Streamed fetch chunk #1
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squito
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Jun 14, 2018
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I think everything you're doing here seems fine, more or less, but now that I see how much has to be done for this, I'm thinking that perhaps this is not worth the complexity, especially given the sasl limitation. Sorry I know this sucks for you, as it was a lot of work to figure all this out.
the only real way that I can think of simplifying this change is if you leave the streamId as a String in StreamCallback. But I don't think that is really going to make a huge difference overall.
lets discuss this in the morning, I'll also talk to Kostas about it
| listener.onSuccess(resp.streamChunkId.chunkIndex, resp.body()); | ||
| } else { | ||
| StreamCallback<StreamChunkId> streamCallback = | ||
| outstandingStreamFetches.get(resp.streamChunkId).get(); |
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the whole point of the supplier is to delay this call to get(), right? you want to avoid creating the output file until here?
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Yes and to avoid creating unnecessary temp files for the small chunks (under maxRemoteBlockSizeFetchToMem) which will be loaded into the memory.
| if (frame == null) { | ||
| break; | ||
| if (isSasl) { | ||
| if (!isFrameSizeAvailable()) { |
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so this means, with sasl, there is no automatic conversion of fetch-to-disk?
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I think there is. With sasl the pipeline contains two instances of TransportFrameDecoder:
- the saslFrameDecoder which handles smaller frames which goes to the saslDecryption
- the "regular" frame decoder where we are doing our trick with the bigger fetch chunks
I base this on the transferTo method of EncryptedMessage which breaks down the original message into smaller chunks when needed.
attilapiros
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Nov 22, 2018
## What changes were proposed in this pull request? There were two related fixes regarding `from_json`, `get_json_object` and `json_tuple` ([Fix #1](apache@c8803c0), [Fix #2](apache@86174ea)), but they weren't comprehensive it seems. I wanted to extend those fixes to all the parsers, and add tests for each case. ## How was this patch tested? Regression tests Author: Burak Yavuz <[email protected]> Closes apache#20302 from brkyvz/json-invfix. (cherry picked from commit e01919e) Signed-off-by: hyukjinkwon <[email protected]>
attilapiros
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Nov 22, 2018
This is a backport of apache#20598. ## What changes were proposed in this pull request? Solved two bugs to enable stream-stream self joins. ### Incorrect analysis due to missing MultiInstanceRelation trait Streaming leaf nodes did not extend MultiInstanceRelation, which is necessary for the catalyst analyzer to convert the self-join logical plan DAG into a tree (by creating new instances of the leaf relations). This was causing the error `Failure when resolving conflicting references in Join:` (see JIRA for details). ### Incorrect attribute rewrite when splicing batch plans in MicroBatchExecution When splicing the source's batch plan into the streaming plan (by replacing the StreamingExecutionPlan), we were rewriting the attribute reference in the streaming plan with the new attribute references from the batch plan. This was incorrectly handling the scenario when multiple StreamingExecutionRelation point to the same source, and therefore eventually point to the same batch plan returned by the source. Here is an example query, and its corresponding plan transformations. ``` val df = input.toDF val join = df.select('value % 5 as "key", 'value).join( df.select('value % 5 as "key", 'value), "key") ``` Streaming logical plan before splicing the batch plan ``` Project [key#6, value#1, value#12] +- Join Inner, (key#6 = key#9) :- Project [(value#1 % 5) AS key#6, value#1] : +- StreamingExecutionRelation Memory[#1], value#1 +- Project [(value#12 % 5) AS key#9, value#12] +- StreamingExecutionRelation Memory[#1], value#12 // two different leaves pointing to same source ``` Batch logical plan after splicing the batch plan and before rewriting ``` Project [key#6, value#1, value#12] +- Join Inner, (key#6 = key#9) :- Project [(value#1 % 5) AS key#6, value#1] : +- LocalRelation [value#66] // replaces StreamingExecutionRelation Memory[#1], value#1 +- Project [(value#12 % 5) AS key#9, value#12] +- LocalRelation [value#66] // replaces StreamingExecutionRelation Memory[#1], value#12 ``` Batch logical plan after rewriting the attributes. Specifically, for spliced, the new output attributes (value#66) replace the earlier output attributes (value#12, and value#1, one for each StreamingExecutionRelation). ``` Project [key#6, value#66, value#66] // both value#1 and value#12 replaces by value#66 +- Join Inner, (key#6 = key#9) :- Project [(value#66 % 5) AS key#6, value#66] : +- LocalRelation [value#66] +- Project [(value#66 % 5) AS key#9, value#66] +- LocalRelation [value#66] ``` This causes the optimizer to eliminate value#66 from one side of the join. ``` Project [key#6, value#66, value#66] +- Join Inner, (key#6 = key#9) :- Project [(value#66 % 5) AS key#6, value#66] : +- LocalRelation [value#66] +- Project [(value#66 % 5) AS key#9] // this does not generate value, incorrect join results +- LocalRelation [value#66] ``` **Solution**: Instead of rewriting attributes, use a Project to introduce aliases between the output attribute references and the new reference generated by the spliced plans. The analyzer and optimizer will take care of the rest. ``` Project [key#6, value#1, value#12] +- Join Inner, (key#6 = key#9) :- Project [(value#1 % 5) AS key#6, value#1] : +- Project [value#66 AS value#1] // solution: project with aliases : +- LocalRelation [value#66] +- Project [(value#12 % 5) AS key#9, value#12] +- Project [value#66 AS value#12] // solution: project with aliases +- LocalRelation [value#66] ``` ## How was this patch tested? New unit test Author: Tathagata Das <[email protected]> Closes apache#20765 from tdas/SPARK-23406-2.3.
attilapiros
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Apr 8, 2019
…te temporary path in local staging directory ## What changes were proposed in this pull request? Th environment of my cluster as follows: ``` OS:Linux version 2.6.32-220.7.1.el6.x86_64 (mockbuildc6b18n3.bsys.dev.centos.org) (gcc version 4.4.6 20110731 (Red Hat 4.4.6-3) (GCC) ) #1 SMP Wed Mar 7 00:52:02 GMT 2012 Hadoop: 2.7.2 Spark: 2.3.0 or 3.0.0(master branch) Hive: 1.2.1 ``` My spark run on deploy mode yarn-client. If I execute the SQL `insert overwrite local directory '/home/test/call_center/' select * from call_center`, a HiveException will appear as follows: `Caused by: org.apache.hadoop.hive.ql.metadata.HiveException: java.io.IOException: Mkdirs failed to create file:/home/xitong/hive/stagingdir_hive_2019-02-19_17-31-00_678_1816816774691551856-1/-ext-10000/_temporary/0/_temporary/attempt_20190219173233_0002_m_000000_3 (exists=false, cwd=file:/data10/yarn/nm-local-dir/usercache/xitong/appcache/application_1543893582405_6126857/container_e124_1543893582405_6126857_01_000011) at org.apache.hadoop.hive.ql.io.HiveFileFormatUtils.getHiveRecordWriter(HiveFileFormatUtils.java:249)` Current spark sql generate a local temporary path in local staging directory.The schema of local temporary path start with `file`, so the HiveException appears. This PR change the local temporary path to HDFS temporary path, and use DistributedFileSystem instance copy the data from HDFS temporary path to local directory. If Spark run on local deploy mode, 'insert overwrite local directory' works fine. ## How was this patch tested? UT cannot support yarn-client mode.The test is in my product environment. Closes apache#23841 from beliefer/fix-bug-of-insert-overwrite-local-dir. Authored-by: gengjiaan <[email protected]> Signed-off-by: Sean Owen <[email protected]>
attilapiros
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Apr 26, 2019
## What changes were proposed in this pull request?
This PR supports `OpenJ9` in addition to `IBM JDK` and `OpenJDK` in Spark by handling `System.getProperty("java.vendor") = "Eclipse OpenJ9"`.
In `inferDefaultMemory()` and `getKrb5LoginModuleName()`, this PR uses non `IBM` way.
```
$ ~/jdk-11.0.2+9_openj9-0.12.1/bin/jshell
| Welcome to JShell -- Version 11.0.2
| For an introduction type: /help intro
jshell> System.out.println(System.getProperty("java.vendor"))
Eclipse OpenJ9
jshell> System.out.println(System.getProperty("java.vm.info"))
JRE 11 Linux amd64-64-Bit Compressed References 20190204_127 (JIT enabled, AOT enabled)
OpenJ9 - 90dd8cb40
OMR - d2f4534b
JCL - 289c70b6844 based on jdk-11.0.2+9
jshell> System.out.println(Class.forName("com.ibm.lang.management.OperatingSystemMXBean").getDeclaredMethod("getTotalPhysicalMemory"))
public abstract long com.ibm.lang.management.OperatingSystemMXBean.getTotalPhysicalMemory()
jshell> System.out.println(Class.forName("com.sun.management.OperatingSystemMXBean").getDeclaredMethod("getTotalPhysicalMemorySize"))
public abstract long com.sun.management.OperatingSystemMXBean.getTotalPhysicalMemorySize()
jshell> System.out.println(Class.forName("com.ibm.security.auth.module.Krb5LoginModule"))
| Exception java.lang.ClassNotFoundException: com.ibm.security.auth.module.Krb5LoginModule
| at Class.forNameImpl (Native Method)
| at Class.forName (Class.java:339)
| at (#1:1)
jshell> System.out.println(Class.forName("com.sun.security.auth.module.Krb5LoginModule"))
class com.sun.security.auth.module.Krb5LoginModule
```
## How was this patch tested?
Existing test suites
Manual testing with OpenJ9.
Closes apache#24308 from kiszk/SPARK-27397.
Authored-by: Kazuaki Ishizaki <[email protected]>
Signed-off-by: Sean Owen <[email protected]>
attilapiros
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Aug 7, 2019
…comparison assertions ## What changes were proposed in this pull request? This PR removes a few hardware-dependent assertions which can cause a failure in `aarch64`. **x86_64** ``` rootdonotdel-openlab-allinone-l00242678:/home/ubuntu# uname -a Linux donotdel-openlab-allinone-l00242678 4.4.0-154-generic apache#181-Ubuntu SMP Tue Jun 25 05:29:03 UTC 2019 x86_64 x86_64 x86_64 GNU/Linux scala> import java.lang.Float.floatToRawIntBits import java.lang.Float.floatToRawIntBits scala> floatToRawIntBits(0.0f/0.0f) res0: Int = -4194304 scala> floatToRawIntBits(Float.NaN) res1: Int = 2143289344 ``` **aarch64** ``` [rootarm-huangtianhua spark]# uname -a Linux arm-huangtianhua 4.14.0-49.el7a.aarch64 #1 SMP Tue Apr 10 17:22:26 UTC 2018 aarch64 aarch64 aarch64 GNU/Linux scala> import java.lang.Float.floatToRawIntBits import java.lang.Float.floatToRawIntBits scala> floatToRawIntBits(0.0f/0.0f) res1: Int = 2143289344 scala> floatToRawIntBits(Float.NaN) res2: Int = 2143289344 ``` ## How was this patch tested? Pass the Jenkins (This removes the test coverage). Closes apache#25186 from huangtianhua/special-test-case-for-aarch64. Authored-by: huangtianhua <[email protected]> Signed-off-by: Dongjoon Hyun <[email protected]>
attilapiros
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Nov 13, 2019
### What changes were proposed in this pull request? `org.apache.spark.sql.kafka010.KafkaDelegationTokenSuite` failed lately. After had a look at the logs it just shows the following fact without any details: ``` Caused by: sbt.ForkMain$ForkError: sun.security.krb5.KrbException: Server not found in Kerberos database (7) - Server not found in Kerberos database ``` Since the issue is intermittent and not able to reproduce it we should add more debug information and wait for reproduction with the extended logs. ### Why are the changes needed? Failing test doesn't give enough debug information. ### Does this PR introduce any user-facing change? No. ### How was this patch tested? I've started the test manually and checked that such additional debug messages show up: ``` >>> KrbApReq: APOptions are 00000000 00000000 00000000 00000000 >>> EType: sun.security.krb5.internal.crypto.Aes128CtsHmacSha1EType Looking for keys for: kafka/localhostEXAMPLE.COM Added key: 17version: 0 Added key: 23version: 0 Added key: 16version: 0 Found unsupported keytype (3) for kafka/localhostEXAMPLE.COM >>> EType: sun.security.krb5.internal.crypto.Aes128CtsHmacSha1EType Using builtin default etypes for permitted_enctypes default etypes for permitted_enctypes: 17 16 23. >>> EType: sun.security.krb5.internal.crypto.Aes128CtsHmacSha1EType MemoryCache: add 1571936500/174770/16C565221B70AAB2BEFE31A83D13A2F4/client/localhostEXAMPLE.COM to client/localhostEXAMPLE.COM|kafka/localhostEXAMPLE.COM MemoryCache: Existing AuthList: #3: 1571936493/200803/8CD70D280B0862C5DA1FF901ECAD39FE/client/localhostEXAMPLE.COM #2: 1571936499/985009/BAD33290D079DD4E3579A8686EC326B7/client/localhostEXAMPLE.COM #1: 1571936499/995208/B76B9D78A9BE283AC78340157107FD40/client/localhostEXAMPLE.COM ``` Closes apache#26252 from gaborgsomogyi/SPARK-29580. Authored-by: Gabor Somogyi <[email protected]> Signed-off-by: Dongjoon Hyun <[email protected]>
attilapiros
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### What changes were proposed in this pull request? Currently the join operators are not well abstracted, since there are lot of common logic. A trait can be created for easier pattern matching and other future handiness. This is a follow-up PR based on comment apache#27509 (comment) . This PR refined from the following aspects: 1. Refined structure of all physical join operators 2. Add missing joinType field for CartesianProductExec operator 3. Refined codes related to Explain Formatted The EXPLAIN FORMATTED changes are 1. Converge all join operator `verboseStringWithOperatorId` implementations to `BaseJoinExec`. Join condition displayed, and join keys displayed if it’s not empty. 2. `#1` will add Join condition to `BroadcastNestedLoopJoinExec`. 3. `#1` will **NOT** affect `CartesianProductExec`,`SortMergeJoin` and `HashJoin`s, since they already got there override implementation before. 4. Converge all join operator `simpleStringWithNodeId` to `BaseJoinExec`, which will enhance the one line description for `CartesianProductExec` with `JoinType` added. 5. Override `simpleStringWithNodeId` in `BroadcastNestedLoopJoinExec` to show `BuildSide`, which was only done for `HashJoin`s before. ### Why are the changes needed? Make the code consistent with other operators and for future handiness of join operators. ### Does this PR introduce any user-facing change? No ### How was this patch tested? Existing tests Closes apache#27595 from Eric5553/RefineJoin. Authored-by: Eric Wu <[email protected]> Signed-off-by: Wenchen Fan <[email protected]>
attilapiros
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Mar 18, 2021
… correctly ### What changes were proposed in this pull request? This PR proposes: 1. `CREATE OR REPLACE TEMP VIEW USING` should use `TemporaryViewRelation` to store temp views. 2. By doing #1, it fixes the issue where the temp view being replaced is not uncached. ### Why are the changes needed? This is a part of an ongoing work to wrap all the temporary views with `TemporaryViewRelation`: [SPARK-34698](https://issues.apache.org/jira/browse/SPARK-34698). This also fixes a bug where the temp view being replaced is not uncached. ### Does this PR introduce _any_ user-facing change? Yes, the temp view being replaced with `CREATE OR REPLACE TEMP VIEW USING` is correctly uncached if the temp view is cached. ### How was this patch tested? Added new tests. Closes apache#31825 from imback82/create_temp_view_using. Authored-by: Terry Kim <[email protected]> Signed-off-by: Wenchen Fan <[email protected]>
attilapiros
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Nov 4, 2022
…ly equivalent children in `RewriteDistinctAggregates` ### What changes were proposed in this pull request? In `RewriteDistinctAggregates`, when grouping aggregate expressions by function children, treat children that are semantically equivalent as the same. ### Why are the changes needed? This PR will reduce the number of projections in the Expand operator when there are multiple distinct aggregations with superficially different children. In some cases, it will eliminate the need for an Expand operator. Example: In the following query, the Expand operator creates 3\*n rows (where n is the number of incoming rows) because it has a projection for each of function children `b + 1`, `1 + b` and `c`. ``` create or replace temp view v1 as select * from values (1, 2, 3.0), (1, 3, 4.0), (2, 4, 2.5), (2, 3, 1.0) v1(a, b, c); select a, count(distinct b + 1), avg(distinct 1 + b) filter (where c > 0), sum(c) from v1 group by a; ``` The Expand operator has three projections (each producing a row for each incoming row): ``` [a#87, null, null, 0, null, UnscaledValue(c#89)], <== projection #1 (for regular aggregation) [a#87, (b#88 + 1), null, 1, null, null], <== projection #2 (for distinct aggregation of b + 1) [a#87, null, (1 + b#88), 2, (c#89 > 0.0), null]], <== projection #3 (for distinct aggregation of 1 + b) ``` In reality, the Expand only needs one projection for `1 + b` and `b + 1`, because they are semantically equivalent. With the proposed change, the Expand operator's projections look like this: ``` [a#67, null, 0, null, UnscaledValue(c#69)], <== projection #1 (for regular aggregations) [a#67, (b#68 + 1), 1, (c#69 > 0.0), null]], <== projection #2 (for distinct aggregation on b + 1 and 1 + b) ``` With one less projection, Expand produces 2\*n rows instead of 3\*n rows, but still produces the correct result. In the case where all distinct aggregates have semantically equivalent children, the Expand operator is not needed at all. Benchmark code in the JIRA (SPARK-40382). Before the PR: ``` distinct aggregates: Best Time(ms) Avg Time(ms) Stdev(ms) Rate(M/s) Per Row(ns) Relative ------------------------------------------------------------------------------------------------------------------------ all semantically equivalent 14721 14859 195 5.7 175.5 1.0X some semantically equivalent 14569 14572 5 5.8 173.7 1.0X none semantically equivalent 14408 14488 113 5.8 171.8 1.0X ``` After the PR: ``` distinct aggregates: Best Time(ms) Avg Time(ms) Stdev(ms) Rate(M/s) Per Row(ns) Relative ------------------------------------------------------------------------------------------------------------------------ all semantically equivalent 3658 3692 49 22.9 43.6 1.0X some semantically equivalent 9124 9214 127 9.2 108.8 0.4X none semantically equivalent 14601 14777 250 5.7 174.1 0.3X ``` ### Does this PR introduce _any_ user-facing change? No. ### How was this patch tested? New unit tests. Closes apache#37825 from bersprockets/rewritedistinct_issue. Authored-by: Bruce Robbins <[email protected]> Signed-off-by: Wenchen Fan <[email protected]>
attilapiros
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May 11, 2023
…nto w/ and w/o `ansi` suffix to pass sql analyzer test in ansi mode ### What changes were proposed in this pull request? After apache#40496, run ``` SPARK_ANSI_SQL_MODE=true build/sbt "sql/testOnly org.apache.spark.sql.SQLQueryTestSuite" ``` There is one test faild with `spark.sql.ansi.enabled = true` ``` [info] - timestampNTZ/datetime-special.sql_analyzer_test *** FAILED *** (11 milliseconds) [info] timestampNTZ/datetime-special.sql_analyzer_test [info] Expected "...date(999999, 3, 18, [false) AS make_date(999999, 3, 18)#x, make_date(-1, 1, 28, fals]e) AS make_date(-1, ...", but got "...date(999999, 3, 18, [true) AS make_date(999999, 3, 18)#x, make_date(-1, 1, 28, tru]e) AS make_date(-1, ..." Result did not match for query #1 [info] select make_date(999999, 3, 18), make_date(-1, 1, 28) (SQLQueryTestSuite.scala:777) [info] org.scalatest.exceptions.TestFailedException: ``` The failure reason is the last parameter of function `MakeDate` is `failOnError: Boolean = SQLConf.get.ansiEnabled`. So this pr split `timestampNTZ/datetime-special.sql` into w/ and w/o ansi to mask this test difference. ### Why are the changes needed? Make SQLQueryTestSuite test pass with `spark.sql.ansi.enabled = true`. ### Does this PR introduce _any_ user-facing change? No ### How was this patch tested? - Pass GitHub Actions - Manual checked `SPARK_ANSI_SQL_MODE=true build/sbt "sql/testOnly org.apache.spark.sql.SQLQueryTestSuite"` Closes apache#40552 from LuciferYang/SPARK-42921. Authored-by: yangjie01 <[email protected]> Signed-off-by: Hyukjin Kwon <[email protected]>
attilapiros
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### What changes were proposed in this pull request? This PR uses SMALLINT (as TINYINT ranges [0, 255]) instead of BYTE to fix the ByteType mapping for MsSQLServer JDBC ```java [info] com.microsoft.sqlserver.jdbc.SQLServerException: Column, parameter, or variable #1: Cannot find data type BYTE. [info] at com.microsoft.sqlserver.jdbc.SQLServerException.makeFromDatabaseError(SQLServerException.java:265) [info] at com.microsoft.sqlserver.jdbc.SQLServerStatement.getNextResult(SQLServerStatement.java:1662) [info] at com.microsoft.sqlserver.jdbc.SQLServerStatement.doExecuteStatement(SQLServerStatement.java:898) [info] at com.microsoft.sqlserver.jdbc.SQLServerStatement$StmtExecCmd.doExecute(SQLServerStatement.java:793) [info] at com.microsoft.sqlserver.jdbc.TDSCommand.execute(IOBuffer.java:7417) [info] at com.microsoft.sqlserver.jdbc.SQLServerConnection.executeCommand(SQLServerConnection.java:3488) [info] at com.microsoft.sqlserver.jdbc.SQLServerStatement.executeCommand(SQLServerStatement.java:262) [info] at com.microsoft.sqlserver.jdbc.SQLServerStatement.executeStatement(SQLServerStatement.java:237) [info] at com.microsoft.sqlserver.jdbc.SQLServerStatement.executeUpdate(SQLServerStatement.java:733) [info] at org.apache.spark.sql.jdbc.JdbcDialect.createTable(JdbcDialects.scala:267) ``` ### Why are the changes needed? bugfix ### Does this PR introduce _any_ user-facing change? no ### How was this patch tested? new tests ### Was this patch authored or co-authored using generative AI tooling? no Closes apache#46164 from yaooqinn/SPARK-47938. Lead-authored-by: Kent Yao <[email protected]> Co-authored-by: Dongjoon Hyun <[email protected]> Signed-off-by: Dongjoon Hyun <[email protected]>
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…rtition data results should return user-facing error
### What changes were proposed in this pull request?
Create an example parquet table with partitions and insert data in Spark:
```
create table t(col1 string, col2 string, col3 string) using parquet location 'some/path/parquet-test' partitioned by (col1, col2);
insert into t (col1, col2, col3) values ('a', 'b', 'c');
```
Go into the `parquet-test` path in the filesystem and try to copy parquet data file from path `col1=a/col2=b` directory into `col1=a`. After that, try to create new table based on parquet data in Spark:
```
create table broken_table using parquet location 'some/path/parquet-test';
```
This query errors with internal error. Stack trace excerpts:
```
org.apache.spark.SparkException: [INTERNAL_ERROR] Eagerly executed command failed. You hit a bug in Spark or the Spark plugins you use. Please, report this bug to the corresponding communities or vendors, and provide the full stack trace. SQLSTATE: XX000
...
Caused by: java.lang.AssertionError: assertion failed: Conflicting partition column names detected: Partition column name list #0: col1
Partition column name list #1: col1, col2For partitioned table directories, data files should only live in leaf directories.
And directories at the same level should have the same partition column name.
Please check the following directories for unexpected files or inconsistent partition column names: file:some/path/parquet-test/col1=a
file:some/path/parquet-test/col1=a/col2=b
at scala.Predef$.assert(Predef.scala:279)
at org.apache.spark.sql.execution.datasources.PartitioningUtils$.resolvePartitions(PartitioningUtils.scala:391)
...
```
Fix this by changing internal error to user-facing error.
### Why are the changes needed?
Replace internal error with user-facing one for valid sequence of Spark SQL operations.
### Does this PR introduce _any_ user-facing change?
Yes, it presents the user with regular error instead of internal error.
### How was this patch tested?
Added checks to `ParquetPartitionDiscoverySuite` which simulate the described scenario by manually breaking parquet table in the filesystem.
### Was this patch authored or co-authored using generative AI tooling?
No.
Closes apache#47668 from nikolamand-db/SPARK-49163.
Authored-by: Nikola Mandic <[email protected]>
Signed-off-by: Wenchen Fan <[email protected]>
attilapiros
pushed a commit
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Oct 4, 2024
…rtition data results should return user-facing error
### What changes were proposed in this pull request?
Create an example parquet table with partitions and insert data in Spark:
```
create table t(col1 string, col2 string, col3 string) using parquet location 'some/path/parquet-test' partitioned by (col1, col2);
insert into t (col1, col2, col3) values ('a', 'b', 'c');
```
Go into the `parquet-test` path in the filesystem and try to copy parquet data file from path `col1=a/col2=b` directory into `col1=a`. After that, try to create new table based on parquet data in Spark:
```
create table broken_table using parquet location 'some/path/parquet-test';
```
This query errors with internal error. Stack trace excerpts:
```
org.apache.spark.SparkException: [INTERNAL_ERROR] Eagerly executed command failed. You hit a bug in Spark or the Spark plugins you use. Please, report this bug to the corresponding communities or vendors, and provide the full stack trace. SQLSTATE: XX000
...
Caused by: java.lang.AssertionError: assertion failed: Conflicting partition column names detected: Partition column name list #0: col1
Partition column name list #1: col1, col2For partitioned table directories, data files should only live in leaf directories.
And directories at the same level should have the same partition column name.
Please check the following directories for unexpected files or inconsistent partition column names: file:some/path/parquet-test/col1=a
file:some/path/parquet-test/col1=a/col2=b
at scala.Predef$.assert(Predef.scala:279)
at org.apache.spark.sql.execution.datasources.PartitioningUtils$.resolvePartitions(PartitioningUtils.scala:391)
...
```
Fix this by changing internal error to user-facing error.
### Why are the changes needed?
Replace internal error with user-facing one for valid sequence of Spark SQL operations.
### Does this PR introduce _any_ user-facing change?
Yes, it presents the user with regular error instead of internal error.
### How was this patch tested?
Added checks to `ParquetPartitionDiscoverySuite` which simulate the described scenario by manually breaking parquet table in the filesystem.
### Was this patch authored or co-authored using generative AI tooling?
No.
Closes apache#47668 from nikolamand-db/SPARK-49163.
Authored-by: Nikola Mandic <[email protected]>
Signed-off-by: Wenchen Fan <[email protected]>
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