[
https://issues.apache.org/jira/browse/SPARK-20895?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16025976#comment-16025976
]
Takeshi Yamamuro commented on SPARK-20895:
------------------------------------------
At least, I feel we could get performance improvements based on this when
optimizer takes much time described in the description.
> Support fast execution based on an optimized plan and parameter placeholders
> ----------------------------------------------------------------------------
>
> Key: SPARK-20895
> URL: https://issues.apache.org/jira/browse/SPARK-20895
> Project: Spark
> Issue Type: Improvement
> Components: SQL
> Affects Versions: 2.1.1
> Reporter: Takeshi Yamamuro
> Priority: Minor
>
> In database scenarios, users sometimes use parameterized queries for repeated
> execution (e.g., by using prepared statements).
> So, I think this functionality is also useful for Spark users.
> What I suggest here seems to be like:
> My prototype here:
> https://github.com/apache/spark/compare/master...maropu:PreparedStmt2
> {code}
> scala> Seq((1, 2), (2, 3)).toDF("col1", "col2").createOrReplaceTempView("t")
> // Define a query with a parameter placeholder named `val`
> scala> val df = sql("SELECT * FROM t WHERE col1 = $val")
> scala> df.explain
> == Physical Plan ==
> *Project [_1#13 AS col1#16, _2#14 AS col2#17]
> +- *Filter (_1#13 = cast(parameterholder(val) as int))
> +- LocalTableScan [_1#13, _2#14]
> // Apply optimizer rules and get an optimized logical plan with the parameter
> placeholder
> scala> val preparedDf = df.prepared
> // Bind an actual value and do execution
> scala> preparedDf.bindParam("val", 1).show()
> +----+----+
> |col1|col2|
> +----+----+
> | 1| 2|
> +----+----+
> {code}
> To implement this, my prototype adds a new expression leaf node named
> `ParameterHolder`.
> In a binding phase, this node is replaced with `Literal` including an actual
> value by using `bindParam`.
> Currently, Spark sometimes consumes much time to rewrite logical plans in
> `Optimizer` (e.g. constant propagation desribed in SPARK-19846).
> So, I feel this approach is also helpful in that case:
> {code}
> def timer[R](f: => {}): Unit = {
> val count = 9
> val iters = (0 until count).map { i =>
> val t0 = System.nanoTime()
> f
> val t1 = System.nanoTime()
> val elapsed = t1 - t0 + 0.0
> println(s"#$i: ${elapsed / 1000000000.0}")
> elapsed
> }
> println("Avg. Elapsed Time: " + ((iters.sum / count) / 1000000000.0) + "s")
> }
> import org.apache.spark.sql.Row
> import org.apache.spark.sql.types._
> val numCols = 50
> val df = spark.range(100).selectExpr((0 until numCols).map(i => s"id AS
> _c$i"): _*)
> // Add conditions to take much time in Optimizer
> val filter = (0 until 128).foldLeft(lit(false))((e, i) =>
> e.or(df.col(df.columns(i % numCols)) === (rand() * 10).cast("int")))
> val df2 = df.filter(filter).sort(df.columns(0))
> // Regular path
> timer {
> df2.filter(df2.col(df2.columns(0)) === lit(3)).collect
> df2.filter(df2.col(df2.columns(0)) === lit(4)).collect
> df2.filter(df2.col(df2.columns(0)) === lit(5)).collect
> df2.filter(df2.col(df2.columns(0)) === lit(6)).collect
> df2.filter(df2.col(df2.columns(0)) === lit(7)).collect
> df2.filter(df2.col(df2.columns(0)) === lit(8)).collect
> }
> #0: 24.178487906
> #1: 22.619839888
> #2: 22.318617035
> #3: 22.131305502
> #4: 22.532095611
> #5: 22.245152778
> #6: 22.314114847
> #7: 22.284385952
> #8: 22.053593855
> Avg. Elapsed Time: 22.519732597111112s
> // Prepared path
> val df3b = df2.filter(df2.col(df2.columns(0)) === param("val")).prepared
> timer {
> df3b.bindParam("val", 3).collect
> df3b.bindParam("val", 4).collect
> df3b.bindParam("val", 5).collect
> df3b.bindParam("val", 6).collect
> df3b.bindParam("val", 7).collect
> df3b.bindParam("val", 8).collect
> }
> #0: 0.744693912
> #1: 0.743187129
> #2: 0.745100003
> #3: 0.721668718
> #4: 0.757573342
> #5: 0.763240883
> #6: 0.731287275
> #7: 0.728740601
> #8: 0.674275592
> Avg. Elapsed Time: 0.7344186061111112s
> {code}
> I'm not sure this approach is acceptable, so welcome any suggestion and
> advice.
--
This message was sent by Atlassian JIRA
(v6.3.15#6346)
---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org
For additional commands, e-mail: issues-h...@spark.apache.org
