanism** to transform the behavior of querying data from the state into querying data from the Fluss source table, thereby avoiding redundant storage of the same data in both the Fluss source table and the state. This drastically reduces state size and improves performance for many streaming analytics and enrichment workloads.
 
 ## Example: Delta Join in Flink 2.1
 
@@ -43,7 +43,7 @@ CREATE TABLE `fluss_catalog`.`my_db`.`left_src` (
   `content` VARCHAR NOT NULL,
   PRIMARY KEY (city_id, order_id) NOT ENFORCED
 ) WITH (
-    -- prefix lookup key
+    -- prefix key
     'bucket.key' = 'city_id',
     -- in Flink 2.1, delta join only support append-only source
     'table.merge-engine' = 'first_row'
@@ -83,9 +83,8 @@ Join `fluss_catalog`.`my_db`.`right_src` T2
 ON T1.`city_id` = T2.`city_id`;
 ```
 
-If the physical plan includes `DeltaJoin`, it indicates that the optimizer has successfully transformed the traditional streaming join into a delta join.
+If the printed plan includes `DeltaJoin` as shown below, it indicates that the optimizer has successfully transformed the traditional streaming join into a delta join.
 
-### Example Optimized Execution Plan
 ```title="Flink Plan"
 == Abstract Syntax Tree ==
 LogicalSink(table=[fluss_catalog.my_db.snk], fields=[city_id, order_id, content, city_name])
@@ -112,7 +111,6 @@ Sink(table=[fluss_catalog.my_db.snk], fields=[city_id, order_id, content, city_n
       +- Exchange(distribution=[hash[city_id]])
          +- TableSourceScan(table=[[fluss_catalog, my_db, right_src]], fields=[city_id, city_name])
 ```
-This confirms that the delta join optimization is active.
 
 ## Understanding Prefix Keys
 A prefix key defines the portion of a table’s primary key that can be used for efficient key-based lookups or index pruning.
@@ -125,9 +123,9 @@ For example:
 
 In this setup:
 * The delta join operator uses the prefix key (`city_id`) to retrieve only relevant right-side records matching each left-side event. 
-* This eliminates the need to hold all records for every city in memory, significantly reducing state size.
+* This eliminates the need to hold all records for every city in Flink state, significantly reducing state size.
 
-Prefix keys thus form the foundation for state-efficient lookups in delta joins, enabling Flink to scale join workloads efficiently even under high throughput.
+Prefix keys thus form the foundation for efficient lookups in delta joins, enabling Flink to scale join workloads efficiently even under high throughput.
 
 ## Flink Version Support
 
@@ -144,9 +142,9 @@ Refer to the [Delta Join](https://issues.apache.org/jira/browse/FLINK-37836) for
 
 #### Limitations
 
-- The primary key or the prefix lookup key of the tables must be included as part of the equivalence conditions in the join.
+- The primary key or the prefix key of the tables must be included as part of the equivalence conditions in the join.
 - The join must be a INNER join.
-- The downstream nodes of the join can accept duplicate changes, such as a sink that provides UPSERT mode.
+- The downstream nodes of the join can accept duplicate changes, such as a sink that provides UPSERT mode without `upsertMaterialize`.
 - All join inputs should be INSERT-ONLY streams.
   - This is why the option `'table.merge-engine' = 'first_row'` is added to the source table DDL.
 - All upstream nodes of the join should be `TableSourceScan` or `Exchange`.