Add a best practice for checkpointing.

docs/source/user_guide/best_practices.rst

@@ -69,6 +69,43 @@ Even though Koalas tries its best to optimize and reduce such shuffle operations
 optimizers, it is best to avoid shuffling in the application side whenever possible.
 
 
+Use checkpoint
+--------------
+
+After a bunch of operations on Koalas objects, the underlying Spark planner can slow down due to the huge and complex plan.
+If the Spark plan becomes huge or it takes the planning long time, ``DataFrame.spark.checkpoint()``
+or ``DataFrame.spark.local_checkpoint()`` would be helpful.
+
+.. code-block:: python
+
+   >>> import databricks.koalas as ks
+   >>> kdf = ks.DataFrame({'id': range(10)})
+   >>> kdf = kdf[kdf.id > 5]
+   >>> kdf['id'] = kdf['id'] + (10 * kdf['id'] + kdf['id'])
+   >>> kdf = kdf.groupby('id').head(2)
+   >>> kdf.spark.explain()
+   == Physical Plan ==
+   *(3) Project [__index_level_0__#0L, id#31L]
+   +- *(3) Filter (isnotnull(__row_number__#44) AND (__row_number__#44 <= 2))
+      +- Window [row_number() windowspecdefinition(__groupkey_0__#36L, __natural_order__#16L ASC NULLS FIRST, specifiedwindowframe(RowFrame, unboundedpreceding$(), currentrow$())) AS __row_number__#44], [__groupkey_0__#36L], [__natural_order__#16L ASC NULLS FIRST]
+         +- *(2) Sort [__groupkey_0__#36L ASC NULLS FIRST, __natural_order__#16L ASC NULLS FIRST], false, 0
+            +- Exchange hashpartitioning(__groupkey_0__#36L, 200), true, [id=#33]
+               +- *(1) Project [__index_level_0__#0L, (id#1L + ((id#1L * 10) + id#1L)) AS __groupkey_0__#36L, (id#1L + ((id#1L * 10) + id#1L)) AS id#31L, __natural_order__#16L]
+                  +- *(1) Project [__index_level_0__#0L, id#1L, monotonically_increasing_id() AS __natural_order__#16L]
+                     +- *(1) Filter (id#1L > 5)
+                        +- *(1) Scan ExistingRDD[__index_level_0__#0L,id#1L]
+
+   >>> kdf = kdf.spark.local_checkpoint()  # or kdf.spark.checkpoint()
+   >>> kdf.spark.explain()
+   == Physical Plan ==
+   *(1) Project [__index_level_0__#0L, id#31L]
+   +- *(1) Scan ExistingRDD[__index_level_0__#0L,id#31L,__natural_order__#59L]
+
+As you can see, the previous Spark plan is dropped and starts with a simple plan.
+The result of the previous DataFrame is stored in the configured file system when calling ``DataFrame.spark.checkpoint()``,
+or in the executor when calling ``DataFrame.spark.local_checkpoint()``.
+
+
 Avoid shuffling
 ---------------
 
@@ -173,6 +210,7 @@ the index column. Specify the index column whenever possible.
 
 See  `working with PySpark <pandas_pyspark.rst#pyspark>`_
 
+
 Use ``distributed`` or ``distributed-sequence`` default index
 -------------------------------------------------------------