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Apache Beam vs Luigi: What are the differences?
## Key Differences between Apache Beam and Luigi
Apache Beam and Luigi are both popular tools used for building data pipelines, but they have some key differences that distinguish them from each other.
1. **Execution Model**: Apache Beam utilizes a unified model for defining and executing batch and streaming data processing jobs, making it easier to write code that can run on different distributed processing backends like Apache Flink, Apache Spark, and Google Cloud Dataflow. On the other hand, Luigi is primarily focused on managing batch jobs and orchestrating dependencies between tasks in a linear fashion, which may limit its suitability for streaming data processing scenarios.
2. **Language Support**: Apache Beam supports multiple programming languages such as Java, Python, Go, and more, providing developers with flexibility in choosing the language they are most comfortable with for building data pipelines. In contrast, Luigi is mainly Python-based, which can be a limitation for organizations that require support for other languages in their data pipeline development.
3. **Community and Ecosystem**: Apache Beam has a larger community and ecosystem compared to Luigi, with extensive documentation, support, and third-party tools available for building and managing data pipelines. This broader ecosystem can be beneficial for developers looking to leverage existing solutions and best practices in their pipeline development efforts.
4. **Fault Tolerance**: Apache Beam offers robust fault tolerance capabilities through its processing backends, ensuring that data processing jobs can recover from failures and resume processing without data loss. While Luigi also provides some fault tolerance features, its focus on batch processing may not offer the same level of resiliency as Apache Beam in handling complex distributed data processing workflows.
5. **Scalability**: Apache Beam is designed with scalability in mind, allowing developers to easily scale their data processing jobs horizontally by adding more processing resources as needed. Luigi, on the other hand, may face limitations in scaling to handle large volumes of data or complex processing requirements, making it more suitable for smaller-scale data pipelines.
6. **Integration with External Systems**: Apache Beam provides seamless integration with various external systems and data sources, enabling developers to ingest and process data from a wide range of sources with ease. While Luigi also supports integration with external systems, its focus on simplicity and task dependency management may result in additional complexity when dealing with diverse data sources and systems.
In Summary, Apache Beam and Luigi differ in their execution models, language support, community size, fault tolerance, scalability, and integration with external systems, making each tool suitable for specific data pipeline development needs.
I am so confused. I need a tool that will allow me to go to about 10 different URLs to get a list of objects. Those object lists will be hundreds or thousands in length. I then need to get detailed data lists about each object. Those detailed data lists can have hundreds of elements that could be map/reduced somehow. My batch process dies sometimes halfway through which means hours of processing gone, i.e. time wasted. I need something like a directed graph that will keep results of successful data collection and allow me either pragmatically or manually to retry the failed ones some way (0 - forever) times. I want it to then process all the ones that have succeeded or been effectively ignored and load the data store with the aggregation of some couple thousand data-points. I know hitting this many endpoints is not a good practice but I can't put collectors on all the endpoints or anything like that. It is pretty much the only way to get the data.
For a non-streaming approach:
You could consider using more checkpoints throughout your spark jobs. Furthermore, you could consider separating your workload into multiple jobs with an intermittent data store (suggesting cassandra or you may choose based on your choice and availability) to store results , perform aggregations and store results of those.
Spark Job 1 - Fetch Data From 10 URLs and store data and metadata in a data store (cassandra) Spark Job 2..n - Check data store for unprocessed items and continue the aggregation
Alternatively for a streaming approach: Treating your data as stream might be useful also. Spark Streaming allows you to utilize a checkpoint interval - https://spark.apache.org/docs/latest/streaming-programming-guide.html#checkpointing
Pros of Apache Beam
- Open-source5
- Cross-platform5
- Portable2
- Unified batch and stream processing2
Pros of Luigi
- Hadoop Support5
- Python3
- Open soure1
