Spark 2.1

Reynold Xin announces Apache Spark 2.1:

  • Structured Streaming

    Introduced in Spark 2.0, Structured Streaming is a high-level API for building continuous applications. The main goal is to make it easier to build end-to-end streaming applications, which integrate with storage, serving systems, and batch jobs in a consistent and fault-tolerant way.

    • Event-time watermarks: This change lets applications hint to the system when events are considered “too late” and allows the system to bound internal state tracking late events.

    • Support for all file-based formats and all file-based features: With these improvements, Structured Streaming can read and write all file-based formats, e.g. JSON, text, Avro, CSV. In addition, all file-based features—e.g. partitioned files and bucketing—are supported on all formats.

    • Apache Kafka 0.10: This adds native support for Kafka 0.10, including manual assignment of starting offsets and rate limiting.

This is a pretty hefty release.  Click through to read the whole thing.

Related Posts

Auto ML With SQL Server 2019 Big Data Clusters

Marco Inchiosa has a model scenario for using Big Data Clusters to scale out a machine learning problem: H2O provides popular open source software for data science and machine learning on big data, including Apache SparkTM integration. It provides two open source python AutoML classes: h2o.automl.H2OAutoML and pysparkling.ml.H2OAutoML. Both APIs use the same underlying algorithm implementations, […]

Read More

Converting CSV To ORC

Mark Litwintschik investigates whether Spark is faster at converting CSV files to ORC format than Hive or Presto: Spark, Hive and Presto are all very different code bases. Spark is made up of 500K lines of Scala, 110K lines of Java and 40K lines of Python. Presto is made up of 600K lines of Java. […]

Read More

Categories

January 2017
MTWTFSS
« Dec Feb »
 1
2345678
9101112131415
16171819202122
23242526272829
3031