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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