Spark

In this blog post, I will recount my experience working on the addition of the graph embeddings API to the GraphFrames library. I will start with a top-level overview of the vertex representation learning task and the existing approaches. Then, I will focus on generating embeddings from random walks. I will explain the intuition behind and the implementation details of Random Walks in GraphFrames, as well as the chosen trade-offs and limitations. Given a sequence of graph vertices, I will explain the possible ways to learn vertex representations from it. First, I will analyze the common approach of using the word2vec model and its limitations, especially the limitations of the Apache Spark ML Word2vec implementation. I will present the results of my analysis of alternative approaches and their respective advantages and disadvantages. Finally, I will explain the intuition behind the Hash2vec approach that I chose as a starting point for GraphFrames, as well as the details of its implementation in Scala and Spark. Finally, I will present an experimental comparison of Hash2vec and Word2vec embeddings and share my thoughts about future directions.

In this blog post, I will provide an end-to-end example of how to integrate JMH benchmarks for an Apache Spark-based library into an SBT build. I will cover aspects that are often poorly documented, such as setting up a Spark Session and datasets as shared resources, configuring driver memory for standalone Spark with JMH, and more. Additionally, as a bonus, I will demonstrate how to integrate benchmark results into the library’s documentation using the Typelevel Laika static site generator. While many of these steps may seem obvious to experienced JVM developers, I personally spent several hours figuring out how to configure them correctly. From what I have observed, the subject of Apache Spark with JMH is underexplored, and online searches did not yield much guidance. Thus, my hope is that this short post will help someone someday and save them a few hours of effort.

While Open Lakehouse platforms now natively support tables, geospatial data, vectors, and more, property graphs are still missing. In the age of AI and growing interest in Graph RAG, graphs are becoming especially relevant – there’s a need to deliver Knowledge Graphs to RAG systems, with standards, ETL, and frameworks for different scenarios. There’s a young project, Apache GraphAr (incubating), that aims to define a storage standard. For processing, there is a good tooling already. GraphFrames is like Spark for Iceberg – batch and scalable on distributed clusters; Kuzu is like DuckDB for Iceberg – fast, in-memory, and in-process; Apache HugeGraph is like ClickHouse or Doris for graphs – a standalone server for queries. I’m currently working also on graphframes-rs to bring Apache DataFusion and its ecosystem into this picture. All the pieces seem to be here—it just remains to put them together. More thoughts in the full post.

The question about why Apache Spark is "slow" is one of the most often questions I'm hearing from junior engineers and peoples I'm mentoring. While that is partially true, it should be clarified. TLDR – OSS Spark is a multi-purpose engine that is designed to handle different kinds of workloads. Under the hood of Spark is using a data-centric code generation but also it has some vectorization as well as option to fallbak to a pure Volcano-mode. Because of that Spark can be considred as a hybrid engine, that can benefit from all the approaches. But because of it's multi-purpose nature it will be almost always slower compared to pure vectorized engines like Trino on OLAP workloads on top of columnar data, except rare cases of big amount of nulls or deep branching in the query. In this blogpost I'm trying to explain the statement above.

In this blog post, I will provide a brief high-level overview of projects designed to accelerate Apache Spark by the native physical execution, including Databricks Photon, Apache Datafusion Comet, and Apache Gluten (incubating). I will explain the problems these projects aim to solve and their approaches. The main focus will be on the Comet project, particularly its internal architecture. Additionally, I will share my personal experience of making my first significant contribution to the project. This will include not only a description of the problem I solved and my solution but also insights into the overall contribution experience and the pull request review process.

Summary This blog post is a detailed story about how I ported a popular data quality framework, AWS Deequ, to Spark-Connect. Deequ is a very cool, reliable and scalable framework that allows to compute a lot of metrics, checks and anomaly detection suites on the data using Apache Spark cluster. But the Deequ core is a Scala library that uses a lot of low-level Apache Spark APIs for better performance, so it cannot be run directly on any of Spark-Connect environment. To solve this problem, I defined protobuf messages for all main structures of Deequ, like Check, Analyzer, AnomalyDetectionStrategy, etc., wrote a helper object that can re-create Deequ structures from the corresponding protobuf, and finally made a Spark-Connect native plugin that can process Deequ specific messages, construct DQ suits from them, compute the report, and return the result to the Spark-Connect client. I tested my solution with PySpark Connect 3.5.1, but it should work with any of the existing Spark-Connect clients (Spark-Connect Java/Scala, Spark-Connect Go, Spark-Connect Rust, Spark-Connect C#, etc). ...

This blog post presents a very detailed step-by-step guide on how to create a SparkConnect protocol extension in Java and call it from PySpark. It will also cover a topic about how to define all the necessary proto3 messages for it. At the end of this guide you will have a way to interact with Spark JVM from PySpark almost like you can with py4j in a non-connect version.

I recently had the opportunity to work on an open source project that implements a custom Apache Spark data source and associated logic for working with graph data. The code was written to work with Apache Spark 3.2.2. I am committed to extending support to multiple versions of Spark. In this blog post I want to show how the structure of such a project can be organized using Maven profiles.

In this post, I want to discuss the groundbreaking changes in the latest LTS release of the Databricks runtime. This release introduced Spark Connect as the default way to work with shared clusters. I will give a brief introduction to the topic of internal JVM calls and Spark Connect, provide examples of 3d-party OSS projects broken in 14.3, and try to understand the reasons for such a move by Databricks.

In this post, I will show you how to use information from the spark plan to track data lineage at the column level. This approach will also works with recently introduced SparkConnect.