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From Shuyi Chen <suez1...@gmail.com>
Subject Re: Array element access and nested rows
Date Thu, 22 Mar 2018 08:18:19 GMT
Also, you can try to patch in this PR to see if that fixes your issue,
https://github.com/apache/calcite/pull/651.

On Thu, Mar 22, 2018 at 12:14 AM, Shuyi Chen <suez1224@gmail.com> wrote:

> I think the following is what happened:
>
> Calcite is trying to remove all structured type in the plan right below,
> so optimizer and codegen rules never have to deal with structured types.
>
> LogicalProject(EXPR$0=[ITEM($3, 1)])
>   LogicalTableScan(table=[[CATALOG, SALES, DEPT_NESTED]])
>
> First, it flatten the LogicalTableScan, and generate the following:
>
> LogicalProject(DEPTNO=[$0], NAME=[$1], TYPE=[$2.TYPE], DESC=[$2.DESC],
> EMPLOYEES=[$3])
>   LogicalTableScan(table=[[CATALOG, SALES, DEPT_NESTED]])
>
> Then it tries to flatten "LogicalProject(EXPR$0=[ITEM($3, 1)])", and
> generate the following:
>
> LogicalProject(EXPR$0$0=[ITEM($3, 1).EMPNO], EXPR$0$1=[ITEM($3,
> 1).ENAME], EXPR$0$2=[ITEM($3, 1).SKILLS])
>
> However, when it combines the 2 flattening results, it did not correctly
> adjust the ordinal post-flattening, which should be $4 now, not $3. So this
> cause the exception since it is a type mismatch.
>
> I think I've already developed a fix for this. Will create a PR to address
> both issues.
>
> @Anton, although my test error and your issue look similar, I still can't
> reproduce your case (mine throws an error). Can you create a test for it?
> Thanks a lot.
>
>
>
>
>
> On Wed, Mar 21, 2018 at 9:31 PM, Anton Kedin <kedin@google.com.invalid>
> wrote:
>
>> Shuyi,
>>
>> Thank you for looking into this. Can this error in your case be caused by
>> a
>> similar problem? E.g. SKILLRECORD gets flattened, then when you try to
>> select employees[1] you get SKILLRECORD.DESC field instead of actual
>> employees[1] because input ref index is not adjusted for the flattened
>> SKILLRECORD?
>>
>> Thank you,
>> Anton
>>
>>
>> On Wed, Mar 21, 2018 at 8:53 PM Shuyi Chen <suez1224@gmail.com> wrote:
>>
>> > Actually, the cause for my previous findings is: for the first case,
>> > SqlToRelConverterTest introduce another LogicalProject (RelRoot.project)
>> > after applying the SqlToRelConverter to remove fields that are not
>> needed.
>> > But this function does not work with Record type and flattened fields.
>> It
>> > simply projects the first several fields from input index-wise, and does
>> > not take into account the flattening behavior. The second case does not
>> > trigger the extra project because it's trivial.
>> >
>> > For your case, I tried below:
>> >
>> > MockTable deptNestedTable =
>> >     MockTable.create(this, salesSchema, "DEPT_NESTED", false, 4);
>> > deptNestedTable.addColumn("DEPTNO", f.intType, true);
>> > deptNestedTable.addColumn("NAME", f.varchar10Type);
>> > deptNestedTable.addColumn("SKILLRECORD", f.skillRecordType);
>> > deptNestedTable.addColumn("EMPLOYEES", f.empListType);
>> > registerTable(deptNestedTable);
>> >
>> > Run the following test:
>> >
>> > @Test public void testArrayOfRecord() {
>> >   sql("select employees[1] from dept_nested").ok();
>> > }
>> >
>> > I am actually getting the following error when run:
>> >
>> > java.lang.AssertionError: type mismatch:
>> > ref:
>> > RecordType(INTEGER NOT NULL EMPNO, VARCHAR(10) CHARACTER SET
>> "ISO-8859-1"
>> > COLLATE "ISO-8859-1$en_US$primary" NOT NULL ENAME,
>> RecordType(VARCHAR(10)
>> > CHARACTER SET "ISO-8859-1" COLLATE "ISO-8859-1$en_US$primary" NOT NULL
>> > TYPE, VARCHAR(20) CHARACTER SET "ISO-8859-1" COLLATE
>> > "ISO-8859-1$en_US$primary" NOT NULL DESC) NOT NULL ARRAY NOT NULL
>> SKILLS)
>> > NOT NULL ARRAY NOT NULL
>> > input:
>> > VARCHAR(20) CHARACTER SET "ISO-8859-1" COLLATE
>> "ISO-8859-1$en_US$primary"
>> > NOT NULL
>> >
>> > at org.apache.calcite.util.Litmus$1.fail(Litmus.java:31)
>> > at org.apache.calcite.plan.RelOptUtil.eq(RelOptUtil.java:1838)
>> > at org.apache.calcite.rex.RexChecker.visitInputRef(RexChecker.java:125)
>> > at org.apache.calcite.rex.RexChecker.visitInputRef(RexChecker.java:57)
>> > at org.apache.calcite.rex.RexInputRef.accept(RexInputRef.java:112)
>> > at org.apache.calcite.rex.RexChecker.visitCall(RexChecker.java:140)
>> > at org.apache.calcite.rex.RexChecker.visitCall(RexChecker.java:57)
>> > at org.apache.calcite.rex.RexCall.accept(RexCall.java:107)
>> > at
>> >
>> > org.apache.calcite.rex.RexVisitorImpl.visitFieldAccess(RexVi
>> sitorImpl.java:98)
>> > at org.apache.calcite.rex.RexChecker.visitFieldAccess(RexChecke
>> r.java:149)
>> > at org.apache.calcite.rex.RexChecker.visitFieldAccess(RexChecke
>> r.java:57)
>> > at org.apache.calcite.rex.RexFieldAccess.accept(RexFieldAccess.java:81)
>> > at org.apache.calcite.rel.core.Project.isValid(Project.java:187)
>> > at org.apache.calcite.rel.core.Project.<init>(Project.java:84)
>> > at
>> >
>> > org.apache.calcite.rel.logical.LogicalProject.<init>(Logical
>> Project.java:65)
>> > at
>> >
>> > org.apache.calcite.rel.logical.LogicalProject.create(Logical
>> Project.java:120)
>> > at
>> >
>> > org.apache.calcite.rel.logical.LogicalProject.create(Logical
>> Project.java:103)
>> > at
>> >
>> > org.apache.calcite.rel.core.RelFactories$ProjectFactoryImpl.
>> createProject(RelFactories.java:127)
>> > at org.apache.calcite.tools.RelBuilder.project(RelBuilder.java:1064)
>> > at org.apache.calcite.plan.RelOptUtil.createProject(RelOptUtil.
>> java:2956)
>> > at org.apache.calcite.plan.RelOptUtil.createProject(RelOptUtil.
>> java:2873)
>> > at
>> >
>> > org.apache.calcite.sql2rel.RelStructuredTypeFlattener.rewrit
>> eRel(RelStructuredTypeFlattener.java:477)
>> > at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
>> > at
>> >
>> > sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAcce
>> ssorImpl.java:62)
>> > at
>> >
>> > sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMe
>> thodAccessorImpl.java:43)
>> > at java.lang.reflect.Method.invoke(Method.java:498)
>> > at
>> >
>> > org.apache.calcite.util.ReflectUtil.invokeVisitorInternal(Re
>> flectUtil.java:257)
>> > at org.apache.calcite.util.ReflectUtil.invokeVisitor(ReflectUti
>> l.java:214)
>> > at
>> > org.apache.calcite.util.ReflectUtil$1.invokeVisitor(ReflectU
>> til.java:464)
>> > at
>> >
>> > org.apache.calcite.sql2rel.RelStructuredTypeFlattener$Rewrit
>> eRelVisitor.visit(RelStructuredTypeFlattener.java:721)
>> > at
>> >
>> > org.apache.calcite.sql2rel.RelStructuredTypeFlattener.rewrit
>> e(RelStructuredTypeFlattener.java:177)
>> > at
>> >
>> > org.apache.calcite.sql2rel.SqlToRelConverter.flattenTypes(Sq
>> lToRelConverter.java:462)
>> > at
>> >
>> > org.apache.calcite.test.SqlToRelTestBase$TesterImpl.convertS
>> qlToRel(SqlToRelTestBase.java:585)
>> > at
>> >
>> > org.apache.calcite.test.SqlToRelTestBase$TesterImpl.assertCo
>> nvertsTo(SqlToRelTestBase.java:690)
>> > at
>> >
>> > org.apache.calcite.test.SqlToRelConverterTest$Sql.convertsTo
>> (SqlToRelConverterTest.java:2784)
>> > at
>> >
>> > org.apache.calcite.test.SqlToRelConverterTest$Sql.ok(SqlToRe
>> lConverterTest.java:2776)
>> > at
>> >
>> > org.apache.calcite.test.SqlToRelConverterTest.testArrayOfRec
>> ord(SqlToRelConverterTest.java:1059)
>> > at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
>> > at
>> >
>> > sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAcce
>> ssorImpl.java:62)
>> > at
>> >
>> > sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMe
>> thodAccessorImpl.java:43)
>> > at java.lang.reflect.Method.invoke(Method.java:498)
>> > at
>> >
>> > org.junit.runners.model.FrameworkMethod$1.runReflectiveCall(
>> FrameworkMethod.java:50)
>> > at
>> >
>> > org.junit.internal.runners.model.ReflectiveCallable.run(Refl
>> ectiveCallable.java:12)
>> > at
>> >
>> > org.junit.runners.model.FrameworkMethod.invokeExplosively(Fr
>> ameworkMethod.java:47)
>> > at
>> >
>> > org.junit.internal.runners.statements.InvokeMethod.evaluate(
>> InvokeMethod.java:17)
>> > at org.junit.runners.ParentRunner.runLeaf(ParentRunner.java:325)
>> > at
>> >
>> > org.junit.runners.BlockJUnit4ClassRunner.runChild(BlockJUnit
>> 4ClassRunner.java:78)
>> > at
>> >
>> > org.junit.runners.BlockJUnit4ClassRunner.runChild(BlockJUnit
>> 4ClassRunner.java:57)
>> > at org.junit.runners.ParentRunner$3.run(ParentRunner.java:290)
>> > at org.junit.runners.ParentRunner$1.schedule(ParentRunner.java:71)
>> > at org.junit.runners.ParentRunner.runChildren(ParentRunner.java:288)
>> > at org.junit.runners.ParentRunner.access$000(ParentRunner.java:58)
>> > at org.junit.runners.ParentRunner$2.evaluate(ParentRunner.java:268)
>> > at org.junit.runners.ParentRunner.run(ParentRunner.java:363)
>> > at org.junit.runner.JUnitCore.run(JUnitCore.java:137)
>> > at
>> >
>> > com.intellij.junit4.JUnit4IdeaTestRunner.startRunnerWithArgs
>> (JUnit4IdeaTestRunner.java:117)
>> > at
>> >
>> > com.intellij.junit4.JUnit4IdeaTestRunner.startRunnerWithArgs
>> (JUnit4IdeaTestRunner.java:42)
>> > at
>> >
>> > com.intellij.rt.execution.junit.JUnitStarter.prepareStreamsA
>> ndStart(JUnitStarter.java:262)
>> > at com.intellij.rt.execution.junit.JUnitStarter.main(JUnitStart
>> er.java:84)
>> > at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
>> > at
>> >
>> > sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAcce
>> ssorImpl.java:62)
>> > at
>> >
>> > sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMe
>> thodAccessorImpl.java:43)
>> > at java.lang.reflect.Method.invoke(Method.java:498)
>> > at com.intellij.rt.execution.application.AppMain.main(AppMain.java:147)
>> >
>> > Shuyi
>> >
>> >
>> > On Wed, Mar 21, 2018 at 6:09 PM, Shuyi Chen <suez1224@gmail.com> wrote:
>> >
>> > > Thanks a lot, Anton. This seems to be a bug in Calcite. When the
>> > statement
>> > > involving record types, sql validation seems to work, but the rel plan
>> > > generated might be wrong.  I can also reproduce your case:
>> > >
>> > > MockTable deptNestedTable =
>> > >     MockTable.create(this, salesSchema, "DEPT_NESTED", false, 4);
>> > > deptNestedTable.addColumn("DEPTNO", f.intType, true);
>> > > deptNestedTable.addColumn("NAME", f.varchar10Type);
>> > > deptNestedTable.addColumn("SKILLRECORD", f.skillRecordType);
>> > > deptNestedTable.addColumn("EMPLOYEES", f.empListType);
>> > > registerTable(deptNestedTable);
>> > >
>> > > Run the following test:
>> > >
>> > > @Test public void testArrayOfRecord() {
>> > >   sql("select skillrecord, employees from dept_nested").ok();
>> > > }
>> > >
>> > > yield:
>> > > LogicalProject(SKILLRECORD=[$0], EMPLOYEES=[$1])
>> > >   LogicalProject(SKILLRECORD=[$2], SKILLRECORD1=[$3], EMPLOYEES=[$4])
>> > >     LogicalProject(DEPTNO=[$0], NAME=[$1], TYPE=[$2.TYPE],
>> > DESC=[$2.DESC],
>> > > EMPLOYEES=[$3])
>> > >       LogicalTableScan(table=[[CATALOG, SALES, DEPT_NESTED]])
>> > >
>> > > Sometimes, it works:
>> > >
>> > > @Test public void testArrayOfRecord() {
>> > > sql("select name, employees from dept_nested").ok();
>> > > }
>> > >
>> > > yield:
>> > >
>> > > LogicalProject(NAME=[$1], EMPLOYEES=[$4])
>> > >   LogicalProject(DEPTNO=[$0], NAME=[$1], TYPE=[$2.TYPE],
>> DESC=[$2.DESC],
>> > > EMPLOYEES=[$3])
>> > >     LogicalTableScan(table=[[CATALOG, SALES, DEPT_NESTED]])
>> > >
>> > > I can take a deeper look.
>> > >
>> > > Shuyi
>> > >
>> > > On Wed, Mar 21, 2018 at 11:06 AM, Anton Kedin
>> <kedin@google.com.invalid>
>> > > wrote:
>> > >
>> > >> Hi,
>> > >>
>> > >> I have an issue I am not sure how to handle, would appreciate any
>> > >> pointers.
>> > >>
>> > >> I have a table with row type:
>> > >> RecordType(
>> > >>     INTEGER orderId,
>> > >>     RecordType(VARCHAR name, INTEGER personId)
>> > >>         person,
>> > >>     RecordType(VARCHAR sku, INTEGER price, VARCHAR currency, VARCHAR
>> > ARRAY
>> > >> tags)
>> > >>         ARRAY items
>> > >> )
>> > >>
>> > >> With this row type I am trying to model a JSON object which looks
>> like
>> > >> this:
>> > >> { "orderId" : 1,
>> > >>   "person" : { "name" : "john", "personId" : 12, },
>> > >>   "items": [
>> > >>     { "sku" : "aaa01", "price" : 12, "currency" : "USD", "tags" :
>> > ["blue",
>> > >> "book"] }
>> > >>   ]}
>> > >>
>> > >> When selecting the whole items array I get the following plan:
>> > >> SELECT items FROM PCOLLECTION
>> > >>
>> > >> LogicalProject(items=[$3])
>> > >>   LogicalProject(orderId=[$0], name=[$1.name],
>> personId=[$1.personId],
>> > >> items
>> > >> =[$2])
>> > >>     LogicalTableScan(table=[[PCOLLECTION]])
>> > >>
>> > >> Which looks correct and it works. One thing to note here is that
>> Calcite
>> > >> flattens the person row, and makes the input ref for the items field
>> as
>> > >> $3,
>> > >> as expected.
>> > >>
>> > >> But when I want to get a specific element from that array I get the
>> > >> following:
>> > >> SELECT items[0] FROM PCOLLECTION
>> > >>
>> > >> LogicalProject(EXPR$0$0=[ITEM($2, 0).sku], EXPR$0$1=[ITEM($2,
>> 0).price],
>> > >> EXPR$0$2=[ITEM($2, 0).currency], EXPR$0$3=[ITEM($2, 0).tags])
>> > >>   LogicalProject(orderId=[$0], name=[$1.name],
>> personId=[$1.personId],
>> > >> items
>> > >> =[$2])
>> > >>     LogicalTableScan(table=[[PCOLLECTION]])
>> > >>
>> > >> The first project looks the same. Flattened person row, items array,
>> all
>> > >> looks similar to the above.
>> > >> But the outer project calls ITEM($2, i). I would expect it to be
>> > >> ITEM($3, i) instead,
>> > >> to adjust for the flattened person row, but it keeps the index as $2,
>> > >> which
>> > >> would have been the correct index if the row was not flattened, but
>> it
>> > >> should be $3 for flattened row, similar to the previous example.
>> > >>
>> > >> Is there something I am missing or is it a bug and Calcite should
>> adjust
>> > >> the input ref index to account for flattened rows in this case as
>> well?
>> > >>
>> > >> Thank you,
>> > >> Anton
>> > >>
>> > >
>> > >
>> > >
>> > > --
>> > > "So you have to trust that the dots will somehow connect in your
>> future."
>> > >
>> >
>> >
>> >
>> > --
>> > "So you have to trust that the dots will somehow connect in your
>> future."
>> >
>>
>
>
>
> --
> "So you have to trust that the dots will somehow connect in your future."
>



-- 
"So you have to trust that the dots will somehow connect in your future."

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