Preface:
Believe that the recent golden nine silver ten job-hopping season, there are many students interview or job loss is the present domestic this mode, actively preparing the interview before the interview, review the entire Java knowledge system will become very important, can be very responsible, is ready to review, will directly affect the success rate of your entry.
But many of you don’t have the right materials to review your entire Java body of knowledge, or you may not know where to start.
I happened to find this material from a netizen group. It is a very technical material both from the perspective of the whole Java knowledge system and interview. The following article will take you to recognize their shortcomings.
1.JVM
The JVM is an imaginary computer that can run Java code, including a set of bytecode instructions, a set of registers, a stack, a garbage collection, a heap, and a storage method field. The JVM runs on top of the operating system and has no direct interaction with the hardware.
2. JAVA collection
The collection classes are stored in the java.util package, and there are three main types: set, list, and Map.
- Collection: Collection is the basic interface to the Collection List, Set, and Queue.
- Iterator: an Iterator that iterates through the data in a collection
- Map: is the basic interface of a mapping table
3. JAVA multi-threaded concurrency
4. JAVA based
1JAVA Anomaly classification and handling
2 Java reflection
3 Java annotations
4 inside the Java classes
Java 5 generics
6JAVA serialization (creating reusable Java objects)
Java 7 copy
5. Spring principle
It is a comprehensive, one-stop solution for enterprise application development, spanning the presentation, business, and persistence layers. But Spring can still be seamlessly integrated with other frameworks.
1. The characteristics of the Spring
2.Spring core components
3. Common Spring modules
4. The Spring package
5.Spring common annotations
6.Spring third-party integration
7. The Spring IOC principle
8. Spring APO principle
9. Spring MVC principle
10. Spring Boot principle
11. The JPA principle
12. Mybatis cache
13. Tomcat architecture
6. The service
1. Service registration discovery
2. API gateway
3. Configure the center
4. Event Scheduling (Kafka)
5. Service Tracking (starter-sleuth)
6. Service Fuse (Hystrix)
7. The API management
7. Netty and RPC
1. net ty principle
2. net ty high performance
3. net ty RPC implementation
4.RMI implementation
5.Protoclol Buffer
6.Thrift
8, the network
1. Network layer 7 architecture
2. The principle of TCP/IP
3.TCP three handshakes/four waves
4. The principle of HTTP
5. The CDN principle
9. The log
1.Slf4j
2.Log4j
3.LogBack
4.ELK
10. Zookeeper
Zookeeper is a distributed coordination service that can be used for service discovery, distributed locking, distributed leadership election, and configuration management. Zookeeper provides a tree structure similar to the Linux file system (it is considered a lightweight in-memory file system, but suitable for storing only a small amount of information. It is not suitable for storing a large number of files or large files), and provides a monitoring and notification mechanism for each node.
1. The Zookeeper
2. Working principle of Zookeeper (Atomic broadcast)
3.Znode has four types of directory nodes
11. Kafka
Kafka is a high-throughput, distributed, publish/subscribe based messaging system originally developed by LinkedIn and written in Scala. Kafka is currently an open source project of Apache.
1. Kafka data storage design
2. Producer design
3. Consumer design
12. RabbitMQ
RabbitMQ is an open source implementation of AMQP developed in the Erlang language.
1. The RabbitMQ architecture
2. Exchange types
13. Hbase
Base is a distributed, column-oriented open source database (actually, column-family oriented). HDFS provides reliable underlying data storage services for Hbase, MapReduce provides high-performance computing capabilities for Hbase, and Zookeeper provides stable services and Failover mechanisms for Hbase. Hbase is a distributed database solution that uses a large number of inexpensive machines to store and read massive data at high speed.
1. Column storage
2. Hbase core concepts
3. Hbase core architecture
4. Hbase write logic
5. HBase vs Cassandra
14. MongoDB
MongoDB is an open source database system based on distributed file storage written in C++ language. Adding more nodes can ensure server performance under high load conditions. MongoDB aims to provide scalable, high-performance data storage solutions for WEB applications. MongoDB stores data as a document. The data structure consists of key=>value pairs. MongoDB documents are similar to JSON objects. Field values can contain other documents, arrays, and document arrays.
15. Cassandra
Apache Cassandra is a highly scalable, high-performance distributed NoSQL database. Cassandra is designed to process large amounts of data on many commodity servers, providing high availability without fear of a single point of failure. Cassandra has a distributed architecture capable of handling large amounts of data. Data is placed on different machines with multiple replicators to achieve high availability without fear of a single point of failure.
1. Data model
2. Cassandra consistently Hash and virtual nodes
3. The Gossip protocols
4. Data replication
5. Data write request and coordinator
6. Data read request and background repair
7. Data storage (CommitLog, MemTable, SSTable)
8. Secondary index (generate RowKey for value summary to be indexed)
9. Data read and write
16. Design patterns
1. Design principles
2. Factory method pattern
3. Abstract factory pattern
4. Singleton mode
5. Builder mode
6. Prototyping
7. Adapter mode
8. Decorator mode
9. Proxy mode
10. Appearance mode
11. Bridge mode
12. Combination mode
13. Enjoy yuan mode
14. Strategic mode
15. Template method pattern
16. Observer mode
17. Iterative subpatterns
18. Chain of responsibility
19. Command mode
20. Memo mode
21. State mode
22. Visitor pattern
23. The intermediary model
24. Interpreter mode
17. Load balancing
Based on the existing network structure, load balancing provides a cheap, effective and transparent method to expand the bandwidth of network devices and servers, increase the throughput, strengthen the network data processing ability, improve the flexibility and availability of the network.
1. Layer-4 load balancing vs Layer-7 load balancing
2. Load balancing algorithm/policy
3.LVS
4.Keepalive
5.Nginx reverse proxy load balancer
6.HAProxy
Database 18.
1 Storage Engine
2 the index
3 Database three paradigm
4 Databases are transactions
5 Stored Procedures (SQL statement sets for specific functions)
6 trigger (a program that can execute automatically)
7 Database concurrency policy
8 Database Lock
9 Based on Redis distributed lock
10 Partition table
Two stage shape agreement
Three stage shape agreement
13 Flexible Transactions
14 CAP
29. Consistency algorithm
1.Paxos
2.Zab
3.Raft
4.NWR
5.Gossip
6. Consistent Hash
20. JAVA algorithm
Binary search
2. Bubble sorting algorithm
3. Insert sorting algorithm
4. Quicksort algorithm
5. Hill sorting algorithm
6. Merge sort algorithm
7. Bucket sorting algorithm
8. Radix sort algorithm
9. Backtracking
10. Shortest path algorithm
11. Maximum subarray algorithm
12. Longest common suborder algorithm
Minimum spanning tree algorithm
21. Data structure
1 Stack
2 Queue
3 Linked List
4 Hash Table
Sort binary trees
6 the red-black tree
7 B-TREE
Eight diagrams
22. Encryption algorithm
1.AES
2.RSA
3.CRC
4.MD5
23. Distributed caching
1. Cache avalanche
2. Cache penetration
3. Cache penetration
4. Preheat the cache
5. Cache updates
6. Cache degradation
24. Hadoop
It’s a big data solution. It provides a distributed system infrastructure. Core content includes HDFS and MapReduce. After hadoop2.0, yarn is introduced. HDFS provides data storage and mapreduce facilitates data calculation.
1. HDFS
2. MapReduce
3. Life cycle of Hadoop MapReduce jobs
25. Spark
Spark provides a comprehensive and unified framework for managing big data processing requirements of various data sets and data sources (batch data or real-time streaming data) with different natures (text data, graph data, etc.).
1. The concept
2. Core architecture
3. Core components
4.SPARK programming model
5.SPARK calculation model
6.SPARK Runs the process
7. The SPARK RDD process
8.SPARK RDD
26. Storm
1. Cluster architecture
2. Programming model (spout->tuple-> Bolt)
3. The Topology
4.Storm Streaming Grouping
27. YARN
YARN is a framework for resource management and task scheduling. It consists of ResourceManager (RM), NodeManager (NM), and ApplicationMaster (AM). ResourceManager monitors, allocates, and manages all resources. ApplicationMaster is responsible for scheduling and coordinating each specific application; NodeManager is responsible for the maintenance of each node. For all applications, RM has absolute control and the right to allocate resources. Each AM negotiates resources with the RM and communicates with the NodeManager to execute and monitor tasks.
1.ResourceManager
2.NodeManager
3. ApplicationMaster
4. YARN operation process
28. Machine learning
1. Decision tree
2. Random forest algorithm
Logistic regression
4.SVM
Naive Bayes
6.K nearest neighbor algorithm
7. K-means algorithm
8. Adaboost algorithm
Neural networks
10. Markov
29. Cloud computing
1. SaaS
2. PaaS
3. IaaS
5. Openstack
Conclusion:
The whole core knowledge point is sorted out by a Meituan architect. Every point of knowledge has a detailed introduction and analysis. If you need the source file, you can follow the wechat public number: Java Programmers Gathering Place and get it.
