Overview of MapR-DB
MapR-DB is an enterprise-grade, high-performance, in-Hadoop, NoSQL (“Not Only SQL”) database management system. You can use it to add real-time, operational analytics capabilities to Hadoop.
MapR-DB as a Document Database
MapR-DB supports OJAI documents as a native data store. OJAI documents are stored in a compact binary format, not as plain ASCII text.
- Store data that is hierarchical and nested, and that evolves over time.
- Read and write individual document fields, subsets of fields, or whole documents from and to disk. To update individual fields or subsets of fields, there is no need to read entire documents, modify them, and then write the modified documents to disk.
- Build Java applications with the MapR-DB JSON API library, which is an implementation of the Open JSON Application Interface (OJAI), a Java API library for easily managing complex, evolving, hierarchical data. You can use many more data types than the standard types that JSON supports, create complex queries, and access JSON tables documents without connection or configuration objects..
- Filter query results within MapR-DB before results are returned to client applications.
- Run client applications on Linux, OS X, and Windows systems.
- Perform complex data analysis on your JSON data with Apache Drill or other analytical tools in real time without having to copy data to another cluster.
- Scale your data to span thousands of nodes.
- Control read and write access to single fields and subsets of fields within a JSON table by using access-control expressions (ACEs).
- Control the disk layout of single fields and subdocuments within JSON tables.
MapR-DB as a Wide-Column Database
MapR-DB also supports binary tables (wide-column tables that store data in binary format) as a native data store. MapR-DB binary tables are identical conceptually to tables in Apache HBase. If you're familiar with HBase tables, you'll be right at home with MapR-DB binary tables. In fact, your HBase applications can switch to using MapR-DB binary tables with no coding changes required.
Support for Table Replication
You can replicate changes (puts and deletes) to the data in one table to another table that is in a separate cluster or within the same cluster. Replicate entire tables, specific column families, and specific columns.
Tables that data is replicated from are called source tables, while tables that data is replicated to are called replicas.
The maximum number of replicas that a source table can replicate to is 64.
The maximum number of source tables that a replica can accept updates from is 64.
Clusters that data is replicated from are called source clusters. Clusters that data is replicated to are called destination clusters. A single cluster can be both a source cluster and a destination cluster, depending on the replication configuration in which the cluster participates.
Replication takes place between source and destination clusters. However, source clusters do not send data to nodes in the destination cluster directly. The replication stream (the data being pushed to the replicas) is consumed by one or more MapR gateways in the destination cluster. The gateways receive the updates from the source cluster, batch them, and apply them to the replica tables. Multiple gateways serve the purpose of both load balancing and failover.
Support for Elasticsearch
You can create external indexes for your MapR-DB binary tables by indexing columns, column families, or entire tables in Elasticsearch. When client applications update data in a source table, MapRDB replicates the update to the Elasticsearch type that is associated with it.
Updates to indexes happen in near real-time because individual updates to your MapR-DB source tables are replicated to Elasticsearch. There is no batching of updates, which would lead to recurring times where data is available in MapR-DB but not searchable in your indexes. Therefore, there is minimal latency between the availability of data in MapR-DB and the searchability of that data by end users.
The MapR Converged Data Platform does not include Elasticsearch, which you can get from https://www.elastic.co/. MapR-DB works with Elasticsearch version 2.2.
Support for Developing MapR-DB Clients in C
libhbase is a library of C APIs for creating and accessing Apache HBase
tables. The open-source version of this library is published on GitHub at https://github.com/mapr/libhbase.
MapR-DB includes a version of
libMapRClient that runs more efficiently and performs faster against
MapR-DB binary tables.
Support for HBase Java API Library
You can access MapR-DB binary tables with the Apache HBase Java API library. Code written for Apache HBase can be easily ported to use MapR-DB binary tables.
Advantages of MapR-DB's Architecture
One of the resulting advantages is that MapR-DB has no layers to pass through when performing operations on data. MapR-DB runs inside of the MFS process, which reads from and writes to disks directly. In contrast, Apache HBase running on the Hadoop file system (HDFS) must communicate with the HDFS process, which in turn must communicate with the ext3 file system, which itself ultimately writes data to disks. The approach taken by MapR-DB eliminates such process hops, duplicate caching, and needless abstractions, with the consequence of optimizing I/O operations on your data.
Another advantage is the absence of compaction delays that arise due to I/O storms as logged operations are merged with structures on disk. MapR-DB, like several other NoSQL databases, is a log-based database. Periodically, logged operations must be written to disk. In MapR-DB, tablets (called regions in Apache HBase) and smaller structures within them are stored partially as b-trees which together with write-ahead log (WAL) files comprise log-structured-merge trees. Write-ahead logs for the smaller structures within tablets are periodically restructured by rolling merge operations on the b-trees. Because MapR-DB performs these merges at small scales, applications running against MapR-DB see no significant effects on latency while the merges are taking place.
Containers reside in storage pools, and each storage pool can store many containers. The default container size is 32GB. For more information about containers, see Containers and the CLDB.
Each tablet of a table, along with its corresponding write-ahead log (WAL) files, b-trees, and other associated structures, is stored in one container. Each container (which can be from 16 to 32 GB in size) can store more than one tablet (which default in size to 4096 MB). The recommended practice is to use the default size for tablets and allow them to be split automatically. Massive tablets can affect synchronization of containers and load balancing across a cluster. Smaller tablets spread data better across more nodes.
There are two important advantages to storing tablets in containers: MapR clusters are extremely scalable and provide exceptional high availability for your data.
- MapR Cluster Scalability
The location of containers in a cluster is tracked by that cluster's container location database (CLDB). CLDBs are updated only when a container is moved, a node fails, or as a result of periodic block change reports. The update rate, even for very large clusters, is therefore relatively low. MapR-FS does not have to query the CLDB often, so it can cache container locations for very long times.
Moreover, CLDBs are very small in comparison to Apache Hadoop namenodes. Namenodes track metadata and block information for all files, and they track locations for all blocks in every file. Because blocks are typically 200 MB or less in size, the total number of objects that a namenode tracks is very large. CLDBs, however, track containers, which are much larger objects, so the size of the location information can be 100 to 1000 times smaller than the location information in a namenode. CLDBs also do not track information about tables and files. Therefore, it is practical to store 10s of exabytes in a MapR cluster, regardless of the number of tables and files.
- High Availability
Containers are replicated to a configurable number of copies, which are distributed to different nodes in the same cluster as the original or master container. A cluster's CLDB determines the order in which the replicas are updated. Together, the replicas form a replication chain that is updated transactionally. When an update is applied to a tablet in the master container (which is at the head of a replication chain), the update is applied serially to the replicas of that container in the chain. The update is complete only when all replicas in the chain are updated.
The result of this architecture is that when a node goes down due to hardware failure, the tablets served by that node are available instantly from one of the other nodes that have the replicated data. In comparison, when a node fails in a busy HBase cluster, it can easily take thirty minutes, if not more, to recover the regions, as the per-RegionServer write-ahead log needs to be replayed in its entirety before other nodes can start serving any of the regions that were being served by the failed RegionServer.
MapR can detect the exact point at which replicas diverge, even at a 2 GB per second update rate. MapR randomly picks any one of the three copies as the new master, rolls back the other surviving replicas to the divergence point, and then rolls forward to converge with the chosen master. MapR can do this on the fly with very little impact on normal operations.
Containers and MapR-FS Volumes. MapR provides volumes as a way to organize data and manage cluster performance. A volume is a logical unit that allows you to apply policies to a set of files, directories, and tables. Volumes are used to enforce disk usage limits, set replication levels, define snapshots and mirrors, and establish ownership and accountability.
There are several advantages to storing table containers in volumes:
You can restrict a volume to a subset of a cluster's nodes. By doing this, you can isolate sensitive data or applications, and even use heterogeneous hardware in the cluster for specific workloads.
For example, you can use data placement to keep personally identifiable information on nodes that have encrypted drives, or to keep MapR-DB tables on nodes that have SSDs. You can also isolate work environments for different database users or applications and place MapR-DB tables on specific hardware for better performance or load isolation
Isolation of work environments for different database users or applications lets you set policies, quotas, and access privileges at for specific users and volumes. You can run multiple jobs with different requirements without conflict.
As an example, the diagram below depicts a MapR cluster storing table and file data. The cluster has three separate volumes mounted at directories
/project/ads. As shown, each directory contains both file data and table data, grouped together logically. Because each of these directories maps to a different volume, data in each directory can have different policy. For example,
/user/johnhas a disk-usage quota, while
/user/daveis on a snapshot schedule. Furthermore, two directories,
/project/adsare mirrored to locations outside the cluster, providing read-only access to high-traffic data, including the tables in those volumes.
Example: Restricting table storage with quotas and physical topologyThis example creates a table with disk usage quota of 100GB restricted to certain data nodes in the cluster. First, we create a volume named
project-tables-vol, specifying the quota and restricting storage to nodes in the
/data/rack1topology, and mounting it in the local namespace. Next, we use the HBase shell to create a new table named
datastore, specifying a path inside the
$ pwd /mapr/cluster1/user/project $ls bin src $ maprcli volume create -name project-tables-vol -path /user/project/tables \ -quota 100G -topology /data/rack1 $ ls bin src tables $ hbase shell HBase Shell; enter 'help<RETURN>' for list of supported commands. Type "exit<RETURN>" to leave the HBase Shell hbase(main):001:0> create '/user/project/tables/datastore', 'colfamily1' 0 row(s) in 0.5180 seconds hbase(main):002:0> exit $ ls -l tables total 1 lrwxr-xr-x 1 mapr mapr 2 Oct 25 15:20 datastore -> mapr::table::2252.32.16498
See Multi-tenancy with MapR for more details.
- Volume Snapshots
Volume snapshots for rollbacks, hot backups, model training, and real-time data analysis management.
- Rollback from errors
- Application errors or inadvertent user errors can mistakenly delete data or modify data in an unexpected way. With volume snapshots, you can rollback your MapR-DB tables to a known, well-defined state.
- Hot backups
- You can create backups of table data on the fly for auditing or governance compliance.
- Model training
- Machine-learning frameworks, such as Apache Mahout, can use snapshots to enable a reproducible and auditable model training process. Snapshots allow the training process to work against a preserved image of the training data from a precise moment in time. In most cases, the use of snapshots requires no additional storage and snapshots are taken in less than one second.
- Managing real-time data analysis
- By using snapshots, query engines such as Apache Drill can produce precise synchronic
summaries of data sources subject to constant updates, such as sensor data or social media
streams. Using a snapshot of your MapR-DB data for such analyses allows very precise
comparisons to be done across multiple ever-changing data sources without the need to stop
real-time data ingestion.
See MapR Snapshots for more details.
- Volume Replication and Mirroring
Mirroring of volumes lets you automatically replicate differential data in real-time across clusters. You might want to do this to create disaster recovery solutions for databases or to provide read-only access to data from multiple locations. Because MapR-DB does not require RegionServers to be reconstructed, databases can be brought up instantly on the mirrored site if the active site goes down.
Mirroring is a parallel operation, copying data directly from the nodes of one MapR cluster to the nodes in a remote MapR cluster. The contents of the volume are mirrored consistently, even if the files in the volume are being written to or deleted.
MapR captures only data that has changed at the file-block level since the last data transfer. After the data differential is identified, it is then compressed and transferred over the WAN to the recovery site, using very low network bandwidth. Finally, checksums are used to ensure data integrity across the two clusters. There is no performance penalty on the cluster because of mirroring.
See Working with Mirror Volumes for more details.