In certain Apache Hadoop use cases we want to get the checksum of files stored in HDFS. This is specifically useful when we are moving data from/to hdfs to verify the file was transferred correctly. Earlier there was no easy way to compare that but starting Apache Hadoop 3.1 we can compare the checksums of a file stored in hdfs and a file stored locally. HDFS-13056 The default checksum algorithm for hdfs chunks is CRC32C. A client can override it by overriding dfs.checksum.type (can be either CRC32 or CRC32C). This is not a cryptographically strong checksum, however it can be used for quick comparison. When we run the checksum command (hdfs dfs -checksum) for a hdfs file it calculates MD5 of MD5 of checksums of individual chunks (each chunk is typically 512 bytes long). However this is not very useful for comparison with a local copy. Example For example, the below command computes the checksum of the file hadoop-common-2.7.3.2.6.3.0-SNAPSHOT.jar stored in HDFS: hdfs dfs -checksum /tmp/hadoop-common-2.7.3.2.6.3.0-SNAPSHOT.jar /tmp/hadoop-common-2.7.3.2.6.3.0-SNAPSHOT.jar MD5-of-0MD5-of-512CRC32C 000002000000000000000000c16859d1d071c6b1ffc9c8557d4909f1 However this checksum is not easily comparable to that of a local copy. Instead we can calculate the CRC32C checksum of the whole file by adding -Ddfs.checksum.combine.mode=COMPOSITE_CRC to same command: bin/hdfs dfs -Ddfs.checksum.combine.mode=COMPOSITE_CRC -checksum /tmp/hadoop-common-2.7.3.2.6.3.0-SNAPSHOT.jar /tmp/hadoop-common-2.7.3.2.6.3.0-SNAPSHOT.jar COMPOSITE-CRC32C 3799db55 Property dfs.checksum.combine.mode=COMPOSITE_CRC tells hdfs to calculate combined CRC of individual CRCs instead of calculating MD5-of-Md5-of-Crcs. It is important to note here that we can calculate checksum of type CRC32C or CRC32 for a hdfs file depending upon how it was originally written. For example we can't calculate CRC32 for file in above example as its chunks was originally written with CRC32C checksums. If we want to get CRC32 of above file we need to specify dfs.checksum.type as CRC32 while writing that file. hdfs dfs -Ddfs.checksum.type=CRC32 -put hadoop-common-2.7.3.2.6.3.0-SNAPSHOT.jar /tmp hdfs dfs -checksum /tmp/hadoop-common-2.7.3.2.6.3.0-SNAPSHOT.jar /tmp/hadoop-common-2.7.3.2.6.3.0-SNAPSHOT.jar MD5-of-0MD5-of-512CRC32 0000020000000000000000009f26e871c80d4cbd78b8d42897e5b364 hdfs dfs -Ddfs.checksum.combine.mode=COMPOSITE_CRC -checksum /tmp/hadoop-common-2.7.3.2.6.3.0-SNAPSHOT.jar /tmp/hadoop-common-2.7.3.2.6.3.0-SNAPSHOT.jar COMPOSITE-CRC32 c1ddb422 This checksum can be easily compared to checksum of same file in local file system with the crc32 command. crc32 hadoop-common-2.7.3.2.6.3.0-SNAPSHOT.jar c1ddb422 ... View more

Ozone is an Object store for Hadoop. It is a redundant, distributed object store built by leveraging primitives present in HDFS. Below are some key features of ozone: A Hadoop compatible file system called Ozone File system that allows programs like Hive or Spark to run against Ozone without any modifications. Ozone supports RPC and REST API for accessing the store. Built to support billions of keys in distributed environment. Ozone can run concurrently with HDFS. Like many other object stores, Ozone has a notion of volume. Only Administrators can create Volumes. Users create buckets in the volumes. To store data inside a bucket, users create keys. An ozone file system allows other Hadoop ecosystem applications like Hive and Spark to use ozone. Once a bucket is created, it is trivial to create an ozone file system. A 10-thousand foot view of Ozone OzoneManager (Om) acts as namespace manager. All ozone entities like volumes, buckets and keys are managed by Om. Om talks to an independent block manager (Storage Container Manager, SCM) to get blocks and passes it on to the Ozone client. SCM: Storage Container Manager is the block and cluster manager for Ozone. Block: Blocks are similar to blocks in HDFS. They are replicated blocks of data. These components map very closely to the existing HDFS NameNode and DataNodes. The most significant difference is the presence of a block manager, SCM. Using Ozone The easiest way to run ozone is to try it out using the docker. To build Ozone from source, please checkout the hadoop sources from github. Then checkout the ozone branch, HDFS-7240 and build it. git checkout HDFS-7240 You can build ozone by running the following build command. mvn clean package -DskipTests=true -Dmaven.javadoc.skip=true -Pdist -Phdsl -Dtar -DskipShade skipShade is just to make compilation faster and not really required. Running Ozone via Docker This assumes that you have a running docker setup on the machine. Please run following commands to see ozone in action. Go to the directory where the docker compose files exist. cd hadoop-dist/target/compose/ozone Start ozone. docker-compose up -d Log into the datanode container docker exec -it ozone_datanode_1 bash Run the ozone load generator ./bin/oz freon Take a look at OzoneManager UI, to see all the requests made by Freon http://localhost:9874/ Congratulations! on your first ozone deployment. In the next part of this tutorial we will cover oz command shell and look at how to use ozone to store files. ... View more

screen-shot-2017-02-15-at-23048-pm.pngWhile opening Zeppelin UI on HD2.5 (on AWS) i don't see any welcome page. When i create a new notebook nothing happens. Also search box "Search your notebook" is also disabled. zeppelin.anonymous.allowed is set to true. There is no Error/Exception in logs. ... View more