kafka data ingestion

A Platform Environment Introduction:1. System Information: Project Information System version: Ubuntu14.04.2 LTS \ \l User: ***** Password: ****** Java environment: Openjdk-7-jre Language: en_US. Utf-8,en_us:en Disk: Each VDA is the system disk (50G) and VDB is mounted in the/storage directory for the data disk (200G).Hc

Don't be afraid of file systems!Kafka relies heavily on file systems to store and cache messages. The traditional idea for hard drives is that hard drives are always slow, which makes many people wonder if file system-based architectures can provide superior performance. The actual speed of the hard drive depends entirely on the way it is used. A well-designed hard drive architecture can be as fast as memory.The linear write speed of the 6 7200-RPM SA

Flume is an excellent data acquisition component, some heavyweight, its nature is based on the query results of SQL statements assembled into OPENCSV format data, the default separator symbol is a comma (,), you can rewrite opencsv some classes to modify 1, download [Root@hadoop0 bigdata]# wget http://apache.fayea.com/flume/1.6.0/apache-flume-1.6.0-bin.tar.gz 2, decompression [Root@hadoop0 bigdata]# TAR-Z

Tags: ORACLE KAFKA OGGEnvironment:SOURCE side: oracle12.2 ogg for Oracle 12.3Target side: KAFKA ogg for Bigdata 12.3Synchronizing data from Oracle to Kafka via OGGSource-side configuration:1. Add additional logs for the tables to be synchronizeddblogin USERID [email protected], PASSWORD oggAdd Trandata scott.tab1Add Tr

URLS can begiven to allow fail-over. 3. Add Brokers (Cluster Expansion)Cluster expansion involves including brokers with new broker IDs in a Kafka Cluster. Typically, when you add new brokers to a cluster, they won't receive any data from existing topics until this tool is R UN to assign existing topics/partitions to the new brokers. The tool allows 2 options to make it easier to move some topics o

-dependencies.jar# another window$ nc-lk 9999# input data2. Receive Kafka Data and Count (WordCount) Packagecom.xiaoju.dqa.realtime_streaming;ImportJava.util.*;Importorg.apache.spark.SparkConf;ImportOrg.apache.spark.api.java.JavaSparkContext;Importorg.apache.spark.api.java.function.FlatMapFunction;ImportOrg.apache.spark.api.java.function.Function2;Importorg.apache.spark.api.java.function.PairFunction;Import

Speaking of headings, this is only a small part of the real-time architecture. Download the latest version flume:apache-flume-1.6.0-bin.tar.gz Unzip, modify Conf/flume-conf.properties name can write casually. What I currently achieve is to read the data from the directory to write to the Kafka, the principle of the east of the Internet a lot of, only to connect the code: a1.sources = R1 a1.sinks = K1 a1.cha

A Kafka cluster expansion is relatively simple, machine configuration is the same premise only need to change the configuration file in the Brokerid to a new start up. It is important to note that if the company intranet DNS changes are not very timely, the old machine needs to be added to the new server host, otherwise the controller server from ZK to get the domain name but not resolve the new machine address situation.Two after the cluster expansio

consumer configuration propertyagent.sources.kafkaSource.kafka.consumer.timeout.ms = 100#-------memorychannel related configuration-------------------------#Channel TypeAgent.channels.memoryChannel.type =Memory#event capacity for channel storageagent.channels.memorychannel.capacity=10000#Transaction Capacityagent.channels.memorychannel.transactioncapacity=1000#---------hdfssink related configuration------------------Agent.sinks.hdfsSink.type =HDFs#Note that we output to one of the following sub

Reprinted with the source: http://blog.csdn.net/honglei915/article/details/37564595 Do not fear file systems! Kafka relies heavily on the file system to store and cache messages. The traditional concept of hard disks is that hard disks are always slow, which makes many people doubt whether the file system-based architecture can provide excellent performance. In fact, the speed of a hard disk depends entirely on how it is used. A well-designed hard di

Kafka repeated consumption reasons Underlying root cause: data has been consumed, but offset has not been submitted. Cause 1: Forcibly kill the thread, causing the data after consumption, offset is not committed. Cause 2: Set offset to auto commit, close Kafka, if Call Consumer.unsubscribe () before close, it is possib