Follow up of https://github.com/apache/spark/pull/24405
### What changes were proposed in this pull request?
The current implementation of _from_avro_ and _AvroDataToCatalyst_ doesn't allow doing schema evolution since it requires the deserialization of an Avro record with the exact same schema with which it was serialized.
The proposed change is to add a new option `actualSchema` to allow passing the schema used to serialize the records. This allows using a different compatible schema for reading by passing both schemas to _GenericDatumReader_. If no writer's schema is provided, nothing changes from before.
### Why are the changes needed?
Consider the following example.
```
// schema ID: 1
val schema1 = """
{
"type": "record",
"name": "MySchema",
"fields": [
{"name": "col1", "type": "int"},
{"name": "col2", "type": "string"}
]
}
"""
// schema ID: 2
val schema2 = """
{
"type": "record",
"name": "MySchema",
"fields": [
{"name": "col1", "type": "int"},
{"name": "col2", "type": "string"},
{"name": "col3", "type": "string", "default": ""}
]
}
"""
```
The two schemas are compatible - i.e. you can use `schema2` to deserialize events serialized with `schema1`, in which case there will be the field `col3` with the default value.
Now imagine that you have two dataframes (read from batch or streaming), one with Avro events from schema1 and the other with events from schema2. **We want to combine them into one dataframe** for storing or further processing.
With the current `from_avro` function we can only decode each of them with the corresponding schema:
```
scalaval df1 = ... // Avro events created with schema1
df1: org.apache.spark.sql.DataFrame = [eventBytes: binary]
scalaval decodedDf1 = df1.select(from_avro('eventBytes, schema1) as "decoded")
decodedDf1: org.apache.spark.sql.DataFrame = [decoded: struct<col1: int, col2: string>]
scalaval df2= ... // Avro events created with schema2
df2: org.apache.spark.sql.DataFrame = [eventBytes: binary]
scalaval decodedDf2 = df2.select(from_avro('eventBytes, schema2) as "decoded")
decodedDf2: org.apache.spark.sql.DataFrame = [decoded: struct<col1: int, col2: string, col3: string>]
```
but then `decodedDf1` and `decodedDf2` have different Spark schemas and we can't union them. Instead, with the proposed change we can decode `df1` in the following way:
```
scalaimport scala.collection.JavaConverters._
scalaval decodedDf1 = df1.select(from_avro(data = 'eventBytes, jsonFormatSchema = schema2, options = Map("actualSchema" -> schema1).asJava) as "decoded")
decodedDf1: org.apache.spark.sql.DataFrame = [decoded: struct<col1: int, col2: string, col3: string>]
```
so that both dataframes have the same schemas and can be merged.
### Does this PR introduce any user-facing change?
This PR allows users to pass a new configuration but it doesn't affect current code.
### How was this patch tested?
A new unit test was added.
Closes#26780 from Fokko/SPARK-27506.
Lead-authored-by: Fokko Driesprong <fokko@apache.org>
Co-authored-by: Gianluca Amori <gianluca.amori@gmail.com>
Signed-off-by: Gengliang Wang <gengliang.wang@databricks.com>
### What changes were proposed in this pull request?
This patch fixes the Java code style violations in SPARK-30159 (#26788) which are caught by lint-java (Github Action caught it and I can reproduce it locally). Looks like Jenkins build may have different policy on checking Java style check or less accurate.
### Why are the changes needed?
Java linter starts complaining.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
lint-java passed locally
This closes#26819Closes#26818 from HeartSaVioR/SPARK-30159-FOLLOWUP.
Authored-by: Jungtaek Lim (HeartSaVioR) <kabhwan.opensource@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
Before this PR, the method `checkAnswer` in Object `QueryTest` returns an optional string. It doesn't throw exceptions when errors happen.
The actual exceptions are thrown in the trait `QueryTest`.
However, there are some test suites(`StreamSuite`, `SessionStateSuite`, `BinaryFileFormatSuite`, etc.) that use the no-op method `QueryTest.checkAnswer` and expect it to fail test cases when the execution results don't match the expected answers.
After this PR:
1. the method `checkAnswer` in Object `QueryTest` will fail tests on errors or unexpected results.
2. add a new method `getErrorMessageInCheckAnswer`, which is exactly the same as the previous version of `checkAnswer`. There are some test suites use this one to customize the test failure message.
3. for the test suites that extend the trait `QueryTest`, we should use the method `checkAnswer` directly, instead of calling the method from Object `QueryTest`.
### Why are the changes needed?
We should fix these method calls to perform actual validations in test suites.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Existing unit tests.
Closes#26788 from gengliangwang/fixCheckAnswer.
Authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
```java
public static final int YEARS_PER_DECADE = 10;
public static final int YEARS_PER_CENTURY = 100;
public static final int YEARS_PER_MILLENNIUM = 1000;
public static final byte MONTHS_PER_QUARTER = 3;
public static final int MONTHS_PER_YEAR = 12;
public static final byte DAYS_PER_WEEK = 7;
public static final long DAYS_PER_MONTH = 30L;
public static final long HOURS_PER_DAY = 24L;
public static final long MINUTES_PER_HOUR = 60L;
public static final long SECONDS_PER_MINUTE = 60L;
public static final long SECONDS_PER_HOUR = MINUTES_PER_HOUR * SECONDS_PER_MINUTE;
public static final long SECONDS_PER_DAY = HOURS_PER_DAY * SECONDS_PER_HOUR;
public static final long MILLIS_PER_SECOND = 1000L;
public static final long MILLIS_PER_MINUTE = SECONDS_PER_MINUTE * MILLIS_PER_SECOND;
public static final long MILLIS_PER_HOUR = MINUTES_PER_HOUR * MILLIS_PER_MINUTE;
public static final long MILLIS_PER_DAY = HOURS_PER_DAY * MILLIS_PER_HOUR;
public static final long MICROS_PER_MILLIS = 1000L;
public static final long MICROS_PER_SECOND = MILLIS_PER_SECOND * MICROS_PER_MILLIS;
public static final long MICROS_PER_MINUTE = SECONDS_PER_MINUTE * MICROS_PER_SECOND;
public static final long MICROS_PER_HOUR = MINUTES_PER_HOUR * MICROS_PER_MINUTE;
public static final long MICROS_PER_DAY = HOURS_PER_DAY * MICROS_PER_HOUR;
public static final long MICROS_PER_MONTH = DAYS_PER_MONTH * MICROS_PER_DAY;
/* 365.25 days per year assumes leap year every four years */
public static final long MICROS_PER_YEAR = (36525L * MICROS_PER_DAY) / 100;
public static final long NANOS_PER_MICROS = 1000L;
public static final long NANOS_PER_MILLIS = MICROS_PER_MILLIS * NANOS_PER_MICROS;
public static final long NANOS_PER_SECOND = MILLIS_PER_SECOND * NANOS_PER_MILLIS;
```
The above parameters are defined in IntervalUtils, DateTimeUtils, and CalendarInterval, some of them are redundant, some of them are cross-referenced.
### Why are the changes needed?
To simplify code, enhance consistency and reduce risks
### Does this PR introduce any user-facing change?
no
### How was this patch tested?
modified uts
Closes#26399 from yaooqinn/SPARK-29757.
Authored-by: Kent Yao <yaooqinn@hotmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
To push the built jars to maven release repository, we need to remove the 'SNAPSHOT' tag from the version name.
Made the following changes in this PR:
* Update all the `3.0.0-SNAPSHOT` version name to `3.0.0-preview`
* Update the sparkR version number check logic to allow jvm version like `3.0.0-preview`
**Please note those changes were generated by the release script in the past, but this time since we manually add tags on master branch, we need to manually apply those changes too.**
We shall revert the changes after 3.0.0-preview release passed.
### Why are the changes needed?
To make the maven release repository to accept the built jars.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
N/A
### What changes were proposed in this pull request?
To push the built jars to maven release repository, we need to remove the 'SNAPSHOT' tag from the version name.
Made the following changes in this PR:
* Update all the `3.0.0-SNAPSHOT` version name to `3.0.0-preview`
* Update the PySpark version from `3.0.0.dev0` to `3.0.0`
**Please note those changes were generated by the release script in the past, but this time since we manually add tags on master branch, we need to manually apply those changes too.**
We shall revert the changes after 3.0.0-preview release passed.
### Why are the changes needed?
To make the maven release repository to accept the built jars.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
N/A
Closes#26243 from jiangxb1987/3.0.0-preview-prepare.
Lead-authored-by: Xingbo Jiang <xingbo.jiang@databricks.com>
Co-authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: Xingbo Jiang <xingbo.jiang@databricks.com>
### What changes were proposed in this pull request?
This PR regenerate the benchmark results in `catalyst` and `avro` module in order to compare JDK8/JDK11 result.
### Why are the changes needed?
This PR aims to verify that there is no regression on JDK11.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
This is a test-only update. We need to run the benchmark manually.
Closes#25972 from dongjoon-hyun/SPARK-29300.
Authored-by: Dongjoon Hyun <dhyun@apple.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
Scala 2.13 emits a deprecation warning for procedure-like declarations:
```
def foo() {
...
```
This is equivalent to the following, so should be changed to avoid a warning:
```
def foo(): Unit = {
...
```
### Why are the changes needed?
It will avoid about a thousand compiler warnings when we start to support Scala 2.13. I wanted to make the change in 3.0 as there are less likely to be back-ports from 3.0 to 2.4 than 3.1 to 3.0, for example, minimizing that downside to touching so many files.
Unfortunately, that makes this quite a big change.
### Does this PR introduce any user-facing change?
No behavior change at all.
### How was this patch tested?
Existing tests.
Closes#25968 from srowen/SPARK-29291.
Authored-by: Sean Owen <sean.owen@databricks.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
Refactored SQL-related benchmark and made them depend on `SqlBasedBenchmark`. In particular, creation of Spark session are moved into `override def getSparkSession: SparkSession`.
### Why are the changes needed?
This should simplify maintenance of SQL-based benchmarks by reducing the number of dependencies. In the future, it should be easier to refactor & extend all SQL benchmarks by changing only one trait. Finally, all SQL-based benchmarks will look uniformly.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
By running the modified benchmarks.
Closes#25828 from MaxGekk/sql-benchmarks-refactoring.
Lead-authored-by: Maxim Gekk <max.gekk@gmail.com>
Co-authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
reorganize the packages of DS v2 interfaces/classes:
1. `org.spark.sql.connector.catalog`: put `TableCatalog`, `Table` and other related interfaces/classes
2. `org.spark.sql.connector.expression`: put `Expression`, `Transform` and other related interfaces/classes
3. `org.spark.sql.connector.read`: put `ScanBuilder`, `Scan` and other related interfaces/classes
4. `org.spark.sql.connector.write`: put `WriteBuilder`, `BatchWrite` and other related interfaces/classes
### Why are the changes needed?
Data Source V2 has evolved a lot. It's a bit weird that `Expression` is in `org.spark.sql.catalog.v2` and `Table` is in `org.spark.sql.sources.v2`.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
existing tests
Closes#25700 from cloud-fan/package.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
The Experimental and Evolving annotations are both (like Unstable) used to express that a an API may change. However there are many things in the code that have been marked that way since even Spark 1.x. Per the dev thread, anything introduced at or before Spark 2.3.0 is pretty much 'stable' in that it would not change without a deprecation cycle. Therefore I'd like to remove most of these annotations. And, remove the `:: Experimental ::` scaladoc tag too. And likewise for Python, R.
The changes below can be summarized as:
- Generally, anything introduced at or before Spark 2.3.0 has been unmarked as neither Evolving nor Experimental
- Obviously experimental items like DSv2, Barrier mode, ExperimentalMethods are untouched
- I _did_ unmark a few MLlib classes introduced in 2.4, as I am quite confident they're not going to change (e.g. KolmogorovSmirnovTest, PowerIterationClustering)
It's a big change to review, so I'd suggest scanning the list of _files_ changed to see if any area seems like it should remain partly experimental and examine those.
### Why are the changes needed?
Many of these annotations are incorrect; the APIs are de facto stable. Leaving them also makes legitimate usages of the annotations less meaningful.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Existing tests.
Closes#25558 from srowen/SPARK-28855.
Authored-by: Sean Owen <sean.owen@databricks.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Currently we have 2 configs to specify which v2 sources should fallback to v1 code path. One config for read path, and one config for write path.
However, I found it's awkward to work with these 2 configs:
1. for `CREATE TABLE USING format`, should this be read path or write path?
2. for `V2SessionCatalog.loadTable`, we need to return `UnresolvedTable` if it's a DS v1 or we need to fallback to v1 code path. However, at that time, we don't know if the returned table will be used for read or write.
We don't have any new features or perf improvement in file source v2. The fallback API is just a safeguard if we have bugs in v2 implementations. There are not many benefits to support falling back to v1 for read and write path separately.
This PR proposes to merge these 2 configs into one.
## How was this patch tested?
existing tests
Closes#25465 from cloud-fan/merge-conf.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
The Spark SQL test framework needs to support 2 kinds of tests:
1. tests inside Spark to test Spark itself (extends `SparkFunSuite`)
2. test outside of Spark to test Spark applications (introduced at b57ed2245c)
The class hierarchy of the major testing traits:
`PlanTestBase`, `SQLTestUtilsBase` and `SharedSparkSession` intentionally don't extend `SparkFunSuite`, so that they can be used for tests outside of Spark. Tests in Spark should extends `QueryTest` and/or `SharedSQLContext` in most cases.
However, the name is a little confusing. As a result, some test suites extend `SharedSparkSession` instead of `SharedSQLContext`. `SharedSparkSession` doesn't work well with `SparkFunSuite` as it doesn't have the special handling of thread auditing in `SharedSQLContext`. For example, you will see a warning starting with `===== POSSIBLE THREAD LEAK IN SUITE` when you run `DataFrameSelfJoinSuite`.
This PR proposes to rename `SharedSparkSession` to `SharedSparkSessionBase`, and rename `SharedSQLContext` to `SharedSparkSession`.
## How was this patch tested?
(Please explain how this patch was tested. E.g. unit tests, integration tests, manual tests)
(If this patch involves UI changes, please attach a screenshot; otherwise, remove this)
Please review https://spark.apache.org/contributing.html before opening a pull request.
Closes#25463 from cloud-fan/minor.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
The mapping of Spark schema to Avro schema is many-to-many. (See https://spark.apache.org/docs/latest/sql-data-sources-avro.html#supported-types-for-spark-sql---avro-conversion)
The default schema mapping might not be exactly what users want. For example, by default, a "string" column is always written as "string" Avro type, but users might want to output the column as "enum" Avro type.
With PR https://github.com/apache/spark/pull/21847, Spark supports user-specified schema in the batch writer.
For the function `to_avro`, we should support user-specified output schema as well.
## How was this patch tested?
Unit test.
Closes#25419 from gengliangwang/to_avro.
Authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
This PR change `CalendarIntervalType`'s readable string representation from `calendarinterval` to `interval`.
## How was this patch tested?
Existing UT
Closes#25225 from wangyum/SPARK-28469.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
There are some hardcoded configs, using config entry to replace them.
## How was this patch tested?
Existing UT
Closes#25059 from WangGuangxin/ConfigEntry.
Authored-by: wangguangxin.cn <wangguangxin.cn@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
Migrate Avro to File source V2.
## How was this patch tested?
Unit test
Closes#25017 from gengliangwang/avroV2.
Authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
When using `from_avro` to deserialize avro data to catalyst StructType format, if `ConvertToLocalRelation` is applied at the time, `from_avro` produces only the last value (overriding previous values).
The cause is `AvroDeserializer` reuses output row for StructType. Normally, it should be fine in Spark SQL. But `ConvertToLocalRelation` just uses `InterpretedProjection` to project local rows. `InterpretedProjection` creates new row for each output thro, it includes the same nested row object from `AvroDeserializer`. By the end, converted local relation has only last value.
I think there're two possible options:
1. Make `AvroDeserializer` output new row for StructType.
2. Use `InterpretedMutableProjection` in `ConvertToLocalRelation` and call `copy()` on output rows.
Option 2 is chose because previously `ConvertToLocalRelation` also creates new rows, this `InterpretedMutableProjection` + `copy()` shoudn't bring too much performance penalty. `ConvertToLocalRelation` should be arguably less critical, compared with `AvroDeserializer`.
## How was this patch tested?
Added test.
Closes#24805 from viirya/SPARK-27798.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
This PR aims to fix an issue on a union avro type with more than one non-null value (for instance `["string", "null", "int"]`) whose the deserialization to a DataFrame would throw a `java.lang.ArrayIndexOutOfBoundsException`. The issue was that the `fieldWriter` relied on the index from the avro schema before nulls were filtered out.
## How was this patch tested?
A test for the case of multiple non-null values was added and the tests were run using sbt by running `testOnly org.apache.spark.sql.avro.AvroSuite`
Closes#24722 from gcmerz/master.
Authored-by: Gabbi Merz <gmerz@palantir.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
When the data is read from the sources, the catalyst schema is always nullable. Since Avro uses Union type to represent nullable, when any non-nullable avro file is read and then written out, the schema will always be changed.
This PR provides a solution for users to keep the Avro schema without being forced to use Union type.
## How was this patch tested?
One test is added.
Closes#24682 from dbtsai/avroNull.
Authored-by: DB Tsai <d_tsai@apple.com>
Signed-off-by: DB Tsai <d_tsai@apple.com>
## What changes were proposed in this pull request?
Spark Avro reader supports reading avro files with provided schema with different field orderings. However, the avro writer doesn't support this feature. This PR enables the Spark avro writer with this feature.
## How was this patch tested?
New test is added.
Closes#24635 from dbtsai/avroFix.
Lead-authored-by: DB Tsai <d_tsai@apple.com>
Co-authored-by: Brian Lindblom <blindblom@apple.com>
Signed-off-by: DB Tsai <d_tsai@apple.com>
## What changes were proposed in this pull request?
### Background:
The data source option `pathGlobFilter` is introduced for Binary file format: https://github.com/apache/spark/pull/24354 , which can be used for filtering file names, e.g. reading `.png` files only while there is `.json` files in the same directory.
### Proposal:
Make the option `pathGlobFilter` as a general option for all file sources. The path filtering should happen in the path globbing on Driver.
### Motivation:
Filtering the file path names in file scan tasks on executors is kind of ugly.
### Impact:
1. The splitting of file partitions will be more balanced.
2. The metrics of file scan will be more accurate.
3. Users can use the option for reading other file sources.
## How was this patch tested?
Unit tests
Closes#24518 from gengliangwang/globFilter.
Authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
The reader schema is said to be evolved (or projected) when it changed after the data is written by writers. Apache Spark file-based data sources have a test coverage for that; e.g. [ReadSchemaSuite.scala](https://github.com/apache/spark/blob/master/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/ReadSchemaSuite.scala). This PR aims to add a test coverage for nested columns by adding and hiding nested columns.
## How was this patch tested?
Pass the Jenkins with newly added tests.
Closes#24139 from dongjoon-hyun/SPARK-27197.
Authored-by: Dongjoon Hyun <dhyun@apple.com>
Signed-off-by: DB Tsai <d_tsai@apple.com>
## What changes were proposed in this pull request?
The reader schema is said to be evolved (or projected) when it changed after the data is written by writers. Apache Spark file-based data sources have a test coverage for that, [ReadSchemaSuite.scala](https://github.com/apache/spark/blob/master/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/ReadSchemaSuite.scala). This PR aims to add `AvroReadSchemaSuite` to ensure the minimal consistency among file-based data sources and prevent a future regression in Avro data source.
## How was this patch tested?
Pass the Jenkins with the newly added test suite.
Closes#24135 from dongjoon-hyun/SPARK-27195.
Authored-by: Dongjoon Hyun <dhyun@apple.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
Currently, looks, to use `from_avro` and `to_avro` in Java APIs side,
```java
import static org.apache.spark.sql.avro.package$.MODULE$;
MODULE$.to_avro
MODULE$.from_avro
```
This PR targets to deprecate and move both functions under `avro` package into `functions` object like the way of our `org.apache.spark.sql.functions`.
Therefore, Java side can import:
```java
import static org.apache.spark.sql.avro.functions.*;
```
and Scala side can import:
```scala
import org.apache.spark.sql.avro.functions._
```
## How was this patch tested?
Manually tested, and unit tests for Java APIs were added.
Closes#23784 from HyukjinKwon/SPARK-26870.
Authored-by: Hyukjin Kwon <gurwls223@apache.org>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
In #23639, the API `supportDataType` is refactored. We should also remove the method `verifyWriteSchema` and `verifyReadSchema` in `DataSourceUtils`.
Since the error message use `FileFormat.toString` to specify the data source naming, this PR also overriding the `toString` method in `AvroFileFormat`.
## How was this patch tested?
Unit test.
Closes#23699 from gengliangwang/SPARK-26716-followup.
Authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
The API `supportDataType` in `FileFormat` helps to validate the output/input schema before exection starts. So that we can avoid some invalid data source IO, and users can see clean error messages.
This PR is to override the validation API in Avro data source.
Also, as per the spec of Avro(https://avro.apache.org/docs/1.8.2/spec.html), `NullType` is supported. This PR fixes the handling of `NullType`.
## How was this patch tested?
Unit test
Closes#23684 from gengliangwang/avroSupportDataType.
Authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
Currently duplicated map keys are not handled consistently. For example, map look up respects the duplicated key appears first, `Dataset.collect` only keeps the duplicated key appears last, `MapKeys` returns duplicated keys, etc.
This PR proposes to remove duplicated map keys with last wins policy, to follow Java/Scala and Presto. It only applies to built-in functions, as users can create map with duplicated map keys via private APIs anyway.
updated functions: `CreateMap`, `MapFromArrays`, `MapFromEntries`, `StringToMap`, `MapConcat`, `TransformKeys`.
For other places:
1. data source v1 doesn't have this problem, as users need to provide a java/scala map, which can't have duplicated keys.
2. data source v2 may have this problem. I've added a note to `ArrayBasedMapData` to ask the caller to take care of duplicated keys. In the future we should enforce it in the stable data APIs for data source v2.
3. UDF doesn't have this problem, as users need to provide a java/scala map. Same as data source v1.
4. file format. I checked all of them and only parquet does not enforce it. For backward compatibility reasons I change nothing but leave a note saying that the behavior will be undefined if users write map with duplicated keys to parquet files. Maybe we can add a config and fail by default if parquet files have map with duplicated keys. This can be done in followup.
## How was this patch tested?
updated tests and new tests
Closes#23124 from cloud-fan/map.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
The build has a lot of deprecation warnings. Some are new in Scala 2.12 and Java 11. We've fixed some, but I wanted to take a pass at fixing lots of easy miscellaneous ones here.
They're too numerous and small to list here; see the pull request. Some highlights:
- `BeanInfo` is deprecated in 2.12, and BeanInfo classes are pretty ancient in Java. Instead, case classes can explicitly declare getters
- Eta expansion of zero-arg methods; foo() becomes () => foo() in many cases
- Floating-point Range is inexact and deprecated, like 0.0 to 100.0 by 1.0
- finalize() is finally deprecated (just needs to be suppressed)
- StageInfo.attempId was deprecated and easiest to remove here
I'm not now going to touch some chunks of deprecation warnings:
- Parquet deprecations
- Hive deprecations (particularly serde2 classes)
- Deprecations in generated code (mostly Thriftserver CLI)
- ProcessingTime deprecations (we may need to revive this class as internal)
- many MLlib deprecations because they concern methods that may be removed anyway
- a few Kinesis deprecations I couldn't figure out
- Mesos get/setRole, which I don't know well
- Kafka/ZK deprecations (e.g. poll())
- Kinesis
- a few other ones that will probably resolve by deleting a deprecated method
## How was this patch tested?
Existing tests, including manual testing with the 2.11 build and Java 11.
Closes#23065 from srowen/SPARK-26090.
Authored-by: Sean Owen <sean.owen@databricks.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
This PR makes Spark's default Scala version as 2.12, and Scala 2.11 will be the alternative version. This implies that Scala 2.12 will be used by our CI builds including pull request builds.
We'll update the Jenkins to include a new compile-only jobs for Scala 2.11 to ensure the code can be still compiled with Scala 2.11.
## How was this patch tested?
existing tests
Closes#22967 from dbtsai/scala2.12.
Authored-by: DB Tsai <d_tsai@apple.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
Add read benchmark for Avro, which is missing for a period.
The benchmark is similar to `DataSourceReadBenchmark` and `OrcReadBenchmark`
Manually run benchmark
Closes#22966 from gengliangwang/avroReadBenchmark.
Lead-authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Co-authored-by: Gengliang Wang <ltnwgl@gmail.com>
Co-authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
## What changes were proposed in this pull request?
Deprecated in Java 11, replace Class.newInstance with Class.getConstructor.getInstance, and primtive wrapper class constructors with valueOf or equivalent
## How was this patch tested?
Existing tests.
Closes#22988 from srowen/SPARK-25984.
Authored-by: Sean Owen <sean.owen@databricks.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Currently in `FailureSafeParser` and `from_avro`, the exception is created with such code
```
throw new SparkException("Malformed records are detected in record parsing. " +
s"Parse Mode: ${FailFastMode.name}.", e.cause)
```
1. The cause part should be `e` instead of `e.cause`
2. If `e` contains non-null message, it should be shown in `from_json`/`from_csv`/`from_avro`, e.g.
```
com.fasterxml.jackson.core.JsonParseException: Unexpected character ('1' (code 49)): was expecting a colon to separate field name and value
at [Source: (InputStreamReader); line: 1, column: 7]
```
3.Kindly show hint for trying PERMISSIVE in error message.
## How was this patch tested?
Unit test.
Closes#22895 from gengliangwang/improve_error_msg.
Authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Refactor BuiltInDataSourceWriteBenchmark, DataSourceWriteBenchmark and AvroWriteBenchmark to use main method.
```
SPARK_GENERATE_BENCHMARK_FILES=1 build/sbt "sql/test:runMain org.apache.spark.sql.execution.benchmark.BuiltInDataSourceWriteBenchmark"
SPARK_GENERATE_BENCHMARK_FILES=1 build/sbt "avro/test:runMain org.apache.spark.sql.execution.benchmark.AvroWriteBenchmark"
```
## How was this patch tested?
manual tests
Closes#22861 from yucai/BuiltInDataSourceWriteBenchmark.
Lead-authored-by: yucai <yyu1@ebay.com>
Co-authored-by: Yucai Yu <yucai.yu@foxmail.com>
Co-authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
## What changes were proposed in this pull request?
Previously in from_avro/to_avro, we override the method `simpleString` and `sql` for the string output. However, the override only affects the alias naming:
```
Project [from_avro('col,
...
, (mode,PERMISSIVE)) AS from_avro(col, struct<col1:bigint,col2:double>, Map(mode -> PERMISSIVE))#11]
```
It only makes the alias name quite long: `from_avro(col, struct<col1:bigint,col2:double>, Map(mode -> PERMISSIVE))`).
We should follow `from_csv`/`from_json` here, to override the method prettyName only, and we will get a clean alias name
```
... AS from_avro(col)#11
```
## How was this patch tested?
Manual check
Closes#22890 from gengliangwang/revise_from_to_avro.
Authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
Current the function `from_avro` throws exception on reading corrupt records.
In practice, there could be various reasons of data corruption. It would be good to support `PERMISSIVE` mode and allow the function from_avro to process all the input file/streaming, which is consistent with from_json and from_csv. There is no obvious down side for supporting `PERMISSIVE` mode.
Different from `from_csv` and `from_json`, the default parse mode is `FAILFAST` for the following reasons:
1. Since Avro is structured data format, input data is usually able to be parsed by certain schema. In such case, exposing the problems of input data to users is better than hiding it.
2. For `PERMISSIVE` mode, we have to force the data schema as fully nullable. This seems quite unnecessary for Avro. Reversing non-null schema might archive more perf optimizations in Spark.
3. To be consistent with the behavior in Spark 2.4 .
## How was this patch tested?
Unit test
Manual previewing generated html for the Avro data source doc:
Closes#22814 from gengliangwang/improve_from_avro.
Authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Avro schema allows recursive reference, e.g. the schema for linked-list in https://avro.apache.org/docs/1.8.2/spec.html#schema_record
```
{
"type": "record",
"name": "LongList",
"aliases": ["LinkedLongs"], // old name for this
"fields" : [
{"name": "value", "type": "long"}, // each element has a long
{"name": "next", "type": ["null", "LongList"]} // optional next element
]
}
```
In current Spark SQL, it is impossible to convert the schema as `StructType` . Run `SchemaConverters.toSqlType(avroSchema)` and we will get stack overflow exception.
We should detect the recursive reference and throw exception for it.
## How was this patch tested?
New unit test case.
Closes#22709 from gengliangwang/avroRecursiveRef.
Authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
With flag `IGNORE_CORRUPT_FILES` enabled, schema inference should ignore corrupt Avro files, which is consistent with Parquet and Orc data source.
## How was this patch tested?
Unit test
Closes#22611 from gengliangwang/ignoreCorruptAvro.
Authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
This patch is to bump the master branch version to 3.0.0-SNAPSHOT.
## How was this patch tested?
N/A
Closes#22606 from gatorsmile/bump3.0.
Authored-by: gatorsmile <gatorsmile@gmail.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
Refactor `DataSourceWriteBenchmark` and add write benchmark for AVRO.
## How was this patch tested?
Build and run the benchmark.
Closes#22451 from gengliangwang/avroWriteBenchmark.
Authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
In the dev list, we can still discuss whether the next version is 2.5.0 or 3.0.0. Let us first bump the master branch version to `2.5.0-SNAPSHOT`.
## How was this patch tested?
N/A
Closes#22426 from gatorsmile/bumpVersionMaster.
Authored-by: gatorsmile <gatorsmile@gmail.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
`toAvroType` converts spark data type to avro schema. It always appends the record name to namespace so its impossible to have an Avro namespace independent of the record name.
When invoked with a spark data type like,
```java
val sparkSchema = StructType(Seq(
StructField("name", StringType, nullable = false),
StructField("address", StructType(Seq(
StructField("city", StringType, nullable = false),
StructField("state", StringType, nullable = false))),
nullable = false)))
// map it to an avro schema with record name "employee" and top level namespace "foo.bar",
val avroSchema = SchemaConverters.toAvroType(sparkSchema, false, "employee", "foo.bar")
// result is
// avroSchema.getName = employee
// avroSchema.getNamespace = foo.bar.employee
// avroSchema.getFullname = foo.bar.employee.employee
```
The patch proposes to fix this so that the result is
```
avroSchema.getName = employee
avroSchema.getNamespace = foo.bar
avroSchema.getFullname = foo.bar.employee
```
## How was this patch tested?
New and existing unit tests.
Please review http://spark.apache.org/contributing.html before opening a pull request.
Closes#22251 from arunmahadevan/avro-fix.
Authored-by: Arun Mahadevan <arunm@apache.org>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
In https://github.com/apache/spark/pull/21838, the class `AvroDataToCatalyst` and `CatalystDataToAvro` were put in package `org.apache.spark.sql`.
They should be moved to package `org.apache.spark.sql.avro`.
Also optimize imports in Avro module.
## How was this patch tested?
Unit test
Closes#22196 from gengliangwang/avro_revise_package_name.
Authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
In https://issues.apache.org/jira/browse/SPARK-24924, the data source provider com.databricks.spark.avro is mapped to the new package org.apache.spark.sql.avro .
As per the discussion in the [Jira](https://issues.apache.org/jira/browse/SPARK-24924) and PR #22119, we should make the mapping configurable.
This PR also improve the error message when data source of Avro/Kafka is not found.
## How was this patch tested?
Unit test
Closes#22133 from gengliangwang/configurable_avro_mapping.
Authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Signed-off-by: Xiao Li <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
In the PR for supporting logical timestamp types https://github.com/apache/spark/pull/21935, a SQL configuration spark.sql.avro.outputTimestampType is added, so that user can specify the output timestamp precision they want.
With PR https://github.com/apache/spark/pull/21847, the output file can be written with user specified types.
So there is no need to have such trivial configuration. Otherwise to make it consistent we need to add configuration for all the Catalyst types that can be converted into different Avro types.
This PR also add a test case for user specified output schema with different timestamp types.
## How was this patch tested?
Unit test
Closes#22151 from gengliangwang/removeOutputTimestampType.
Authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
With code changes in https://github.com/apache/spark/pull/21847 , Spark can write out to Avro file as per user provided output schema.
To make it more robust and user friendly, we should validate the Avro schema before tasks launched.
Also we should support output logical decimal type as BYTES (By default we output as FIXED)
## How was this patch tested?
Unit test
Closes#22094 from gengliangwang/AvroSerializerMatch.
Authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Signed-off-by: DB Tsai <d_tsai@apple.com>
## What changes were proposed in this pull request?
In PR https://github.com/apache/spark/pull/21984 and https://github.com/apache/spark/pull/21935 , the related test cases are using binary files created by Python scripts.
Generate the binary files in test suite to make it more transparent. Also we can
Also move the related test cases to a new file `AvroLogicalTypeSuite.scala`.
## How was this patch tested?
Unit test.
Closes#22091 from gengliangwang/logicalType_suite.
Authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
Support Avro logical date type:
https://avro.apache.org/docs/1.8.2/spec.html#Decimal
## How was this patch tested?
Unit test
Closes#22037 from gengliangwang/avro_decimal.
Authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
