Hadoop
- 2. INTRODUCTION Apache Hadoop is an open-source software framework that supports data-intensive distributed applications, licensed under the Apache v2 license. It enables applications to work with thousands of computation- independent computers and petabytes of data. Hadoop was derived from Google's MapReduce and Google File System (GFS) papers. Hadoop is implemented in Java.
- 3. WHY HADOOP Need to process Multi Petabyte Datasets It is expensive to build reliability in each application Nodes failure is expected and Hadoop can help Number of nodes is not constant Efficient, reliable, Open Source Apache License Workloads are IO bound and not CPU bound
- 4. WHO USES HADOOP Amazon/A9 Facebook Google IBM Joost Last.fm New York Times PowerSet Veoh Yahoo!
- 5. COMMODITY HARDWARE Typically in 2 level architecture Nodes are commodity PCs 30-40 nodes/rack Uplink from rack is 3-4 gigabit Rack-internal is one gigabit
- 7. HDFS (HADOOP DISTRIBUTED FILE SYSTEM) Very Large Distributed File System 10K nodes, 100 million files, 10 PB Assumes Commodity Hardware Files are replicated to handle hardware failure Detect failures and recovers from them Optimized for Batch Processing Data locations exposed so that computations can move to where data resides Provides very high aggregate bandwidth User Space, runs on heterogeneous OS Single Namespace for entire cluster Data Coherency Write-once-read-many access model Client can only append to existing files Files are broken up into blocks Typically 128 MB block size Each block replicated on multiple Data Nodes Intelligent Client Client can find location of blocks Client accesses data directly from Data Node
- 8. NAME NODE METADATA Meta-data in Memory The entire metadata is in main memory No demand paging of meta-data Types of Metadata List of files List of Blocks for each file List of Data Nodes for each block File attributes, e.g. creation time, replication factor, etc. Transaction Log Records file creations, file deletions, etc. is kept here
- 9. DATA NODE Block Server Stores data in the local file system (e.g. ext3) Stores meta-data of a block (e.g. CRC) Serves data and meta-data to Clients Block Report Periodically sends a report of all existing blocks to the Name Node Facilitates Pipelining of Data Forwards data to other specified Data Nodes
- 10. BLOCK PLACEMENT Block Placement Strategy One replica on local node Second replica on a remote rack Third replica on same remote rack Additional replicas are randomly placed Clients read from nearest replica
- 11. DATA CORRECTNESS Use Checksums to validate data Use CRC32, etc. File Creation Client computes checksum per 512 byte Data Node stores the checksum File access Client retrieves the data and checksum from Data Node If Validation fails, Client tries other replicas
- 12. NAMENODE FAILURE A single point of failure Transaction Log stored in multiple directories A directory on the local file system A directory on a remote file system (NFS/CIFS)
- 13. DATA PIPELINING Client retrieves a list of DataNodes on which to place replicas of a block Client writes block to the first DataNode The first DataNode forwards the data to the next DataNode in the Pipeline When all replicas are written, the Client moves on to write the next block in file Rebalancer is used to ensure that the % disk full on DataNodes are similar Usually run when new DataNodes are added Cluster is online when Rebalancer is active Rebalancer is throttled to avoid network congestion Command line tool
- 14. HADOOP MAP/REDUCE The Map-Reduce programming model Framework for distributed processing of large data sets Pluggable user code runs in generic framework Common design pattern in data processing cat * | grep | sort | unique -c | cat > file input | map | shuffle | reduce | output Useful for: Log processing Web search indexing Ad-hoc queries, etc.