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Apache Spark vs Vespa: What are the differences?
<Apache Spark vs. Vespa>
1. **Architecture**: Apache Spark is primarily a distributed data processing framework, focusing on data processing, analytics, and machine learning tasks. Vespa, on the other hand, is a scalable, real-time big data serving platform optimized for many small lookups and updates, making it suitable for serving applications such as recommendation systems and search engines.
2. **Scalability**: Apache Spark is designed for processing and analyzing large datasets in a distributed manner, making it suitable for big data applications. Vespa is optimized for handling many small, latency-sensitive queries and updates, making it more suitable for real-time serving applications.
3. **Use Cases**: Apache Spark is commonly used for tasks such as data processing, analytics, and machine learning, where large-scale data processing is required. Vespa is more suited for applications that require real-time, low-latency data serving, such as personalized recommendations or search engines.
4. **Programming Models**: Apache Spark provides a variety of APIs and libraries for building data processing and analytics workflows, including SQL, streaming, and machine learning libraries. Vespa offers a specialized query language and APIs tailored for building data serving applications that require real-time responses and low latencies.
5. **Community Support**: Apache Spark has a large and active community of developers contributing to the project, providing a wide range of resources, tutorials, and support. Vespa, while also open source, has a smaller community focused on real-time serving applications.
6. **Deployment**: Apache Spark can be deployed on a variety of cluster managers and cloud platforms, giving users flexibility in how they deploy and manage their Spark applications. Vespa, on the other hand, is designed to be deployed as a self-contained system, optimized for running on a cluster of nodes to provide high availability and low latency for serving applications.
In Summary, Apache Spark and Vespa differ in their architecture, scalability, use cases, programming models, community support, and deployment options, catering to different needs in the big data and real-time serving domains.
Advice on Apache Spark and Vespa
Nilesh Akhade
Technical Architect at Self Employed · | 5 upvotes · 558.1K views
We have a Kafka topic having events of type A and type B. We need to perform an inner join on both type of events using some common field (primary-key). The joined events to be inserted in Elasticsearch.
In usual cases, type A and type B events (with same key) observed to be close upto 15 minutes. But in some cases they may be far from each other, lets say 6 hours. Sometimes event of either of the types never come.
In all cases, we should be able to find joined events instantly after they are joined and not-joined events within 15 minutes.
Replies (2)
lvhuyen
The first solution that came to me is to use upsert to update ElasticSearch:
- Use the primary-key as ES document id
- Upsert the records to ES as soon as you receive them. As you are using upsert, the 2nd record of the same primary-key will not overwrite the 1st one, but will be merged with it.
Cons: The load on ES will be higher, due to upsert.
To use Flink:
- Create a KeyedDataStream by the primary-key
- In the ProcessFunction, save the first record in a State. At the same time, create a Timer for 15 minutes in the future
- When the 2nd record comes, read the 1st record from the State, merge those two, and send out the result, and clear the State and the Timer if it has not fired
- When the Timer fires, read the 1st record from the State and send out as the output record.
- Have a 2nd Timer of 6 hours (or more) if you are not using Windowing to clean up the State
Pro: if you have already having Flink ingesting this stream. Otherwise, I would just go with the 1st solution.
Akshaya Rawat
Senior Specialist Platform at Publicis Sapient · | 3 upvotes · 394.3K views
Please refer "Structured Streaming" feature of Spark. Refer "Stream - Stream Join" at https://spark.apache.org/docs/latest/structured-streaming-programming-guide.html#stream-stream-joins . In short you need to specify "Define watermark delays on both inputs" and "Define a constraint on time across the two inputs"
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Learn MorePros of Apache Spark
Pros of Vespa
Pros of Apache Spark
- Open-source61
- Fast and Flexible48
- One platform for every big data problem8
- Great for distributed SQL like applications8
- Easy to install and to use6
- Works well for most Datascience usecases3
- Interactive Query2
- Machine learning libratimery, Streaming in real2
- In memory Computation2
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Cons of Apache Spark
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Cons of Apache Spark
- Speed4
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What is Apache Spark?
Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.
What is Vespa?
Vespa is an engine for low-latency computation over large data sets. It stores and indexes your data such that queries, selection and processing over the data can be performed at serving time.
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What are some alternatives to Apache Spark and Vespa?
Hadoop
The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage.
Splunk
It provides the leading platform for Operational Intelligence. Customers use it to search, monitor, analyze and visualize machine data.
Cassandra
Partitioning means that Cassandra can distribute your data across multiple machines in an application-transparent matter. Cassandra will automatically repartition as machines are added and removed from the cluster. Row store means that like relational databases, Cassandra organizes data by rows and columns. The Cassandra Query Language (CQL) is a close relative of SQL.
Apache Beam
It implements batch and streaming data processing jobs that run on any execution engine. It executes pipelines on multiple execution environments.
Apache Flume
It is a distributed, reliable, and available service for efficiently collecting, aggregating, and moving large amounts of log data. It has a simple and flexible architecture based on streaming data flows. It is robust and fault tolerant with tunable reliability mechanisms and many failover and recovery mechanisms. It uses a simple extensible data model that allows for online analytic application.