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## What changes were proposed in this pull request? Implement an image schema datasource. This image datasource support: - partition discovery (loading partitioned images) - dropImageFailures (the same behavior with `ImageSchema.readImage`) - path wildcard matching (the same behavior with `ImageSchema.readImage`) - loading recursively from directory (different from `ImageSchema.readImage`, but use such path: `/path/to/dir/**`) This datasource **NOT** support: - specify `numPartitions` (it will be determined by datasource automatically) - sampling (you can use `df.sample` later but the sampling operator won't be pushdown to datasource) ## How was this patch tested? Unit tests. ## Benchmark I benchmark and compare the cost time between old `ImageSchema.read` API and my image datasource. **cluster**: 4 nodes, each with 64GB memory, 8 cores CPU **test dataset**: Flickr8k_Dataset (about 8091 images) **time cost**: - My image datasource time (automatically generate 258 partitions): 38.04s - `ImageSchema.read` time (set 16 partitions): 68.4s - `ImageSchema.read` time (set 258 partitions): 90.6s **time cost when increase image number by double (clone Flickr8k_Dataset and loads double number images)**: - My image datasource time (automatically generate 515 partitions): 95.4s - `ImageSchema.read` (set 32 partitions): 109s - `ImageSchema.read` (set 515 partitions): 105s So we can see that my image datasource implementation (this PR) bring some performance improvement compared against old`ImageSchema.read` API. Closes #22328 from WeichenXu123/image_datasource. Authored-by: WeichenXu <weichen.xu@databricks.com> Signed-off-by: Xiangrui Meng <meng@databricks.com> |
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lib | ||
pyspark | ||
test_coverage | ||
test_support | ||
.coveragerc | ||
.gitignore | ||
MANIFEST.in | ||
pylintrc | ||
README.md | ||
run-tests | ||
run-tests-with-coverage | ||
run-tests.py | ||
setup.cfg | ||
setup.py |
Apache Spark
Spark is a fast and general cluster computing system for Big Data. It provides high-level APIs in Scala, Java, Python, and R, and an optimized engine that supports general computation graphs for data analysis. It also supports a rich set of higher-level tools including Spark SQL for SQL and DataFrames, MLlib for machine learning, GraphX for graph processing, and Spark Streaming for stream processing.
Online Documentation
You can find the latest Spark documentation, including a programming guide, on the project web page
Python Packaging
This README file only contains basic information related to pip installed PySpark. This packaging is currently experimental and may change in future versions (although we will do our best to keep compatibility). Using PySpark requires the Spark JARs, and if you are building this from source please see the builder instructions at "Building Spark".
The Python packaging for Spark is not intended to replace all of the other use cases. This Python packaged version of Spark is suitable for interacting with an existing cluster (be it Spark standalone, YARN, or Mesos) - but does not contain the tools required to set up your own standalone Spark cluster. You can download the full version of Spark from the Apache Spark downloads page.
NOTE: If you are using this with a Spark standalone cluster you must ensure that the version (including minor version) matches or you may experience odd errors.
Python Requirements
At its core PySpark depends on Py4J (currently version 0.10.7), but some additional sub-packages have their own extra requirements for some features (including numpy, pandas, and pyarrow).