What are the differences between saveAsTable and insertInto in different SaveMode(s)?
DISCLAIMER I've been exploring insertInto
for some time and although I'm far from an expert in this area I'm sharing the findings for greater good.
Does
insertInto
always expect the table to exist?
Yes (per the table name and the database).
Moreover not all tables can be inserted into, i.e. a (permanent) table, a temporary view or a temporary global view are fine, but not:
a bucketed table
an RDD-based table
Do SaveModes have any impact on insertInto?
(That's recently been my question, too!)
Yes, but only SaveMode.Overwrite. After you think about insertInto
the other 3 save modes don't make much sense (as it simply inserts a dataset).
what's the differences between saveAsTable with SaveMode.Append and insertInto given that table already exists?
That's a very good question! I'd say none, but let's see by just one example (hoping that proves something).
scala> spark.version
res13: String = 2.4.0-SNAPSHOT
sql("create table my_table (id long)")
scala> spark.range(3).write.mode("append").saveAsTable("my_table")
org.apache.spark.sql.AnalysisException: The format of the existing table default.my_table is `HiveFileFormat`. It doesn't match the specified format `ParquetFileFormat`.;
at org.apache.spark.sql.execution.datasources.PreprocessTableCreation$$anonfun$apply$2.applyOrElse(rules.scala:117)
at org.apache.spark.sql.execution.datasources.PreprocessTableCreation$$anonfun$apply$2.applyOrElse(rules.scala:76)
...
scala> spark.range(3).write.insertInto("my_table")
scala> spark.table("my_table").show
+---+
| id|
+---+
| 2|
| 0|
| 1|
+---+
does insertInto with SaveMode.Overwrite make any sense?
I think so given it pays so much attention to SaveMode.Overwrite
. It simply re-creates the target table.
spark.range(3).write.mode("overwrite").insertInto("my_table")
scala> spark.table("my_table").show
+---+
| id|
+---+
| 1|
| 0|
| 2|
+---+
Seq(100, 200, 300).toDF.write.mode("overwrite").insertInto("my_table")
scala> spark.table("my_table").show
+---+
| id|
+---+
|200|
|100|
|300|
+---+
I want to point out a major difference between SaveAsTable
and insertInto
in SPARK.
In partitioned table overwrite
SaveMode work differently in case of SaveAsTable
and insertInto
.
Consider below example.Where I am creating partitioned table using SaveAsTable
method.
hive> CREATE TABLE `db.companies_table`(`company` string) PARTITIONED BY ( `id` date);
OK
Time taken: 0.094 seconds
import org.apache.spark.sql._*
import spark.implicits._
import org.apache.spark.sql._
scala>val targetTable = "db.companies_table"
scala>val companiesDF = Seq(("2020-01-01", "Company1"), ("2020-01-02", "Company2")).toDF("id", "company")
scala>companiesDF.write.mode(SaveMode.Overwrite).partitionBy("id").saveAsTable(targetTable)
scala> spark.sql("select * from db.companies_table").show()
+--------+----------+
| company| id|
+--------+----------+
|Company1|2020-01-01|
|Company2|2020-01-02|
+--------+----------+
Now I am adding 2 new rows with 2 new partition values.
scala> val companiesDF = Seq(("2020-01-03", "Company1"), ("2020-01-04", "Company2")).toDF("id", "company")
scala> companiesDF.write.mode(SaveMode.Append).partitionBy("id").saveAsTable(targetTable)
scala>spark.sql("select * from db.companies_table").show()
+--------+----------+
| company| id|
+--------+----------+
|Company1|2020-01-01|
|Company2|2020-01-02|
|Company1|2020-01-03|
|Company2|2020-01-04|
+--------+----------+
As you can see 2 new rows are added to the table.
Now let`s say i want to Overwrite
partition 2020-01-02 data.
scala> val companiesDF = Seq(("2020-01-02", "Company5")).toDF("id", "company")
scala>companiesDF.write.mode(SaveMode.Overwrite).partitionBy("id").saveAsTable(targetTable)
As per our logic only partitions 2020-01-02 should be overwritten but the case with SaveAsTable
is different.It will overwrite the enter table as you can see below.
scala> spark.sql("select * from db.companies_table").show()
+-------+----------+
|company| id|
+-------+----------+
|Company5|2020-01-02|
+-------+----------+
So if we want to overwrite only certain partitions in the table using SaveAsTable
its not possible.
Refer this Link for more details. https://towardsdatascience.com/understanding-the-spark-insertinto-function-1870175c3ee9