I am not able to find a specific link that explains to me how the meta data is distributed in MAPR(File meta data). When I look at cloudera / hortonworks /apache hadoop I know the meta data is stored in namenode's memory which is then fetched to locate the nodes that holds the blocks. How does it work in MAPR is what I am trying to understand.
Any help would be greatly appreciated.
MapR natively implemented a Network File System (NFS) interface to MapR-FS so that any reads and writes from and to a file system, whether it be to a local file system, Network Attached Storage, or a Storage Area Network, can read and write data from and to MapR-FS.
This is also the reason MapR asks for a raw disk during the installation so that, it reformats the disk as MapR-FS.
Just came across this thread :
http://answers.mapr.com/questions/108/how-is-metadata-stored.html
Related
We are currently running spark jobs, store the data in HDFS and create Hive tables on top of it. Now, we are planning to migrate from HDFS to GFS to store the Hadoop data. Is it possible to use GFS as our Hadoop File System in our case, either by mounting or CIFS share or using GFS directly?
The following link talks about remote mounting of devices, but it mentions that it is not specific to any of the details.
https://www.cloudera.com/documentation/other/reference-architecture/topics/ra_storage_device_accepta...
Please refer me to any document that explains in detail about remote mounting of devices.
Currently, clickhouse stores data on
/var/lib/clickhouse
path and I've read It doesn't have support for deep storage.
By the way, does it have any configs for hdfs setup in config.xml file?
store clickhouse datadir into HDFS it's a really BAD idea ;)
cause HDFS not posix compatible file system, clickhouse will be extremly slow on this deployment variant
you can use https://github.com/jaykelin/clickhouse-hdfs-loader to load data from HDFS into clickhouse, and in near future https://clickhouse.yandex/docs/en/roadmap/ clickhouse may will be support PARQUET format for loading data
clickhouse have own solution for High Availability and Clusterization
please read
https://clickhouse.yandex/docs/en/operations/table_engines/replication/ and https://clickhouse.yandex/docs/en/operations/table_engines/distributed/
#MajidHajibaba
clickhouse designed initially for data locality, it means you have local disk and data will read from local disk as fast as possible
3 years later, S3 and HDFS as remote data storage with local caching is good implemented approach
look https://clickhouse.com/docs/en/engines/table-engines/mergetree-family/mergetree/#table_engine-mergetree-s3 fo details
look to cache_enabled and cache_path options
and https://clickhouse.com/docs/en/operations/storing-data/#configuring-hdfs
HDFS engine provides integration with Apache Hadoop ecosystem by allowing to manage data on HDFSvia ClickHouse. This engine is similar to the File and URL engines, but provides Hadoop-specific features.
https://clickhouse.yandex/docs/ru/operations/table_engines/hdfs/
I am new to Hadoop and much interested in Hadoop Administration,so i tried to install Hadoop 2.2.0 in Ubuntu 12.04 as pseudo distributed mode and installed successfully and run some example jar files also ,now i am trying learn further ,trying to learn data back up and recovery part now,can anyone tell ways to take data back back up and recovery it in hadoop 2.2.0 ,and also please suggest any good books for Hadoop Adminstration and steps to learn Hadoop Adminstration.
Thanks in Advance.
There is no classic backup and recovery functionality in Hadoop. There are several reasons for this:
HDFS uses block level replication for data protection via redundancy.
HDFS scales out massively in size, and it is becoming more economic to backup to disk, rather than tape.
The size of "Big Data" doesn't lend itself to being easily backed up.
Instead of backups, Hadoop uses data replication. Internally, it creates multiple copies of each block of data (by default, 3 copies). It also has a function called 'distcp', which allows you to replicate copies of data between clusters. This is what's typically done for "backups" by most Hadoop operators.
Some companies, like Cloudera, are incorporating the distcp tool into creating a 'backup' or 'replication' service for their distribution of Hadoop. It operates against a specific directory in HDFS, and replicates it to another cluster.
If you really wanted to create a backup service for Hadoop, you can create one manually yourself. You would need some mechanism of accessing the data (NFS gateway, webFS, etc), and could then use tape libraries, VTLs, etc. to create backups.
I am currently "playing around" with Hadoop in a VM (CDH4.1.3 image from cloudera). What I am wondering about is the following (and the documentation did not really help me in that regard).
Following the tutorial, I would format a NameNode first - OK, that is already done if one uses the cloudera image. Likewise the HDFS file structure is already present. In the hdfs-site.xml the datanode data dir is set to:
/var/lib/hadoop-hdfs/cache/${user.name}/dfs/data
which is obviously where the blocks are supposed to be copied to in a real distributed setting. In the cloudera tutorial, one is told to create hdfs "home directories" for each user (/users/<username>), which I do not understand what they are for. Are they just for local test-runs in a single-node setup?
Say I really had petabytes of data on type not fitting into my local storage. This data would have to be distributed straight away, rendering a local "home directory" entirely useless.
Could someone tell me, just to give me an intuition, how a real Hadoop workflow with massive data would look like? What kind of distinct nodes would I have running for a start?
There's the master (JobTracker) with its slave file (where would I put that) allowing the master to resolve all the DataNodes. Then there is my NameNode that keeps track of where the block IDs are stored. The DataNodes are also carry TaskTracker responsibility. In the config files, the NameNode's URI is included -- am I correct so far? Then there is still the ${user.name} variable in the configuration which apparently, if I understood it right, has something to do with WebHDFS, which would also be great if someone could explain to me. In the running examples, the directions tend to be hardcoded to
/var/lib/hadoop-hdfs/cache/1/dfs/data, /var/lib/hadoop-hdfs/cache/2/dfs/data and so on.
So, back to the example: Say, I have my tape and want to import data into my HDFS (and I am required to stream data into the filesystem because I lack the local storage to save it locally on a single machine). Where would I start with the migration process? On an arbitrary DataNode? On the NameNode that distributes the chunks? After all, I cannot assume the data just to "be there", because the name node has to be aware of the block IDs.
It would be great if someone could shortly elaborate on these topics:
What is the home directory really for?
Do I migrate data to the home directory first and to the real distributed system afterwards?
How does WebHDFS work and what role does it play with regards to the user.name variable
How would I migrate "big data" into my HDFS on the fly - or even if it's not big data, how do I populate my file system in a proper way (meaning, that the chunks get randomly distributed across the cluster?
What is the home directory really for?
You have a small confusion here. Just like /home exists for local filesystems on Linux, where users are given their own storage space, /users is a home mount ON the HDFS (Distributed FS). The tutorial needs you to administratively create a home directory for the user you wish to later be running data loads and queries as, such that they get adequate permissions and storage access onto the HDFS. The tutorial is not asking you to create these directories locally.
Do I migrate data to the home directory first and to the real distributed system afterwards?
I believe my above answer should clarify this for you. You should create your home directory on the HDFS, and then load all your data inside of that directory.
How does WebHDFS work and what role does it play with regards to the user.name variable
WebHDFS is one of the various ways to access HDFS. Regular clients to talk to HDFS require use of Java APIs. WebHDFS (and also HttpFs) techniques were added to HDFS to let other languages have their own set of APIs by providing a REST front-end to HDFS. WebHDFS allows user-authentication, to help persist the permission and security models.
How would I migrate "big data" into my HDFS on the fly - or even if it's not big data, how do I populate my file system in a proper way (meaning, that the chunks get randomly distributed across the cluster?
The large part of problem HDFS solves for you is that of managing distribution of data. When loading files or data streams to HDFS (via CLI tools, sinks from Apache Flume, etc.), the blocks are spread in an ideal distribution by HDFS itself, and the chunking is managed by it as well. All you need to do is use the user-side regular FileSystem style APIs and forget about what goes where underneath - its all managed for you.
Hadoop writes the intermediate results to the local disk and the results of the reducer to the HDFS. what does HDFS mean. What does it physically translate to
HDFS is the Hadoop Distributed File System. Physically, it is a program running on each node of the cluster that provides a file system interface very similar to that of a local file system. However, data written to HDFS is not just stored on the local disk but rather is distributed on disks across the cluster. Data stored in HDFS is typically also replicated, so the same block of data may appear on multiple nodes in the cluster. This provides reliable access so that one node's crashing or being busy will not prevent someone from being able to read any particular block of data from HDFS.
Check out http://en.wikipedia.org/wiki/Hadoop_Distributed_File_System#Hadoop_Distributed_File_System for more information.
As Chase indicated, HDFS is Hadoop Distributed File System.
If I may, I recommend this tutorial and video of how HDFS and the Map/Reduce framework works and will serve you as a guide into the world of Hadoop: http://www.cloudera.com/resource/introduction-to-apache-mapreduce-and-hdfs/