[doc] Introduce Full Compaction in compaction doc

docs/content/primary-key-table/compaction.md

@@ -79,6 +79,19 @@ In compaction, you can configure record-Level expire time to expire records, you
 
 Expiration happens in compaction, and there is no strong guarantee to expire records in time.
 
+## Full Compaction
+
+Paimon Compaction uses [Universal-Compaction](https://github.com/facebook/rocksdb/wiki/Universal-Compaction).
+By default, when there is too much incremental data, Full Compaction will be automatically performed. You don't usually
+have to worry about it.
+
+Paimon also provides a configuration that allows for regular execution of Full Compaction.
+
+1. 'compaction.optimization-interval': Implying how often to perform an optimization full compaction, this
+    configuration is used to ensure the query timeliness of the read-optimized system table.
+2. 'full-compaction.delta-commits': Full compaction will be constantly triggered after delta commits. its disadvantage
+    is that it can only perform compaction synchronously, which will affect writing efficiency.
+
 ## Compaction Options
 
 ### Number of Sorted Runs to Pause Writing

docs/content/primary-key-table/table-mode.md

@@ -109,10 +109,7 @@ So by default, compaction is synchronous, and if asynchronous is turned on, ther
 If you don't want to use Deletion Vectors mode, you want to query fast enough in MOR mode, but can only find
 older data, you can also:
 
-1. Configure 'compaction.optimization-interval' when writing data. For streaming jobs, optimized compaction will then
-   be performed periodically; For batch jobs, optimized compaction will be carried out when the job ends. (Or configure
-   `'full-compaction.delta-commits'`, its disadvantage is that it can only perform compaction synchronously, which will
-   affect writing efficiency)
+1. Configure 'compaction.optimization-interval' when writing data.
 2. Query from [read-optimized system table]({{< ref "maintenance/system-tables#read-optimized-table" >}}). Reading from
    results of optimized files avoids merging records with the same key, thus improving reading performance.