一,NameNode 元数据节点:管理文件系统 secondary namenode从元数据节点:元数据节点的别用节点 二,DataNode 数据节点:存储数据的地方 1)客户端向其请求读取或写入文件,元数据节点发起 2)周期性的想元数据节点回报当前存储的数据快信息 三,Block数据块
一,NameNode 元数据节点:管理文件系统
secondary namenode从元数据节点:元数据节点的别用节点
二,DataNode 数据节点:存储数据的地方
1)客户端向其请求读取或写入文件,元数据节点发起
2)周期性的想元数据节点回报当前存储的数据快信息
三,Block数据块:最基本的存储单位,默认64m,当一个文件大小小于一个数据块的大小时,并不会占用整个数据块的空间
write
1),Client向NameNode发起文件写入的请求。
2),NameNode根据文件大小和文件块配置情况,返回给Client它所管理部分DataNode的信息。
30,Client将文件划分为多个Block,根据DataNode的地址信息,按顺序写入到每一个DataNode块中。
read
1),Client向NameNode发起文件读取的请求。
2),NameNode返回文件存储的DataNode的信息。
3),Client读取文件信息。
简单操作:
当前HDFS的基本信息
$:hadoop dfsadmin -report
Configured Capacity: 15217328128 (14.17 GB)
Present Capacity: 8593608704 (8 GB)
DFS Remaining: 8593297408 (8 GB)
DFS Used: 311296 (304 KB)
DFS Used%: 0%
Under replicated blocks: 1
Blocks with corrupt replicas: 0
Missing blocks: 0
-------------------------------------------------
Datanodes available: 1 (1 total, 0 dead)
Name: 127.0.0.1:50010
Decommission Status : Normal
Configured Capacity: 15217328128 (14.17 GB)
DFS Used: 311296 (304 KB)
Non DFS Used: 6623719424 (6.17 GB)
DFS Remaining: 8593297408(8 GB)
DFS Used%: 0%
DFS Remaining%: 56.47%
Last contact: Tue Dec 11 01:16:30 CST 2012
列出HDFS的文件
$;hadoop fs -ls
Found 2 items
-rw-r--r-- 3 sina supergroup 13 2012-12-05 01:43 /user/demo.txt
drwxr-xr-x - sina supergroup 0 2012-11-18 15:17 /user/docs
cat;
$ hadoop fs -cat /user/demo.txt
test-测试

InnoDB uses redologs and undologs to ensure data consistency and reliability. 1.redologs record data page modification to ensure crash recovery and transaction persistence. 2.undologs records the original data value and supports transaction rollback and MVCC.

Key metrics for EXPLAIN commands include type, key, rows, and Extra. 1) The type reflects the access type of the query. The higher the value, the higher the efficiency, such as const is better than ALL. 2) The key displays the index used, and NULL indicates no index. 3) rows estimates the number of scanned rows, affecting query performance. 4) Extra provides additional information, such as Usingfilesort prompts that it needs to be optimized.

Usingtemporary indicates that the need to create temporary tables in MySQL queries, which are commonly found in ORDERBY using DISTINCT, GROUPBY, or non-indexed columns. You can avoid the occurrence of indexes and rewrite queries and improve query performance. Specifically, when Usingtemporary appears in EXPLAIN output, it means that MySQL needs to create temporary tables to handle queries. This usually occurs when: 1) deduplication or grouping when using DISTINCT or GROUPBY; 2) sort when ORDERBY contains non-index columns; 3) use complex subquery or join operations. Optimization methods include: 1) ORDERBY and GROUPB

MySQL/InnoDB supports four transaction isolation levels: ReadUncommitted, ReadCommitted, RepeatableRead and Serializable. 1.ReadUncommitted allows reading of uncommitted data, which may cause dirty reading. 2. ReadCommitted avoids dirty reading, but non-repeatable reading may occur. 3.RepeatableRead is the default level, avoiding dirty reading and non-repeatable reading, but phantom reading may occur. 4. Serializable avoids all concurrency problems but reduces concurrency. Choosing the appropriate isolation level requires balancing data consistency and performance requirements.

MySQL is suitable for web applications and content management systems and is popular for its open source, high performance and ease of use. 1) Compared with PostgreSQL, MySQL performs better in simple queries and high concurrent read operations. 2) Compared with Oracle, MySQL is more popular among small and medium-sized enterprises because of its open source and low cost. 3) Compared with Microsoft SQL Server, MySQL is more suitable for cross-platform applications. 4) Unlike MongoDB, MySQL is more suitable for structured data and transaction processing.

MySQL index cardinality has a significant impact on query performance: 1. High cardinality index can more effectively narrow the data range and improve query efficiency; 2. Low cardinality index may lead to full table scanning and reduce query performance; 3. In joint index, high cardinality sequences should be placed in front to optimize query.

The MySQL learning path includes basic knowledge, core concepts, usage examples, and optimization techniques. 1) Understand basic concepts such as tables, rows, columns, and SQL queries. 2) Learn the definition, working principles and advantages of MySQL. 3) Master basic CRUD operations and advanced usage, such as indexes and stored procedures. 4) Familiar with common error debugging and performance optimization suggestions, such as rational use of indexes and optimization queries. Through these steps, you will have a full grasp of the use and optimization of MySQL.

MySQL's real-world applications include basic database design and complex query optimization. 1) Basic usage: used to store and manage user data, such as inserting, querying, updating and deleting user information. 2) Advanced usage: Handle complex business logic, such as order and inventory management of e-commerce platforms. 3) Performance optimization: Improve performance by rationally using indexes, partition tables and query caches.


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