diff --git a/README.md b/README.md
index 0a90a3137..20b0a6f55 100644
--- a/README.md
+++ b/README.md
@@ -101,7 +101,7 @@ orders.createOrReplaceTempView("orders")
 spark.sql("select * from orders where o_orderdate > date '1998-07-26'").show(20000, false)
 ```
 
-The result should showup on Spark console and you can check the DAG diagram with some Columnar Processing stage.
+The result should showup on Spark console and you can check the DAG diagram with some Columnar Processing stage. Native SQL engine still lacks some features, please check out the [limitations](./docs/limitations.md).
 
 
 ## Performance data
diff --git a/docs/limitation.md b/docs/limitation.md
index 92887d4ff..a4b66f5e1 100644
--- a/docs/limitation.md
+++ b/docs/limitation.md
@@ -4,11 +4,13 @@
 Native SQL engine currenlty works with Spark 3.0.0 only. There are still some trouble with latest Shuffle/AQE API from Spark 3.0.1, 3.0.2 or 3.1.x.
 
 ## Operator limitations
+All performance critical operators in TPC-H/TPC-DS should be supported. For those unsupported operators, Native SQL engine will automatically fallback to row operators in vanilla Spark.
+
 ### Columnar Projection with Filter
 We used 16 bit selection vector for filter so the max batch size need to be < 65536
 
 ### Columnar Sort
-To reduce the peak memory usage, we used smaller data structure(uin16_t). This limits 
+Columnar Sort does not support spill to disk yet. To reduce the peak memory usage, we used smaller data structure(uin16_t), so this limits 
 - the max batch size to be < 65536
 - the number of batches in one partiton to be < 65536