### What changes were proposed in this pull request?
Adds `from_avro` and `to_avro` functions to SparkR.
### Why are the changes needed?
Feature parity.
### Does this PR introduce _any_ user-facing change?
New functions exposed in SparkR API.
### How was this patch tested?
New unit tests.
Closes#30216 from zero323/SPARK-33304.
Authored-by: zero323 <mszymkiewicz@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This pull request changes the description about `to_avro` and `from_avro` functions to include Python as a supported language as the functions have been supported in Python since Apache Spark 3.0.0 [[SPARK-26856](https://issues.apache.org/jira/browse/SPARK-26856)].
### Why are the changes needed?
Same as above.
### Does this PR introduce _any_ user-facing change?
Yes. The description changed by this pull request is on https://spark.apache.org/docs/latest/sql-data-sources-avro.html#to_avro-and-from_avro.
### How was this patch tested?
Tested manually by building and checking the document in the local environment.
Closes#30105 from kjmrknsn/fix-docs-sql-data-sources-avro.
Authored-by: Keiji Yoshida <kjmrknsn@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Updated `docs/sql-data-sources-avro.md`, and added a few sentences about already deprecated in code Avro option `ignoreExtension`.
<img width="968" alt="Screen Shot 2020-01-15 at 10 24 14" src="https://user-images.githubusercontent.com/1580697/72413684-64d1c780-3781-11ea-948a-d3cccf4c72df.png">
Closes#27174
### Why are the changes needed?
To make users doc consistent to the code where `ignoreExtension` has been already deprecated, see 3663dbe541/external/avro/src/main/scala/org/apache/spark/sql/avro/AvroUtils.scala (L46-L47)
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
by building docs
Closes#27194 from MaxGekk/avro-doc-deprecation-ignoreExtension.
Authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This is a follow-up of https://github.com/apache/spark/pull/26780
In https://github.com/apache/spark/pull/26780, a new Avro data source option `actualSchema` is introduced for setting the original Avro schema in function `from_avro`, while the expected schema is supposed to be set in the parameter `jsonFormatSchema` of `from_avro`.
However, there is another Avro data source option `avroSchema`. It is used for setting the expected schema in readiong and writing.
This PR is to use the option `avroSchema` option for reading Avro data with an evolved schema and remove the new one `actualSchema`
### Why are the changes needed?
Unify and simplify the Avro data source options.
### Does this PR introduce any user-facing change?
Yes.
To deserialize Avro data with an evolved schema, before changes:
```
from_avro('col, expectedSchema, ("actualSchema" -> actualSchema))
```
After changes:
```
from_avro('col, actualSchema, ("avroSchema" -> expectedSchema))
```
The second parameter is always the actual Avro schema after changes.
### How was this patch tested?
Update the existing tests in https://github.com/apache/spark/pull/26780Closes#27045 from gengliangwang/renameAvroOption.
Authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Fixed typo in `docs` directory and in other directories
1. Find typo in `docs` and apply fixes to files in all directories
2. Fix `the the` -> `the`
### Why are the changes needed?
Better readability of documents
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
No test needed
Closes#26976 from kiszk/typo_20191221.
Authored-by: Kazuaki Ishizaki <ishizaki@jp.ibm.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
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 is a minor documentation change whereby the https://spark.apache.org/docs/latest/sql-data-sources-avro.html mentions "The date type and naming of record fields should match the input Avro data or Catalyst data,"
The term Catalyst data is confusing. It should instead say, Spark's internal data type such as String
Type or IntegerType.
## How was this patch tested?
(Please explain how this patch was tested. E.g. unit tests, integration tests, manual tests)
There are no code changes; only doc changes.
Please review https://spark.apache.org/contributing.html before opening a pull request.
Closes#24787 from dmatrix/br-orc-ds.doc.changes.
Authored-by: Jules Damji <dmatrix@comcast.net>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
Add AL2 license to metadata of all .md files.
This seemed to be the tidiest way as it will get ignored by .md renderers and other tools. Attempts to write them as markdown comments revealed that there is no such standard thing.
## How was this patch tested?
Doc build
Closes#24243 from srowen/SPARK-26918.
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?
Avro is built-in but external data source module since Spark 2.4 but `from_avro` and `to_avro` APIs not yet supported in pyspark.
In this PR I've made them available from pyspark.
## How was this patch tested?
Please see the python API examples what I've added.
cd docs/
SKIP_SCALADOC=1 SKIP_RDOC=1 SKIP_SQLDOC=1 jekyll build
Manual webpage check.
Closes#23797 from gaborgsomogyi/SPARK-26856.
Authored-by: Gabor Somogyi <gabor.g.somogyi@gmail.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
## 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?
Fix Typos.
## How was this patch tested?
NA
Closes#23145 from kjmrknsn/docUpdate.
Authored-by: Keiji Yoshida <kjmrknsn@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.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?
1. Split the main page of sql-programming-guide into 7 parts:
- Getting Started
- Data Sources
- Performance Turing
- Distributed SQL Engine
- PySpark Usage Guide for Pandas with Apache Arrow
- Migration Guide
- Reference
2. Add left menu for sql-programming-guide, keep first level index for each part in the menu.
## How was this patch tested?
Local test with jekyll build/serve.
Closes#22746 from xuanyuanking/SPARK-24499.
Authored-by: Yuanjian Li <xyliyuanjian@gmail.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
2018-10-18 11:59:06 -07:00
Renamed from docs/avro-data-source-guide.md (Browse further)
