Hbase Nedir
Avery Blaschko
Gnmz dnyasında ilişkisel veritabanı modellerinin birbirinin neredeyse aynısı olduğu sylemek mmkn. rneğin bir Mysql database ile Oracle database arasında Rdbms olarak bir fark olduğu sylemek son derece g genelde farkın gvenlik , performans ve veri byklğ karşısında Cluster alışma mantığından geldiğini syleyebilirim. RDBMS konusunda bence en iyi database pazardan da anlaşılacağı Oracle dır.
Column Family databaselere rnek Apache Hbase %100 open source HDFS storage kullanabilen ok hızlı performansa sahip , kmesi zor Fault toleransı olan bir NOSQL zmdr. Milyarlarca satır Milyonlarca kolondan oluşabilen ve VERSION mantığına sahip Hadoop gibi genel sıradan donanım zerinde bile inanılmaz iyi olan bir NOSQL zmdr.Eğer random ve real time data access istiyorsanız Hbase dşnmeniz gereken bir seenektir. Hbase bir Cloumn oriented databasedir. Yani amacınız 5 milyar kayıt ierisinde kolon bazında en byk 50 taneyi bulmaksa doğru yerdesiniz demektir.
Couchbase server dağıtık, document ve key/value bazlı bir NoSQL zm. Couchbase server kalıcı veriyi memory gc ile birleştirmiş son derece hızlı veri getirebilen update edebilen bir database. Yazma ve okuma işlemlerinde Key/value apisini kullanalirken bu veriler JSon dokmanı olabilir. Couchbase sağladığı dokman indexlemesi zellikle sorgulamada byk avantajlar sağlayabilir.
Couchbase serverlerın diğer belli başlı en temel zellikleri hız. Veriyi mmkn olduğunca memory tutan ve kullanan bu sistem bize veri response timelarında milisaniyeleri ok rahatlıkla sağlıyabiliyor. Node ekleme ve Cluster mimarisi olan bu yapıda tutarlılıkda en st seviyelerde olabiliyor.
Bir başka nemli zelliği ise kolaylığı. oğu Nosql database gibi kolay kurulum ve hızlı bir şekilde Nodelar eklenebilen yapısı mevcut. Schemaless yapısı sayesinde veriyi tutmadan yapısını tanımlamanıza gerek yok. Design time ve Run Time yapısı son derece mantıklı ve kolay. Yine Couchbase sorgulaması son derece kolay olan bir database.
zellikle iki ana kavram gnmz IT dnyasında ne ıkıyor bunlardan biri latency biri throughput . Bu iki nemli başlıkta Couchbase serverların low -latency ve high throughput ihtiyacı olan web ve mobile uygulamalarda inanılmaz iyi performansları olduğunu gryoruz.
Bir başka yazımda diğer NOSQL zmleri ile detaylı bilgiler paylaşmak zere. NOSQL zmleri ile ilgili firmanızda detaylı bir bilgiye ihtiyacınız varsa bana zeke...@bilginc.com dan ulaşabilirsiniz.
HBase includes several methods of loading data into tables. The most straightforward method is to either use the TableOutputFormat class from a MapReduce job, or use the normal client APIs; however, these are not always the most efficient methods.
The bulk load feature uses a MapReduce job to output table data in HBase's internal data format, and then directly loads the generated StoreFiles into a running cluster. Using bulk load will use less CPU and network resources than simply using the HBase API.
The first step of a bulk load is to generate HBase data files (StoreFiles) from a MapReduce job using HFileOutputFormat. This output format writes out data in HBase's internal storage format so that they can be later loaded very efficiently into the cluster.
In order to function efficiently, HFileOutputFormat must be configured such that each output HFile fits within a single region. In order to do this, jobs whose output will be bulk loaded into HBase use Hadoop's TotalOrderPartitioner class to partition the map output into disjoint ranges of the key space, corresponding to the key ranges of the regions in the table.
After the data has been prepared using HFileOutputFormat, it is loaded into the cluster using completebulkload. This command line tool iterates through the prepared data files, and for each one determines the region the file belongs to. It then contacts the appropriate Region Server which adopts the HFile, moving it into its storage directory and making the data available to clients.
If the region boundaries have changed during the course of bulk load preparation, or between the preparation and completion steps, the completebulkloads utility will automatically split the data files into pieces corresponding to the new boundaries. This process is not optimally efficient, so users should take care to minimize the delay between preparing a bulk load and importing it into the cluster, especially if other clients are simultaneously loading data through other means.
After a data import has been prepared, either by using the importtsv tool with the "importtsv.bulk.output" option or by some other MapReduce job using the HFileOutputFormat, the completebulkload tool is used to import the data into the running cluster.
The -c config-file option can be used to specify a file containing the appropriate hbase parameters (e.g., hbase-site.xml) if not supplied already on the CLASSPATH (In addition, the CLASSPATH must contain the directory that has the zookeeper configuration file if zookeeper is NOT managed by HBase).
Although the importtsv tool is useful in many cases, advanced users may want to generate data programatically, or import data from other formats. To get started doing so, dig into ImportTsv.java and check the JavaDoc for HFileOutputFormat.
NoSQL; MSSQL, MySQL ve PostgreSQL gibi ilişkisel veritabanı sistemlerine (RDBMS) alternatif olarak retilmiş bir veritabanı sistemidir. Bu veritabanı zm, bize ilişkisel olmayan, esnek yapılı, byk verili ve ok sayıda aktif kullanıcılı sistemlerde yksek performans ve ynetim kolaylığı sunar.
Couchbase; document ve key-value tabanlı, memory-first yapısına sahip bir NoSQL veritabanı zmdr. Verileri JSON olarak tutar ve N1QL sorgulama diline sahiptir. Linkedin, eBay ve PayPal gibi şirketler tarafından kullanılır. Couchbase dışında MongoDB, Cassandra, HBase gibi farklı NoSQL veritabanları da gnmzde sıka kullanılmaktadır.
Daha sonra, Configure Disk, Memory ve Services butonuna tıklayarak ilgili ayarlamalarımızı tamamladığımız ekrana geelim. Ekrandaki alanlar ile ilgili detaylı bilgi almak isterseniz. (Bknz.)
There are two deploy modes that can be used to launch Spark applications on YARN. In cluster mode, the Spark driver runs inside an application master process which is managed by YARN on the cluster, and the client can go away after initiating the application. In client mode, the driver runs in the client process, and the application master is only used for requesting resources from YARN.
Running Spark on YARN requires a binary distribution of Spark which is built with YARN support.Binary distributions can be downloaded from the downloads page of the project website.To build Spark yourself, refer to Building Spark.
To make Spark runtime jars accessible from YARN side, you can specify spark.yarn.archive or spark.yarn.jars. For details please refer to Spark Properties. If neither spark.yarn.archive nor spark.yarn.jars is specified, Spark will create a zip file with all jars under $SPARK_HOME/jars and upload it to the distributed cache.
will print out the contents of all log files from all containers from the given application. You can also view the container log files directly in HDFS using the HDFS shell or API. The directory where they are located can be found by looking at your YARN configs (yarn.nodemanager.remote-app-log-dir and yarn.nodemanager.remote-app-log-dir-suffix). The logs are also available on the Spark Web UI under the Executors Tab. You need to have both the Spark history server and the MapReduce history server running and configure yarn.log.server.url in yarn-site.xml properly. The log URL on the Spark history server UI will redirect you to the MapReduce history server to show the aggregated logs.
To review per-container launch environment, increase yarn.nodemanager.delete.debug-delay-sec to alarge value (e.g. 36000), and then access the application cache through yarn.nodemanager.local-dirson the nodes on which containers are launched. This directory contains the launch script, JARs, andall environment variables used for launching each container. This process is useful for debuggingclasspath problems in particular. (Note that enabling this requires admin privileges on clustersettings and a restart of all node managers. Thus, this is not applicable to hosted clusters).
Note that for the first option, both executors and the application master will share the samelog4j configuration, which may cause issues when they run on the same node (e.g. trying to writeto the same log file).
As covered in security, Kerberos is used in a secure Hadoop cluster toauthenticate principals associated with services and clients. This allows clients tomake requests of these authenticated services; the services to grant rightsto the authenticated principals.
Hadoop services issue hadoop tokens to grant access to the services and data.Clients must first acquire tokens for the services they will access and pass them along with theirapplication as it is launched in the YARN cluster.
An HBase token will be obtained if HBase is in on classpath, the HBase configuration declaresthe application is secure (i.e. hbase-site.xml sets hbase.security.authentication to kerberos),and spark.yarn.security.tokens.hbase.enabled is not set to false.
Similarly, a Hive token will be obtained if Hive is on the classpath, its configurationincludes a URI of the metadata store in "hive.metastore.uris, andspark.yarn.security.tokens.hive.enabled is not set to false.
If an application needs to interact with other secure HDFS clusters, thenthe tokens needed to access these clusters must be explicitly requested atlaunch time. This is done by listing them in the spark.yarn.access.namenodes property.
Hadoop, yalın tabiriyle, sıradan sunucularda byk verileri işlemek amacıyla kullanılan aık kaynak kodlu bir ktphanedir. Her trl veri iin devasa depolama, ok yksek işlem gc ve neredeyse sınırsız sayıda eşzamanlı grevleri ynetme yeteneği sağlar. Dağınık bir bilgi işlem ortamında byk verileri verimli bir şekilde ynetmenizi ve işlemenizi mmkn kılar. Hadoop drt ana modlden oluşur.
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