serialization in hadoop

Knowing and learning about Hadoop, we have to understand the composition of Hadoop, and based on my own experience, I introduce the Hadoop component, the big data processing process, and the three aspects of Hadoop core: Hadoop Components 650) this.width=650;

Make sure that the three machines have the same user name and install the same directory *************SSH Non-key login simple introduction (before building a local pseudo-distributed, it is generated, now the three machines of the public key private key is the same, so the following is not configured)Stand-alone operation:Generate Key: Command ssh-keygen-t RSA then four carriage returnCopy the key to native: command Ssh-copy-id hadoop-senior.zuoyan.c

1, the main learning of Hadoop in the four framework: HDFs, MapReduce, Hive, HBase. These four frameworks are the most core of Hadoop, the most difficult to learn, but also the most widely used.2, familiar with the basic knowledge of Hadoop and the required knowledge such as Java Foundation,Linux Environment, Linux common commands 3. Some basic knowledge of Hadoo

Using HDFS to store small files is not economical, because each file is stored in a block, and the metadata of each block is stored in the namenode memory. Therefore, a large number of small files, it will eat a lot of namenode memory. (Note: A small file occupies one block, but the size of this block is not a set value. For example, each block is set to 128 MB, but a 1 MB file exists in a block, the actual size of datanode hard disk is 1 m, not 128 M. Therefore, the non-economic nature here ref

grouping (partition) The Hadoop streaming framework defaults to '/t ' as the key and the remainder as value, using '/t ' as the delimiter,If there is no '/t ' separator, the entire row is key; the key/tvalue pair is also used as the input for reduce in the map.-D stream.map.output.field.separator Specifies the split key separator, which defaults to/t-D stream.num.map.output.key.fields Select key Range-D map.output.key.field.separator Specifies the se

Tags: hadoop mysql map-reduce import export mysqlto facilitate the MapReduce direct access to the relational database (mysql,oracle), Hadoop offers two classes of Dbinputformat and Dboutputformat. Through the Dbinputformat class, the database table data is read into HDFs, and the result set generated by MapReduce is imported into the database table according to the Dboutputformat class. when running MapRe

Today, HDFS, the core of hadoop, is very important. It is a distributed file system. Why does hadoop support massive data storage? In fact, it depends mainly on the HDFS capability, mainly on the ability of HDFS to store massive data. 1. Why can HDFS store massive data? In the beginning, let's think about this problem. I don't need to talk about the basic concepts of HDFS ~ We focus on usage rather than "re

Tags: hadoop Linux environment construction Build a pseudo-distributed hadoop Environment 1. network connection between the host machine (Windows) and the client (Linux installed in a virtual machine. A) The host-only host is connected to the client separately; Benefits: Network isolation; Disadvantage: the virtual machine cannot communicate with other servers; B. The bridge host is in the same LAN as the c

combine multiple files into one ZIP file. Each file is compressed separately, and all files are stored at the end of the ZIP file. This attribute indicates that the ZIP file supports splitting at the file boundary. Each part contains one or more files in the zip compressed file. Hadoop CompressionAlgorithmAdvantages and disadvantages When considering how to compress data that will be processed by mapreduce, it is important to consider whether the

a relational database. Apache Zookeeper: Is a distributed, open source Coordination service designed for distribution applications, which is mainly used to solve some data management problems frequently encountered in distributed applications, simplify the coordination and management of distributed applications, and provide high-performance distributed services. Apache Mahout: A distributed framework for machine learning and data mining based on Hadoo