A technical blog
1.解决单点故障：multi-server to keep healthy operation.
2.响应慢：增加负载均衡器和服务器，分摊访问压力，提高响应速度.
3.建立load balancer cluster to prevent load balancer fails.
宗旨：每题答案都有得分点，答案涵盖即可。不需要太多细节。
初级：
熟悉SSM架构： 用一个业务说明Spring MVC如何发挥作用。
熟悉Spring MVC细节，比如@Autowired用法，如何将URL映射到Controller, ModelAndView对象返回到方式等
结合项目说如何使用AOP。
如何建索引，怎么用？比如建好索引后，where语句中写name like ‘123%’会不会用到索引，什么情况下不改建索引，哪些语句不用索引？
除了索引，还可以怎样优化SQL，比如分库分表，或通过执行计划查看SQL的优化点。结合项目讲。
会优化很有利于通过面试。（高级开发更要了解优化）
主要有集合，多线程，异常处理，JVM虚拟机等。
hashcode有没有重写过？什么场景下重写？结合hash table，实现hashmap。
高级开发最好能通过ConcurrentHashMap说明并发方面的底层代码实现。
ArrayList, LinkedList的差别？ArrayList如何扩容？
高级开发最好研究底层代码。
Set如何实现防冲的，比如TreeSet和HashSet？
Collection方法，比如方法比较，包装成线程安全的方法？
如何通过ArrayList实现队列或堆栈？
synchronized和可重入锁的区别，问一下信息量防止并发机制
线程如何返回值？其实就是callable， runnable区别
ThreadLocal, volatile关键字来说明线程的内存模型
线程池的使用与常用参数
线程问的多的说并发机制。
1.结构图，工作流程
针对队的垃圾回收机制， 画个图，描述一下新生代，老生代等
回收机制流程，如何在代码中优化内存性能
如果出现OOM异常，如何排查，如何看Dump文件？
GC概念，强弱软引用， finalize方法等
证明自己能干活。同时结合底层代码说出Spring MVC, AOP,ICO架构流程。或者拦截器，controller等高级用法
证明自己有Spring Boot, Spring Cloud经验。说出Spring cloud组件用法
证明自己有分布式开发经验。说出分布式服务等运行机制，如何部署，如何通过nginx等做到负载均衡
如何SQL调优，比如索引，执行计划，或其他优化点。
结合concurrentHashMap源码，说出final，volatile，transient用法，以及如何用lock防止写并发。
结合项目说设计模式
多线程，说出lock或volatile等高级知识点
GC流程，如何通过日志和dump文件排查OOM异常；在高级一些，如何在代码中优化内存代码
诀窍：结合源代码，实际项目说出资深问题
Advantages of Redis
(1) blazing fast. Because data stores in memory.
(2) rich data types, like String, List, Set, Sorted set, Hash
(3) support transactions, atomic operation.
(4) it is used to Cache, Message transportation, and set expired time
Advantages of Redis compared to Memcached
(1) MemCached: only support simple string
(2) Redis has faster speed than MemCached
(3) Redis can persist data
Common performance problems and solutions
(1) It is better not to persistency for Master node, like RDB snapshot, AOF log file
(2) Turn on AOF replication in a Slave node if data is important; Synchronize once per second.
(3) For stability of speed and connectivity, master-slave node in one local area net.
(4) Singly linkedlist data structure is better than graph structure in Master-Slave replication, like Master <- Slave1 <- Slave2 <- Slave3…
Data structures in Python
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java面试通关要点
java工程师成神之路
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Google File System-R
MongoDB summary
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Redis Official PPT
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implement Singleton, Factory and MVC pattern. Design a class: LRU, Trie, Iterator, BST, Blocking Queue.
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HDFS is short of Hadoop Distributed File System, which is a distributed file system to run on commodity hardware. HDFS is highly fault-tolerance and is designed to be deployed on low-cost hardware. HDFS provides high throughput access to application data and is suitable for applications that have large data sets.
HDFS relaxes a few POSIX requirements to enable streaming access to file system data.
HDFS is part of Apache Hadoop Core project.
Hardware failure
Hardware failure is the norm rather than the exception. Therefore, detection of faults and quick, automatic recovery from them is a core architectural goal of HDFS.
Streaming data access
Applications that run on HDFS need streaming access to their datasets. They are not general purpose applications that typically run on general purpose file systems. HDFS is designed for batch processing rather than interactive use by users. The emphasis is on high throughput of data access rather than low latency of data access.
Large data sets
Simple coherency model
HDFS applications need a write-once-write-many access model for files. A file once created, written, and closed need not be changed except for appends and truncates. Appending the content to the end of files is supported but cannot be updated at arbrirary point.
Moving computation is cheaper than moving data
Portability across heterogeneous hardware and software platforms
Apache Cassandra database is the right choice when you need scalability and high availability without compromising performance. Linear scalability and proven fault-tolerance on commodity hardware or cloud infrastructure make it the perfect platform for mision-critical data.
Cassandra’s support for replicating across multiple datacenters is best-in-class, providing lower latency for your users and the peace of mind of knowing that you can survive regional outages.
Redis is an open-source, in-memory data structure store, used as a database, cache and message broker.
It supports data structures such as strings, hashes, lists, sets, sorted sets with range queries, bitmaps, hyperloglogs and geospatial indexes with radius queries.
Redis has built-in replication, Lua Scripting, LRU eviction, transactions and different levels of on-disk persistence, and provides high availability via Resia Sentinel and automatic partitioning with Redis Cluster.
We can run atomic operations on these types, like appending to a string; incrementing the value in a hash; pushing an element to a list; computing set intersection, union and difference; or getting the member with highest ranking in a sorted set.
In order to achieve its outstanding performance, Redis works with an in-memory dataset. Depending on your use case, you can persist it either by dumping the dataset to disk every once in a while, or by appending each command to a log.
Redis also supports trivial-to-setup master-slave asychronous replication, with very fast non-blocking first synchronization, auto-reconnection with partial resynchronization on net split.
A open-source distributed document database that provides high performance, high availability, and automatic scaling.
A record in MongoDB is a document, which is a data structure composed of field and value pairs. MongoDB documents are similar to JSON objects. The values of fields may include other documents, arrays, and arrays of documents.
Amazon EC2, is short of Amazon Elastic Compute Cloud, is a web service that provides resizeable computing capacity that we use to build and host software systems. Using Amazon EC2 eliminates your need to invest in hardware up front, so you can develop and deploy applications faster. You can use Amazon EC2 to launch as mnay or as fewer virtual servers as you need to builder a cluter.
Amazon S3 has a simple web services interface that you can use to store and retrieve any amount of data, at any time, from anywhere on the web.
We can send requests to create buckets, store and retrieve objects, and manage permissions on our recources. The guide also describe access control and the authentication process. Access control defines who can access objects and buckets within S3, and the type of access.
A bucket is a container for objects stored in S3. Every objects is contained in a bucket. For example, if a object named photes/puppy.jpg is stored in the johnsmith bucket, then it is addressable using the URL: http://johnsmith.s3.amazonaws.com/photos/puppy.jpg
Objects are the fundamental entities stored in S3. Objects consist of object data and metadata. Metadata is a set of name-value pairs that describe the object. These include some default metadata, such as data last modified, and standard HTTP metadata, such as Content-Type.
A Key is the unique identifier for an object within a bucket. Each object in a bucket has exactly one key. Because the combination of a bucket, key, and version ID uniquely identify each object. S3 can be thought of as a basic data map between “bucket + key + version” and the object itself. Every object in Amazon S3 can be uniquely addressed through the combination of the web service endpoint, bucket name, key, and optionally, a version. For example, in the URL http://doc.s3.amazonaws.com/2006-03-01/AmazonS3.wsdl, “doc” is the name of the bucket and “2006-03-01/AmazonS3.wsdl” is the key.
We can choose the geographical region where S3 will store the buckets we create.
S3 provides eventual consistency for read-after-write.