Distributed Filesystems
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1 Distributed Filesystems Amir H. Payberah Swedish Institute of Computer Science April 8, 2014 Amir H. Payberah (SICS) Distributed Filesystems April 8, / 32
2 What is Filesystem? Controls how data is stored in and retrieved from disk. Amir H. Payberah (SICS) Distributed Filesystems April 8, / 32
3 What is Filesystem? Controls how data is stored in and retrieved from disk. Amir H. Payberah (SICS) Distributed Filesystems April 8, / 32
4 Distributed Filesystems When data outgrows the storage capacity of a single machine: partition it across a number of separate machines. Distributed filesystems: manage the storage across a network of machines. Amir H. Payberah (SICS) Distributed Filesystems April 8, / 32
5 Amir H. Payberah (SICS) Distributed Filesystems April 8, / 32
6 HDFS Hadoop Distributed FileSystem Appears as a single disk Runs on top of a native filesystem, e.g., ext3 Fault tolerant: can handle disk crashes, machine crashes,... Based on Google s filesystem GFS Amir H. Payberah (SICS) Distributed Filesystems April 8, / 32
7 HDFS is Good for... Storing large files Terabytes, Petabytes, etc MB or more per file. Streaming data access Data is written once and read many times. Optimized for batch reads rather than random reads. Cheap commodity hardware No need for super-computers, use less reliable commodity hardware. Amir H. Payberah (SICS) Distributed Filesystems April 8, / 32
8 HDFS is Not Good for... Low-latency reads High-throughput rather than low latency for small chunks of data. HBase addresses this issue. Large amount of small files Better for millions of large files instead of billions of small files. Multiple writers Single writer per file. Writes only at the end of file, no-support for arbitrary offset. Amir H. Payberah (SICS) Distributed Filesystems April 8, / 32
9 HDFS Daemons (1/2) HDFS cluster is manager by three types of processes. Namenode Manages the filesystem, e.g., namespace, meta-data, and file blocks Metadata is stored in memory. Amir H. Payberah (SICS) Distributed Filesystems April 8, / 32
10 HDFS Daemons (1/2) HDFS cluster is manager by three types of processes. Namenode Manages the filesystem, e.g., namespace, meta-data, and file blocks Metadata is stored in memory. Datanode Stores and retrieves data blocks Reports to Namenode Runs on many machines Amir H. Payberah (SICS) Distributed Filesystems April 8, / 32
11 HDFS Daemons (1/2) HDFS cluster is manager by three types of processes. Namenode Manages the filesystem, e.g., namespace, meta-data, and file blocks Metadata is stored in memory. Datanode Stores and retrieves data blocks Reports to Namenode Runs on many machines Secondary Namenode Only for checkpointing. Not a backup for Namenode Amir H. Payberah (SICS) Distributed Filesystems April 8, / 32
12 HDFS Daemons (2/2) Amir H. Payberah (SICS) Distributed Filesystems April 8, / 32
13 Files and Blocks (1/3) Files are split into blocks. Blocks Single unit of storage: a contiguous piece of information on a disk. Transparent to user. Managed by Namenode, stored by Datanode. Blocks are traditionally either 64MB or 128MB: default is 64MB. Amir H. Payberah (SICS) Distributed Filesystems April 8, / 32
14 Files and Blocks (2/3) Why is a block in HDFS so large? Amir H. Payberah (SICS) Distributed Filesystems April 8, / 32
15 Files and Blocks (2/3) Why is a block in HDFS so large? To minimize the cost of seeks. Time to read a block = seek time + transfer time seektime Keeping the ratio transfertime small: we are reading data from the disk almost as fast as the physical limit imposed by the disk. Example: if seek time is 10ms and the transfer rate is 100MB/s, to make the seek time 1% of the transfer time, we need to make the block size around 100MB. Amir H. Payberah (SICS) Distributed Filesystems April 8, / 32
16 Files and Blocks (3/3) Same block is replicated on multiple machines: default is 3 Replica placements are rack aware. 1st replica on the local rack. 2nd replica on the local rack but different machine. 3rd replica on the different rack. Namenode determines replica placement. Amir H. Payberah (SICS) Distributed Filesystems April 8, / 32
17 HDFS Client Client interacts with Namenode To update the Namenode namespace. To retrieve block locations for writing and reading. Client interacts directly with Datanode To read and write data. Namenode does not directly write or read data. Amir H. Payberah (SICS) Distributed Filesystems April 8, / 32
18 HDFS Write 1. Create a new file in the Namenode s Namespace; calculate block topology. 2, 3, 4. Stream data to the first, second and third node. 5, 6, 7. Success/failure acknowledgment. Amir H. Payberah (SICS) Distributed Filesystems April 8, / 32
19 HDFS Read 1. Retrieve block locations. 2, 3. Read blocks to re-assemble the file. Amir H. Payberah (SICS) Distributed Filesystems April 8, / 32
20 Namenode Memory Concerns For fast access Namenode keeps all block metadata in-memory. Will work well for clusters of 100 machines. Changing block size will affect how much space a cluster can host. 64MB to 128MB will reduce the number of blocks and increase the space that Namenode can support. Amir H. Payberah (SICS) Distributed Filesystems April 8, / 32
21 HDFS Federation Hadoop 2+ Each Namenode will host part of the blocks. A Block Pool is a set of blocks that belong to a single namespace. Support for machine clusters. Amir H. Payberah (SICS) Distributed Filesystems April 8, / 32
22 Namenode Fault-Tolerance (1/2) Namenode is a single point of failure. If Namenode crashes then cluster is down. Amir H. Payberah (SICS) Distributed Filesystems April 8, / 32
23 Namenode Fault-Tolerance (1/2) Namenode is a single point of failure. If Namenode crashes then cluster is down. Secondary Namenode periodically merges the namespace image and log and a persistent record of it written to disk (checkpointing). But, the state of the secondary Namenode lags that of the primary: does not provide high-availability of the filesystem Amir H. Payberah (SICS) Distributed Filesystems April 8, / 32
24 Namenode Fault-Tolerance (2/2) High availability Namenode. Hadoop 2+ Active standby is always running and takes over in case main Namenode fails. Amir H. Payberah (SICS) Distributed Filesystems April 8, / 32
25 Summary Good for large files. Streaming access rather than random access. Daemons: Namenode, Secondary Namenode, and Datanode Amir H. Payberah (SICS) Distributed Filesystems April 8, / 32
26 HDFS Installation and Shell Amir H. Payberah (SICS) Distributed Filesystems April 8, / 32
27 HDFS Installation Three options Local (Standalone) Mode Pseudo-Distributed Mode Fully-Distributed Mode Amir H. Payberah (SICS) Distributed Filesystems April 8, / 32
28 Installation - Local Default configuration after the download. Executes as a single Java process. Works directly with local filesystem. Useful for debugging. Amir H. Payberah (SICS) Distributed Filesystems April 8, / 32
29 Installation - Pseudo-Distributed (1/6) Still runs on a single node. Each daemon runs in its own Java process. Namenode Secondary Namenode Datanode Configuration files: hadoop-env.sh core-site.xml hdfs-site.xml Amir H. Payberah (SICS) Distributed Filesystems April 8, / 32
30 Installation - Pseudo-Distributed (2/6) Specify environment variables in hadoop-env.sh export JAVA_HOME=/opt/jdk1.7.0_51 Amir H. Payberah (SICS) Distributed Filesystems April 8, / 32
31 Installation - Pseudo-Distributed (3/6) Specify location of Namenode in core-site.sh <property> <name>fs.defaultfs</name> <value>hdfs://localhost:8020</value> <description>namenode URI</description> </property> Amir H. Payberah (SICS) Distributed Filesystems April 8, / 32
32 Installation - Pseudo-Distributed (4/6) Configurations of Namenode in hdfs-site.sh Path on the local filesystem where the Namenode stores the namespace and transaction logs persistently. <property> <name>dfs.namenode.name.dir</name> <value>/opt/hadoop-2.2.0/hdfs/namenode</value> <description>description...</description> </property> Amir H. Payberah (SICS) Distributed Filesystems April 8, / 32
33 Installation - Pseudo-Distributed (5/6) Configurations of Secondary Namenode in hdfs-site.sh Path on the local filesystem where the Secondary Namenode stores the temporary images to merge. <property> <name>dfs.namenode.checkpoint.dir</name> <value>/opt/hadoop-2.2.0/hdfs/secondary</value> <description>description...</description> </property> Amir H. Payberah (SICS) Distributed Filesystems April 8, / 32
34 Installation - Pseudo-Distributed (6/6) Configurations of Datanode in hdfs-site.sh Comma separated list of paths on the local filesystem of a Datanode where it should store its blocks. <property> <name>dfs.datanode.data.dir</name> <value>/opt/hadoop-2.2.0/hdfs/datanode</value> <description>description...</description> </property> Amir H. Payberah (SICS) Distributed Filesystems April 8, / 32
35 Start HDFS and Test Format the Namenode directory (do this only once, the first time). hdfs namenode -format Amir H. Payberah (SICS) Distributed Filesystems April 8, / 32
36 Start HDFS and Test Format the Namenode directory (do this only once, the first time). hdfs namenode -format Start the Namenode, Secondary namenode and Datanode daemons. hadoop-daemon.sh start namenode hadoop-daemon.sh start secondarynamenode hadoop-daemon.sh start datanode jps Amir H. Payberah (SICS) Distributed Filesystems April 8, / 32
37 Start HDFS and Test Format the Namenode directory (do this only once, the first time). hdfs namenode -format Start the Namenode, Secondary namenode and Datanode daemons. hadoop-daemon.sh start namenode hadoop-daemon.sh start secondarynamenode hadoop-daemon.sh start datanode jps Verify the deamons are running: Namenode: Secondary Namenode: Datanode: Amir H. Payberah (SICS) Distributed Filesystems April 8, / 32
38 HDFS Shell hdfs dfs -<command> -<option> <path> Amir H. Payberah (SICS) Distributed Filesystems April 8, / 32
39 HDFS Shell hdfs dfs -<command> -<option> <path> hdfs dfs -ls / hdfs dfs -ls file:///home/big hdfs dfs -ls hdfs://localhost/ hdfs dfs -cat /dir/file.txt hdfs dfs -cp /dir/file1 /otherdir/file2 hdfs dfs -mv /dir/file1 /dir2/file2 hdfs dfs -mkdir /newdir hdfs dfs -put file.txt /dir/file.txt # can also use copyfromlocal hdfs dfs -get /dir/file.txt file.txt # can also use copytolocal hdfs dfs -rm /dir/filetodelete hdfs dfs -help Amir H. Payberah (SICS) Distributed Filesystems April 8, / 32
40 Questions? Amir H. Payberah (SICS) Distributed Filesystems April 8, / 32
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