ByteDance - Backend Developer Internship Interviews

At the beginning did not let self-introduction, three projects asked two, is to introduce the project, the technology stack is which, the entire project process is how, and then raised some optimization issues, mainly database database, and network communications, the overall difficulty of the self-perception of the medium to high, I basically asked all the questions during the interview process are sorted out in the following, with the answer, if you have any questions, please! I've organized all the questions I was asked during the interview in the following.

1. Advantages and disadvantages of using uuid for databases

Using a UUID (Universally Unique Identifier) as a primary key or unique identifier in a database has some distinct advantages and disadvantages.
vantage
Global Uniqueness:
UUIDs are globally unique, which means that it is almost impossible to duplicate UUIDs generated in different databases or systems. This facilitates data merging in distributed systems.
No centralized distribution is required:
Instead of requiring a central server to assign IDs, each node can generate UUIDs independently, which is particularly useful in distributed systems.
Security:
UUIDs are not based on any predictable data (such as timestamps or MAC addresses) and are therefore more difficult to guess or predict, which is an advantage in applications with high security requirements.
Simplify copying and merging:
Due to the uniqueness of the UUID, the data copying and merging process is much simpler and there is no need to worry about ID conflicts.
drawbacks
Storage and indexing efficiency:
UUIDs typically take up 128 bits, which is more storage space than traditional self-incrementing IDs (typically 32 or 64 bits). This can lead to larger indexes and slightly less efficient queries.
Poor readability:
A UUID is a string of random characters that are poorly readable for humans and not easy to recognize and remember directly.
Performance Issues:
Due to the randomness of the UUIDs, the database's B-tree indexes may become less contiguous, which can lead to degraded insertion and query performance.
Compatibility issues:
Some older database systems may not support the UUID type, or may not support it well enough, which may require additional processing at the application layer.
summarize
Using a UUID as a database identifier requires weighing the advantages and disadvantages based on specific application scenarios and requirements. In distributed systems or scenarios with high data security requirements, UUID is a good choice. However, in environments with extremely high performance requirements or limited storage space, other types of identifiers may need to be considered.

2. The data structure of the redis cache repository, and the underlying data structure of each type

Redis is an open source, memory-based data structure storage system that can be used as a database, cache, and messaging middleware.Redis supports a variety of data structures, each of which has its own specific purpose and underlying implementation. The following are common data structures in Redis and their underlying implementations:
String (String)
Underlying data structure: Simple Dynamic String (SDS).
Features: can store strings, integers or floating point numbers. Supports atomic operations, such as self-increment (INCR) and self-decrement (DECR).
List
Underlying data structure: bi-directional linked list or compressed list (ziplist).
Features: ordered list of strings, supports insertion and deletion operations from both ends (LPUSH, RPUSH, LPOP, RPOP).
Sets
Underlying data structure: hash table (hashtable) or integer set (intset).
Features: unordered set of strings, supports set operations such as concatenation (SUNION), intersection (SINTER) and difference (SDIFF).
Sorted Set
Underlying data structures: skiplist and hash table.
Features: ordered collection of strings, each element associated with a score (score), sorted by score. Supports range queries (ZRANGE, ZREVRANGE).
Hash
Underlying data structure: hash table or compressed list (ziplist).
Features: a collection of key-value pairs, suitable for storing objects.
Bitmap
Underlying data structure: string.
Characteristics: Implemented via strings, supports bit level operations such as Set Bit (SETBIT), Get Bit (GETBIT).
Geographic location (Geo)
Underlying data structure: ordered collections.
Features: Used to store geographic location information and support geographic location related queries, such as nearby locations (GEORADIUS).
Stream
Underlying data structure: a complex data structure designed specifically for logging, similar to Kafka's log.
Features: support for multiple consumers and message grouping , suitable for implementing message queues .
summarize
Redis has a variety of data structure designs, each with its own specific application scenario. Understanding the underlying implementation of these data structures can help you better utilize the performance advantages of Redis and choose the right data structure for your specific needs.

3. What is a B-tree, B+-tree

In database systems and file storage, B-Tree and B+ Tree are key data structures for efficient data storage and retrieval. They have different structures and features and are suitable for different application scenarios. In this paper, we will introduce the structure, characteristics, advantages and disadvantages of B-Tree and B+ Tree, and discuss their application scenarios.

B-Tree
Structure and Properties
The B-tree is a self-balancing multi-way lookup tree with the following properties:
Node structure: each node can have multiple key values and child pointers. The key values in a node are ordered and the child pointers point to the corresponding subtree.
Balance: all leaf nodes are located at the same level, keeping the tree balanced.
Node key value range: the number of key values for each node is within a fixed range (usually [t-1, 2t-1], where t is the order of the B-tree).
Find: Starting from the root node, compare the key values one level down until the target key is found or a leaf node is reached.
Insertion: when the number of keys in a node exceeds the upper limit, the node splits into two nodes, with the middle key rising to the parent.
Deletion: Deletion operations may result in merging or redistribution of nodes. If the number of keys in a node is below the lower limit, it may be necessary to merge with a sibling node or borrow keys from a parent node.
Pros and Cons
Pros:
Suitable for disk storage, it reduces the number of disk accesses because each node can store multiple key values.
The height of the tree is low and the time complexity of the find, insert and delete operations is O(log n).
Drawbacks:
The implementation is more complex, especially in the splitting and merging operations of nodes.
For range queries and read-intensive applications, performance may not be as good as the B+ tree.

B+ Tree
Structure and Properties
The B+ tree is a variant of the B tree with the following characteristics:
Data storage: all the actual data is stored in the leaf nodes and the internal nodes store only the indexes.
Leaf Node Chained List: leaf nodes are connected by a chained list to support efficient range queries.
Internal nodes: store only key values and child pointers, and do not contain data records.
Find: The find operation starts at the root node and compares key values downward, level by level, until it reaches a leaf node.
Insertion: The insertion operation involves inserting key values into leaf nodes, which may result in splitting of leaf nodes. The insertion operation for internal nodes is similar to the B-tree.
Deletion: Deletion operations involve removing key values in leaf nodes, which may result in merging or redistribution of leaf nodes. Deletion operations for internal nodes are similar to B-trees.
Pros and Cons
Pros:
Provides efficient range queries where leaf nodes are connected by a chained table for fast access to contiguous data.
Internal nodes only need to store key values, reducing memory usage.
Better cache utilization as all data is stored in leaf nodes.
Drawbacks:
Leaf nodes store larger amounts of data, which may lead to increased memory usage.
The implementation is relatively complex, especially when managing a chained list of leaf nodes.

Comparison of B-Tree and B+-Tree

Characteristics B-Tree B+-Tree
Data storage location Both internal and leaf nodes store data Leaf nodes only store data, internal nodes store indexes
Index structure Both internal nodes and leaf nodes can do lookups Only leaf nodes store the actual data, internal nodes are only indexed
Range queries need to traverse the entire tree, less efficient Leaf nodes are connected by a chain list, range queries are more efficient
Storage efficiency Fewer keys may be stored, more data is stored More keys are stored, more data is stored in leaf nodes
Operational complexity More complex to implement, need to handle splitting and merging of nodes Relatively complex to implement, need to manage leaf node lists

application scenario
B Tree:
File System: B-tree is suitable for scenarios that require frequent insertion and deletion operations, such as the directory structure of the file system.
Database indexes: suitable for index structures that require frequent updates.
B+ Tree:
Database Indexing: Due to providing efficient range queries, B+ trees are widely used in database indexing, especially in scenarios with high performance requirements for range queries.
Large-scale data storage: for scenarios that require efficient range queries and data storage, such as data warehouses and logging systems.

4. What is a Hash Index

Hash-Table

A hash table is a data structure, also known as a hash table, which stores and finds data by mapping a keyword (Key) to a specific location in an array (called an index). Its core idea is to use the hash function to convert any size of data into a fixed-length integer, which is used as the basis for calculating the subscript of the array. The time complexity of insertion, deletion, and lookup operations can usually reach O(1) because it obtains the storage location directly based on the key value, but it may degrade to linear time in extreme cases, such as when there are too many hash conflicts.

Hash index (hash index) based on the hash table implementation, only the exact match index all the columns of the query is valid. For each row of data, the storage engine will be indexed on all the columns to calculate a hash code (hash code), hash code is a smaller value, and different key values of the rows calculated hash code is not the same. The hash index will store all the hash code in the index, while in the hash table to save a pointer to each row of data.

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The main components of a hash table include the hash function, buckets (Array Buckets), and chaining or open addressing method to resolve conflicts. The hash function is responsible for generating the index, while the bucket is a place to store elements with the same hash value; when two elements have the same hash value, conflicts are handled by chaining (zipper method) or probing sequences (open addressing method).
Hash indexing is a hash-table based database indexing technique used to efficiently store and retrieve data in a database.Hash indexes enable fast data access by mapping key values to locations in a fixed-size array (hash table).
Key Features of Hash Indexes
Constant time complexity:
Ideally, a hash index can complete a query operation in constant time complexity O(1), which means that the query time remains essentially constant regardless of the size of the dataset.
Hash Conflict:
Since hash functions may map different keys to the same location (i.e., hash conflicts), Hash indexes need to handle conflicts. Common conflict resolution methods include chaining (chaining) and open addressing (open addressing).
Range queries are not supported:
Hash indexes do not support range queries because they can only find data by exact matching. This means that Hash indexes are not suitable for performing range queries or sorting operations.
Efficient insertion and deletion:
Insertion and deletion operations for Hash indexes are also usually efficient, as they also require only constant time complexity O(1).
Structure of a Hash Index
Hash table: a fixed-size array of pointers or references to stored data records.
Hash Functions: Functions that map keys to locations in a hash table.
Conflict resolution mechanisms: methods used to deal with hash conflicts, such as the chain address method or the open address method.
Advantages of Hash Indexes
Efficient query performance:
Ideally, a hash index can complete a query operation in constant time complexity O(1), which makes it ideal for scenarios that require fast and accurate matching.
Efficient insertion and deletion:
Insertion and deletion operations for Hash indexes are also usually efficient, as they also require only constant time complexity O(1).
Disadvantages of Hash Indexes
Range queries are not supported:
Hash indexes do not support range queries because they can only find data by exact matching.
Hash Conflict:
Hash conflicts can lead to performance degradation, especially if the load factor is high.
Memory footprint:
Hash indexes typically require a large amount of memory space to store the hash table, especially if the keys are unevenly distributed.
summarize
Hash indexing is a database indexing technique based on hash tables that enables fast data access by mapping keys and values to locations in a hash table.Hash indexes ideally provide query performance with constant time complexity, but they do not support range queries and need to deal with hash conflicts. Understanding the structure and characteristics of hash indexes helps to better design and optimize database indexes.

Scenarios for Hash Indexes

Hash indexes are suitable for certain specific usage scenarios due to their specific performance characteristics and limitations. The following are some of the scenarios where the use of Hash indexes is appropriate:
Exact Match Query
Scenario Description: Hash indexes can provide very fast query performance when database queries rely heavily on exact matches (i.e., equals queries).
Example: Finds a specific user ID, order number, or other unique identifier.
in-memory database
SCENARIO DESCRIPTION: In in-memory databases, Hash indexes can take full advantage of their constant time complexity since the data is stored entirely in memory.
Examples: in-memory database systems such as Redis, Memcached, etc.
Small data sets
Scenario Description: For small-sized datasets, Hash indexes have relatively small memory footprint and hash conflict problems and can provide efficient query performance.
Examples: configuration tables, dictionary tables, etc.
static data
Scenario Description: For static data that changes infrequently, hash indexes can provide stable query performance and do not require frequent adjustment of the index structure.
Examples: history table, log table, etc.
load balancing
Scenario Description: In distributed systems, hash indexes can be used to implement load balancing, where requests are distributed to different servers through hash functions.
Examples: distributed caching systems, distributed databases, etc.
Scenarios where Hash Indexes are not suitable
Scope Query:
Hash indexes do not support range queries, so they are not suitable for scenarios that require frequent execution of range queries.
Sorting operations:
Hash indexes do not preserve the order of keys and values, and are therefore not suitable for scenarios that require sorting operations.
Large-scale datasets:
For large-scale datasets, the memory footprint and hash conflict issues of hash indexes can become significant and affect performance.
Frequently updated data:
Frequent insertion and deletion operations may result in reshuffling of the hash table, affecting performance.
summarize
Hash indexes are suitable for scenarios that require fast and exact matching queries, especially in in-memory databases and small datasets. However, it is not suitable for scenarios that require range queries, sorting operations, or large-scale datasets. Understanding the scenarios for which Hash indexes are suitable helps to better design and optimize database indexes.

6. What is websocket and what is it used for? How to use it in your project

The WebSocket protocol is not restricted by the same-origin policy and enables cross-domain communication.
With WebSocket, the client and server can establish a persistent connection for two-way communication.

WebSocket is a protocol for full-duplex communication over a single TCP connection. It makes exchanging data between a client and a server much easier, allowing the server to actively push data to the client. In the WebSocket API, the browser and the server only need to complete a handshake once, and a persistent connection can be created directly between the two, with bi-directional data transfer.
Main Uses of WebSocket
Real-time communication:
WebSocket is well suited for applications that require real-time updates, such as online chats, collaborative multi-player editing, real-time gaming, stock quotes, and more.
Push Notifications:
The server can actively push messages to the client, which is suitable for real-time notifications, alarm systems, etc.
Reduce delays:
WebSocket reduces the latency and overhead of each request compared to traditional HTTP requests because it requires only one handshake.
Save bandwidth:
WebSocket uses less bandwidth because it does not need to send HTTP headers with each request.

7. How to understand gin's middleware

Gin is a high-performance HTTP web framework written in the Go language , which is widely used to build RESTful APIs and other web services . Gin's Middleware is one of its core features , allowing developers to insert custom logic into the request processing flow , so as to achieve such functionality as logging , request validation , authentication and so on .
Understanding Gin's Middleware

1. The role of middleware
   Middleware plays the role of an "interceptor" in the Gin framework, which performs actions before or after the request reaches the final handler. Middleware can be used for the following purposes:
   Logging: Records details of requests and responses.
   Authentication: Verifies the identity and privileges of the user.
   Request validation: validates the data format and content of the request.
   Error handling: catching and handling errors.
   Performance monitoring: Monitor the processing time of requests.
   Request Rewrite: modifies the content of the request or response.
   How Middleware Works
   In Gin, middleware is a function that takes an * object as an argument and calls the () method to continue processing the request chain. Middleware can perform the following actions:
   Preprocessing: Code that is executed before calling ().
   Postprocessing: code that is executed after () is called.
   How to write and use middleware
   The following is a simple example of Gin middleware for logging the processing time of a request:

package main

import (
    "fmt"
    "time"
    "/gin-gonic/gin"
)

// Define a middleware function
func LoggerMiddleware() {
    return func(c *) {
        startTime := ()

        // Continue processing the request
        ()

        // Calculate the request processing time
        latency := (startTime)
        ("Request took %v\n", latency)
    }
}

func main() {
    r := ()

    // Use the middleware
    (LoggerMiddleware())

    ("/ping", func(c *) {
        (200, {
            "message": "pong", {
        })
    })

    () // Listen and start the service on 0.0.0.0:8080.
}

9. Using the gin framework specifically how to synchronize data updates from mysql database to redis

When using the Gin framework, synchronizing data updates from a MySQL database to Redis can be accomplished in a number of ways. The following is a basic implementation idea and sample code that shows how this can be accomplished in a Gin application.
Ideas for implementation
Initialize database connections: connect to MySQL databases and Redis databases.
Define Data Model: Defines the data model used to manipulate the MySQL database.
Write synchronization logic: synchronize data to Redis when it is updated.
Using Gin to process requests: Call the synchronization logic when Gin processes a request.
sample code (computing)
Below is a simple example showing how to synchronize data from MySQL to Redis in a Gin application.
Initializing a Database Connection

package main

import (
    "database/sql"
    "fmt"
    "/gin-gonic/gin"
    "/go-redis/redis/v8"
    _ "/go-sql-driver/mysql"
    "context"
)

var (
    db *
    redisClient *
)

func init() {
    var err error
    // initialization MySQL grout
    db, err = ("mysql", "user:password@tcp(127.0.0.1:3306)/dbname")
    if err != nil {
        panic(err)
    }

    // initialization Redis grout
    redisClient = (&{
        Addr: "localhost:6379",
        Password: "", // no password set
        DB: 0, // use default DB
    })
}

type User struct {
    ID   int    `json:"id"`
    Name string `json:"name"`
}

func getUserFromMySQL(id int) (*User, error) {
    var user User
    row := ("SELECT id, name FROM users WHERE id = ?", id)
    if err := (&, &); err != nil {
        return nil, err
    }
    return &user, nil
}

func saveUserToRedis(user *User) error {
    ctx := ()
    return (ctx, ("user:%d", ), , 0).Err()
}

func syncUser(id int) error {
    user, err := getUserFromMySQL(id)
    if err != nil {
        return err
    }
    return saveUserToRedis(user)
}
utilization Gin Processing requests
func main() {
    r := ()

    ("/users/:id", func(c *) {
        id := ("id")
        userId, err := (id)
        if err != nil {
            (400, {"error": "invalid user ID"})
            return
        }

        if err := syncUser(userId); err != nil {
            (500, {"error": ()})
            return
        }

        (200, {"message": "user synced successfully"})
    })

    () // listens for and receives information in the 0.0.0.0:8080 Start the service on the
}

10. How mysql databases are indexed

In a MySQL database, an index is a data structure used to increase the speed of data retrieval. Indexes can greatly reduce the time it takes for a database system to find data, especially when working with large amounts of data.MySQL supports many types of indexes, including B-tree indexes, hash indexes, full-text indexes, and so on. The following are the detailed steps and considerations on how to create an index in a MySQL database.
Selecting the right column
Before creating an index, you first need to select the appropriate columns. Usually, the following are suitable for creating an index:
Primary Key Columns: Primary key columns are usually columns that uniquely identify each row in a table and are suitable for creating indexes.
Foreign key columns: Foreign key columns are used for associations between tables and are suitable for creating indexes.
Columns frequently used in query conditions: If a column is frequently used in WHERE clauses, it is appropriate to create an index.
Columns frequently used for sorting: If a column is frequently used in ORDER BY clauses, it is appropriate to create an index.
Creating Indexes
In MySQL, you can use the CREATE INDEX statement to create indexes. The following are some common examples of creating indexes:
single-column index
CREATE INDEX idx_name ON table_name (column_name);
compound index
CREATE INDEX idx_name ON table_name (column1, column2);
unique index
CREATE UNIQUE INDEX idx_name ON table_name (column_name);
full text index
CREATE FULLTEXT INDEX idx_name ON table_name (column_name);
View Index
You can use the SHOW INDEX statement to view index information in a table:
SHOW INDEX FROM table_name;
Delete Index
If you need to delete an index, you can use the DROP INDEX statement:
DROP INDEX idx_name ON table_name;
Notes on Indexing
Index selectivity: Columns with high selectivity (i.e., columns with more distinct values) are more suitable for index creation.
Index size: Indexes take up extra storage space, so there is a trade-off between index size and query performance.
Index maintenance: Indexes need to be maintained regularly, especially when data is updated frequently, the maintenance cost of indexes will increase.
Type of index: Choose the appropriate type of index according to the needs of the query, for example, B-tree index is suitable for range query, hash index is suitable for equal value query.
Recommendations for using indexes
Avoid creating indexes on frequently updated columns: Frequently updated columns result in frequent index rebuilds, affecting performance.
Pay attention to the order when using composite indexes: the order of composite indexes affects query performance, and the highly selective columns are usually placed first.
Analyze and optimize indexes regularly: Use the ANALYZE TABLE and OPTIMIZE TABLE statements to analyze and optimize indexes.
summarize
In MySQL databases, indexes are an important means of improving query performance. By choosing the right columns, creating the right types of indexes, and paying attention to index maintenance and optimization, you can significantly improve the query efficiency of your database. Understanding how to create indexes and the considerations for using indexes is essential for database design and performance optimization.

11. How exactly is the chat data stored in the redis cache sorted?

Storing and sorting chat data in Redis usually involves the use of Sorted Sets. Sorted Sets are a data structure provided by Redis that is similar to Sets, but each member has a score by which the members can be sorted. The following are the exact steps and examples of how to store and sort chat data in Redis.
Storing Chat Data with Ordered Collections
Storing Chat Messages
Suppose we have a chat application that needs to store chat messages between users and sort them chronologically. We can accomplish this using ordered collections where the timestamps of the messages are used as scores and the contents of the messages are used as members.

ZADD chat_messages 1609459200 "Hello, how are you?"
ZADD chat_messages 1609459260 "I'm good, thanks!"
ZADD chat_messages 1609459320 "What are you up to?"

import redis
import time

# connect to Redis server (computer)
r = (host='localhost', port=6379, db=0)

# Storing Chat Messages
message_time = int(())
message_content = "Hello, how are you?"
('chat_messages', {message_content: message_time})

# Get chat messages sorted by time
messages = ('chat_messages', 0, -1, withscores=True)
for message, timestamp in messages:
    print(f"Time: {timestamp}, Message: {('utf-8')}")

# Get the latest chat messages
latest_messages = ('chat_messages', 0, 9, withscores=True)
for message, timestamp in latest_messages:
    print(f"Time: {timestamp}, Message: {('utf-8')}")

12. What if I have to store a large amount of data into the database and the database cannot be loaded? What are the solution strategies

When a database is unable to load a large amount of data, there are various strategies that can be used to solve the problem. Here are some common strategies to solve the problem:
Optimize database configuration
Resizing the Buffer Pool: Increase the size of MySQL's buffer pool to improve memory usage.
Adjust connections: Adjusts the maximum number of connections to the database based on server performance.
Enable query caching: For frequently executed queries, enabling query caching can improve performance.
Optimizing queries and indexes
Analyze and optimize queries: Use EXPLAIN to analyze queries and optimize slow queries.
Create appropriate indexes: Create indexes for columns that are frequently used in query conditions to improve query efficiency.
Avoid full table scans: Ensure that queries use indexes to avoid full table scans.
Partitions and Tables
Horizontal partitioning: Divide a large table into multiple smaller tables by some condition (e.g., time, range) to spread the data and query load.
Vertical partitioning: Split different columns of a table into different tables to reduce the amount of data in a single table.
read-write separation
Master-Slave Replication: Uses a master-slave replication architecture to spread read operations across multiple slave repositories, reducing the load on the master repository.
Load balancing: Use a load balancer to distribute read and write requests to different database servers.
Using Cache
In-memory caching: Use an in-memory caching system such as Redis or Memcached to cache frequently accessed data.
Query caching: For frequently executed query results, use query caching to reduce database access.
Database clustering
Master-slave clustering: Use master-slave clustering architecture to improve database read/write capability and availability.
Sharded Cluster: Use sharding technology to distribute data to multiple database nodes to improve concurrent processing capability.
Data archiving and cleansing
Data archiving: Periodically archive historical data to other storage systems, reducing the amount of data in the main database.
Data Cleaning: Regularly clean up data that is no longer needed to reduce storage pressure on the database.
Hardware upgrades
Increase memory: Increase the memory of the server to improve the caching of the database.
Use SSDs: Improve I/O performance by using solid-state drives (SSDs) instead of mechanical drives.
Upgrade CPU: Upgrade the CPU of the server to improve data processing capability.
Using NoSQL Databases
Choosing the right NoSQL database: For unstructured or semi-structured data, it may be more appropriate to use a NoSQL database (e.g. MongoDB, Cassandra).
Monitoring and Tuning
Real-time monitoring: Use monitoring tools to monitor the performance indicators of the database in real time, and find and solve problems in time.
Regular Tuning: Regular database tuning to optimize configurations and queries.
summarize
When a database is unable to load a large amount of data, the problem can be solved through a variety of strategies such as optimizing database configuration, optimizing queries and indexes, partitioning and table splitting, read/write separation, using caching, database clustering, data archiving and cleansing, hardware upgrades, using NoSQL databases, and monitoring and tuning. Choosing the right strategy for a specific situation can effectively improve database load capacity and performance.

13. Tell me more about the programs, the process and so on.

14. How to do data persistence and what are the options

Data persistence refers to storing data on a non-volatile storage medium (e.g., hard disk) to ensure that data is not lost after a system reboot or crash. There are multiple data persistence schemes available in different application scenarios and technology stacks. The following are some common data persistence scenarios:
relational database
MySQL: Widely used relational database that supports transactions and complex queries.
PostgreSQL: Powerful open source relational database with support for advanced features.
SQL Server: A relational database developed by Microsoft for enterprise-level applications.
Oracle: A commercial relational database that provides powerful data management and analysis capabilities.
NoSQL Database
MongoDB: Document-based NoSQL database for semi-structured data.
Cassandra: Distributed NoSQL database for large-scale data and high availability.
Redis: In-memory NoSQL database that supports data persistence to disk.
Couchbase: Document-oriented NoSQL database with flexible data model support.
file system
Local File System: Stores data in files on the local hard disk.
Network File System: such as NFS (Network File System), which stores data in a file system shared over a network.
Object storage: e.g. Amazon S3, AliCloud OSS, stores data as objects in the cloud.
log file
WAL (Write-Ahead Logging): In a database, logs are written before data to ensure data persistence.
Logging file system: such as ext4, XFS, through the logging of file system operations, to improve data recovery capabilities.
distributed storage system (DSS)
Hadoop HDFS: A distributed file system for big data storage and processing.
Ceph: A distributed storage system that supports object, block, and file storage.
GlusterFS: A distributed file system that provides high availability and scalability.
message queue
Kafka: a high-throughput distributed message queue that supports data persistence to disk.
RabbitMQ: message queuing system that supports multiple message persistence strategies.
ActiveMQ: open source messaging middleware , supports a variety of persistence methods .
Object-Relational Mapping (ORM)
Hibernate: Java's ORM framework , support for object persistence to a relational database .
Django ORM: Python's ORM framework that supports persisting objects to a database.
Entity Framework: An ORM framework for .NET that supports persisting objects to a database.
cloud service
Cloud databases: e.g. Amazon RDS, AliCloud RDS, provide hosted database services.
Cloud storage: e.g. Amazon S3, AliCloud OSS, providing highly available cloud storage services.
Hybrid persistence
Cache + Persistence: Combines in-memory caching (e.g., Redis) with relational databases, writing to the cache and then asynchronously writing to the database.
Multi-level storage: Combine SSDs and HDDs to store hot data on SSDs and cold data on HDDs.
summarize
Data persistence is an important means to ensure data security and reliability. According to application scenarios and technical requirements, you can choose relational databases, NoSQL databases, file systems, log files, distributed storage systems, message queues, ORM frameworks, cloud services, or hybrid persistence solutions. Understanding the characteristics and application scenarios of different persistence solutions will help you choose the most suitable data persistence strategy.