From 16dc9f344c08deee104090106cb0a537a90e33fc Mon Sep 17 00:00:00 2001 From: Rohan Bhanderi <rohan.bhanderi@sjsu.edu> Date: Fri, 23 Oct 2015 01:10:46 -0700 Subject: [PATCH] Fix typo "Received" to "Receiver" in streaming-kafka-integration.md Removed typo on line 8 in markdown : "Received" -> "Receiver" Author: Rohan Bhanderi <rohan.bhanderi@sjsu.edu> Closes #9242 from RohanBhanderi/patch-1. --- docs/streaming-kafka-integration.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/streaming-kafka-integration.md b/docs/streaming-kafka-integration.md index 5db39ae54a..ab7f0117c0 100644 --- a/docs/streaming-kafka-integration.md +++ b/docs/streaming-kafka-integration.md @@ -5,7 +5,7 @@ title: Spark Streaming + Kafka Integration Guide [Apache Kafka](http://kafka.apache.org/) is publish-subscribe messaging rethought as a distributed, partitioned, replicated commit log service. Here we explain how to configure Spark Streaming to receive data from Kafka. There are two approaches to this - the old approach using Receivers and Kafka's high-level API, and a new experimental approach (introduced in Spark 1.3) without using Receivers. They have different programming models, performance characteristics, and semantics guarantees, so read on for more details. ## Approach 1: Receiver-based Approach -This approach uses a Receiver to receive the data. The Received is implemented using the Kafka high-level consumer API. As with all receivers, the data received from Kafka through a Receiver is stored in Spark executors, and then jobs launched by Spark Streaming processes the data. +This approach uses a Receiver to receive the data. The Receiver is implemented using the Kafka high-level consumer API. As with all receivers, the data received from Kafka through a Receiver is stored in Spark executors, and then jobs launched by Spark Streaming processes the data. However, under default configuration, this approach can lose data under failures (see [receiver reliability](streaming-programming-guide.html#receiver-reliability). To ensure zero-data loss, you have to additionally enable Write Ahead Logs in Spark Streaming (introduced in Spark 1.2). This synchronously saves all the received Kafka data into write ahead logs on a distributed file system (e.g HDFS), so that all the data can be recovered on failure. See [Deploying section](streaming-programming-guide.html#deploying-applications) in the streaming programming guide for more details on Write Ahead Logs. -- GitLab