InfluxDB is an open source temporal database specifically designed for processing time series data. It is developed and maintained by InfluxData. The following is a detailed introduction to InfluxDB: Introduction: InfluxDB is a high-performance, scalable, and easy-to-use open-source temporal database used to process time related data such as monitoring, application metrics, IoT sensor data, etc. Established on: InfluxDB was developed by InfluxData in 2014 and has become its core product. Founder: The founder of InfluxDB is Paul Dix, a software engineer with extensive experience in distributed systems and databases. Applicable scenario: InfluxDB is suitable for processing large-scale time series data. Its characteristics make it very popular in the field of monitoring and operation, commonly used for storing and analyzing indicator data of servers, network devices, sensors, and applications. In addition, InfluxDB is also suitable for various IoT (Internet of Things) and real-time data application scenarios. Advantages: 1. High performance: InfluxDB has designed a dedicated storage and query engine for time series data, which can handle high frequency and large amounts of data. 2. Scalability: InfluxDB supports horizontal scaling and can increase storage and query capabilities by adding more nodes. 3. Ease of use: InfluxDB provides a simplified data writing and query experience through a simple API and Query language (InfluxQL or Flux). 4. Data Model: InfluxDB uses a data model of tags and fields, enabling efficient indexing and querying of data based on different dimensions. Disadvantages: 1. Distributed consistency: There are some limitations to the consistency of InfluxDB in distributed environments, especially during write operations. 2. Backup and recovery: The process of backing up and restoring data in InfluxDB is relatively complex and does not support automatic backup. Principle: InfluxDB uses a storage engine similar to a log structure to append and write time series data, providing fast write speed. Data is stored in segments according to time, similar to the concept of Partition table. It also supports data compression to reduce storage footprint. Performance: InfluxDB has been optimized for high load environments and can handle a large number of write and query requests. It provides high performance and low storage overhead through technologies such as batch writing and data compression. Official website: The official website of InfluxDB is: https://www.influxdata.com/products/influxdb/ Summary: InfluxDB is a high-performance, scalable, and easy-to-use open source database specifically designed for temporal data processing. Through its simplified API and Query language, it is suitable for various monitoring, IoT and real-time data application scenarios. Although there are some limitations in distributed consistency and backup, the characteristics of InfluxDB in data storage and querying make it widely popular in temporal data analysis.

TimescaleDB is an open source time series Database management system and an extension of PostgreSQL. It aims to address the needs of processing large-scale time series data, providing high-performance insertion, query, and analysis capabilities. TimescaleDB was released in February 2017 and was founded by Timescale Corporation. The founders of Timescale include Mike Freedman, Ajay Kulkarni, Feng Pan, and others. TimescaleDB provides efficient query and analysis performance by partitioning and sharding time series data on PostgreSQL. Applicable scenario: 1. Internet of Things (IoT) application: Process time series data generated by device sensors, such as temperature, humidity, pressure, etc. 2. Distributed monitoring system: collect and analyze a large number of time series performance index data, such as server CPU utilization, network traffic, etc. 3. Financial Market data analysis: process and analyze financial Market data, such as stock price, trading volume, etc. 4. Log analysis: Store and analyze system logs for troubleshooting and performance optimization. Advantages: 1. High performance: TimescaleDB achieves high scalability and parallel queries through time based partitioning and sharding, providing fast insertion and query performance. 2. Fully compatible with PostgreSQL: As an extension, TimescaleDB is compatible with the standard PostgreSQL Query language and functions, and can seamlessly integrate with the existing PostgreSQL ecosystem. 3. Data consistency: TimescaleDB supports ACID (atomicity, consistency, isolation, and persistence) transactions to ensure data consistency and reliability. 4. Flexibility: It can use SQL language for complex queries and data analysis, and also supports the use of extended commands and functions for advanced data processing. Disadvantages: 1. Relatively new: As a relatively new Database management system, TimescaleDB may lack the functions and tools of some mature database systems in some aspects. 2. High learning cost: For users who are not familiar with PostgreSQL, learning and understanding TimescaleDB may require some additional effort. Principle: TimescaleDB achieves high performance by horizontally dividing time series data into multiple consecutive partitions and blocks. Each partition contains data over a period of time, while each block contains a continuous time series within the partition. In this way, queries can only access relevant partitions and blocks, thereby improving query performance. Performance: TimescaleDB provides high performance by adopting multiple optimization strategies and technologies. This includes automatic data partitioning, data sharding, compression, and data storage reduction. It also supports parallel queries and efficient processing of large-scale time series data. Official website: The official website of TimescaleDB is https://www.timescale.com Detailed documentation, tutorials, examples, and community support are provided above.

OpenTSDB is a distributed and scalable time series database based on Hadoop and HBase, used to store and analyze large-scale time series data. It was developed by StumbleUpon and is currently maintained by the Apache Software Foundation. The establishment of OpenTSDB can be traced back to 2010, initially developed as an internal StumbleUpon project. The founders of this project are Benjamin Reed and Vladimir Smirnov. OpenTSDB is suitable for processing massive time series data. By using timestamps and a set of tags (key value pairs) to identify data points, a large amount of time series data can be efficiently stored and queried. It has a wide range of application scenarios in fields such as the Internet of Things, monitoring, and log analysis. The advantages of OpenTSDB include: 1. Scalability: OpenTSDB is built on Hadoop and HBase, and can increase storage and processing capabilities through horizontal scaling to adapt to the constantly growing data scale. 2. High performance: OpenTSDB uses HBase as the storage engine to quickly write and query Big data sets. 3. Powerful query function: OpenTSDB provides rich query functions, including range query, aggregation query, filtering query, etc., making it convenient for users to quickly obtain the required data. 4. Flexible data model: OpenTSDB supports multi-dimensional labels to organize data, allowing users to flexibly slice and analyze data based on different dimensions. However, OpenTSDB also has some limitations and drawbacks: 1. Complex deployment and management: The deployment of OpenTSDB requires reliance on underlying components such as Hadoop and HBase, which requires high technical requirements from system administrators. 2. Large storage space occupation: Due to the use of distributed storage solutions, OpenTSDB introduces certain redundancy during storage, resulting in a large storage space occupation. 3. Weak support for data point updates: OpenTSDB is better at storing and querying time series data, but has weaker support for frequently updated data points. The working principle of OpenTSDB is to shard and store time series data in an HBase cluster. Each data point can be identified by a timestamp and a set of labels (key value pairs), which can be used for querying and aggregation. When querying data, OpenTSDB will convert the query criteria into HBase query statements and send them to various data nodes, and then summarize and return the results to the user. In terms of performance, OpenTSDB can handle large-scale datasets and supports fast writing and querying. The performance depends on the configuration and scale of the underlying HBase cluster, as well as the distribution of data. For more information about OpenTSDB, you can refer to its official website: https://opentsdb.net/

CrateDB is an open source Distributed database management system designed to provide high performance and powerful data processing functions by combining real-time query processing capabilities with horizontal scalability. CrateDB can handle powerful SQL queries and combine high throughput insert, update, and delete operations. This database uses a distributed architecture and is capable of storing and processing large amounts of data in large-scale clusters. CrateDB was founded by Jodok Batlog and Bernd Dorn in 2013 and originally belonged to Crate.io. CrateDB is an open source branch based on the NoSQL database Elasticsearch, aimed at expanding the functionality of Elasticsearch and providing better support for real-time analysis. CrateDB is applicable to scenarios requiring real-time analysis and Big data processing. It is commonly used in Internet of Things (IoT) applications, sensor data processing, log analysis, industrial automation, time series analysis, geographic information systems, and more. CrateDB's efficient processing ability for massive amounts of data makes it an ideal choice for processing real-time data and complex queries. The advantages of CrateDB include: 1. Powerful query function: CrateDB supports standard SQL queries, allowing users to easily perform complex data analysis on large-scale datasets. 2. Distributed architecture: CrateDB's distributed design enables it to scale horizontally to handle large-scale data and provide better performance and reliability. 3. Real time data processing: CrateDB supports high throughput real-time data updates and queries, suitable for scenarios that require quick response to new data. 4. Open source and free: CrateDB is open source, allowing users to freely use, modify, and distribute the software. However, CrateDB also has some drawbacks: 1. Relatively small ecosystem: Compared to some mature database systems, the ecosystem of CrateDB is relatively small and may lack support for some third-party tools and plugins. 2. The Learning curve is steep: Since CrateDB is a relatively new database system, it may take some time to learn and practice to master its working principles and best practices. The working principle of CrateDB is based on a distributed storage engine and uses Elasticsearch as the underlying storage and indexing engine. It uses a document based data model and scalable sharding architecture to distribute data across multiple nodes. CrateDB utilizes a distributed query engine to distribute queries to specific nodes and merge the results into the final query result. From a performance perspective, CrateDB performs well in handling complex queries and large-scale datasets. It can scale horizontally to handle large amounts of data and has the characteristics of high throughput and low latency. You can find it on the official website of CrateDB（ https://crate.io/cratedb/ ）Learn more details about CrateDB on, including documentation, Case study, and user guides.

QuestDB is a high-performance temporal database specifically designed to handle large-scale data and high concurrency access. It adopts a column based storage method and supports SQL queries. The goal of QuestDB is to become the preferred database for processing large-scale data in financial markets. QuestDB was founded by Nicolas Hourcard in 2013 as an open source project originally developed as a performance testing tool. After a period of development and reputation accumulation, QuestDB has gradually become a widely used database solution. QuestDB is applicable to many different scenarios, especially those requiring fast reading and writing of large amounts of data, such as financial transactions, Network monitoring, and data analysis of Internet of Things devices. It can process billions of data and complete queries at the millisecond level, with high scalability and fault tolerance. One of the main advantages of QuestDB is its excellent performance. Compared to traditional relational databases, it can provide higher write and query speeds. Secondly, QuestDB supports the standard SQL Query language, enabling developers to easily migrate data from other relational databases or integrate with other tools. The principle of QuestDB is to adopt a highly optimized columnar storage method, which organizes data into columns to reduce I/O operations and improve compression rate. In addition, QuestDB also uses a custom query engine and distributed architecture to achieve highly concurrent read and write operations. Regarding performance, the test results of QuestDB show that it can process millions of records per second and complete queries at the millisecond level. This makes it an ideal choice for processing real-time data. For its drawbacks, QuestDB is currently not as complete in functionality as some traditional relational databases. In addition, as it is a relatively new project, its community and ecosystem are not as large as some established databases, so there may be a lack of mature tools and libraries. You can visit the official website of QuestDB（ https://questdb.io ）Learn more. The official website provides documentation, sample code, and community support, making it easy for developers to understand and use this database.

MemSQL is a high-performance, distributed In-memory database that can be used for real-time analysis and operation of large-scale data. It was developed by MemSQL Inc. and first released in 2013. Founders Eric Frenchiel and Nikita Shamgunov jointly founded this company in 2011. The core design concept of MemSQL is to combine main memory (RAM) and flash memory (Flash) to provide fast data access and processing capabilities. It supports Transaction processing and real-time data analysis, and supports SQL Query language. MemSQL has a distributed architecture that enables high availability and scalability by sharding data and storing it on multiple servers. MemSQL is suitable for scenarios that require rapid processing of large amounts of data, such as real-time analysis, transaction processing, real-time recommendations, and real-time reporting. It is widely used in fields such as finance, e-commerce, social media, and gaming. Advantages: 1. High performance: MemSQL utilizes a combination of memory and flash memory to perform well in processing large-scale data, outperforming traditional disk driven database systems. 2. Real time performance: Due to the use of memory and flash memory, MemSQL can quickly respond and process real-time data, suitable for scenarios that require real-time operation and analysis of data. 3. Scalability: MemSQL's distributed architecture and sharded storage method make it easy to scale to large-scale datasets and high concurrency requests. 4. SQL compatibility: MemSQL supports the standard SQL Query language, reducing user learning and migration costs. Disadvantages: 1. High cost: Due to MemSQL's reliance on high-performance storage media such as memory and flash memory, its hardware cost is relatively high. 2. Sufficient memory resources are required: As MemSQL mainly relies on memory for data read and write operations, it requires sufficient memory resources to leverage its advantages. The working principle of MemSQL is to shard and store data on different nodes, with each node having a replica to provide high availability. The sharding and replication of data are automatically managed, and MemSQL can automatically evenly allocate data to various nodes and perform data recovery in the event of node failure. It has ACID transaction support similar to traditional relational databases, ensuring data consistency and reliability. In terms of performance, MemSQL utilizes the high-speed read and write characteristics of memory and flash memory to achieve millisecond level data response time. It supports technologies such as parallel queries and index optimization, which can effectively process large-scale data while providing excellent query performance. You can learn more about the MemSQL database on its official website: https://www.memsql.com/

TimestreamDB is a cloud native temporal database service launched by Amazon in 2020. It is specifically designed to handle large amounts of time series data and provides high reliability and performance for analysis and queries. TimestreamDB made its debut at the Amazon re: Invent global conference in 2019 and was officially launched in 2020. Its founder is an engineer at Amazon. TimestreamDB is suitable for various scenarios that require processing and analyzing temporal data. This includes IoT data monitoring, application performance monitoring, financial transaction logs, sensor data, and more. It can process a large amount of high-frequency recorded time series data and provide complex query and analysis functions. The advantages of TimestreamDB include: 1. High scalability: capable of processing massive amounts of time series data. 2. Quick query and analysis: Provides fast real-time query and aggregation capabilities, supporting complex analysis and query operations. 3. High performance write: Supports high throughput data write operations and can handle fast writes of real-time data. 4. Security and reliability: Provide security functions such as data encryption and access control to ensure data security. 5. Integration with Amazon ecosystem: It can be integrated with other AWS services, such as Lambda, Kinesis, and S3, to facilitate data flow and conversion. The working principle of TimestreamDB is to organize time series data into a multi-level table structure. Each table can define time granularity and storage strategies, enabling data storage and queries to be optimized according to requirements. Data is distributed with time as the primary key to improve query efficiency and throughput. TimestreamDB has very high performance and can easily handle large-scale time series data. It supports high concurrency write and real-time query operations, and provides low latency data access. In addition, TimestreamDB can automatically handle data compression and storage to reduce storage space and costs. The official website of TimestreamDB is: https://aws.amazon.com/timestream/ . On the official website, users can find more detailed information, documentation, use cases, pricing, and more about TimestreamDB.

TimescaleDB is an open source time series database built on top of the relational database PostgreSQL and provides high performance and scalability. The following is a detailed description of the installation process, database creation, and data addition, deletion, and modification of TimescaleDB: 1. Installation of TimescaleDB: a. Installing PostgreSQL: The first step is to install the PostgreSQL database, which can be downloaded from the official website and installed according to the instructions. b. Install the TimescaleDB extension: After installing PostgreSQL, you can use the following command to install the TimescaleDB extension: ```bash $ sudo apt update $ sudo apt install timescaledb-2-postgresql-13 ``` Alternatively, visit the official website of TimescaleDB and follow the instructions to choose the appropriate installation method. 2. Initialization of TimescaleDB: a. Initialize TimescaleDB extension: enter the Command-line interface of PostgreSQL, and execute the following command to initialize TimescaleDB extension: ```bash $ psql -U postgres -c "CREATE EXTENSION IF NOT EXISTS timescaledb CASCADE;" ``` b. Create a TimescaleDB database: Execute the following command to create a TimescaleDB database: ```bash $ createdb -U postgres mydb ``` 3. Adding, deleting, modifying, and querying databases: a. Creating tables: In TimescaleDB, standard SQL statements can be used to create tables. For example: ```sql CREATE TABLE conditions ( time TIMESTAMPTZ NOT NULL, location TEXT NOT NULL, temperature DOUBLE PRECISION NULL, humidity DOUBLE PRECISION NULL ); ``` b. Insert Data: Use the Insert statement to insert data into the table. For example: ```sql INSERT INTO conditions (time, location, temperature, humidity) VALUES ('2022-01-01 00:00:00', 'New York', 25.5, 70.3); ``` c. Update data: Use the UPDATE statement to update the data in the table. For example: ```sql UPDATE conditions SET temperature = 26.5 WHERE location = 'New York'; ``` d. Delete data: Use the DELETE statement to delete data from the table. For example: ```sql DELETE FROM conditions WHERE location = 'New York'; ``` e. Query data: Use the SELECT statement to query the data in the table. For example: ```sql SELECT * FROM conditions WHERE temperature > 20; ``` The above is a detailed description of the installation process of TimescaleDB, as well as the creation of the database and the addition, deletion, and modification of data. As needed, various programming language libraries or client tools can be used to easily operate and manage TimescaleDB.

OpenTSDB (Open Time Series Database) is a powerful distributed temporal database used to store and analyze massive amounts of temporal data. It is built based on Hadoop and HBase and can quickly process large-scale temporal data, suitable for storing and querying various monitoring, logging, alarm and other temporal data. The following is the installation process of OpenTSDB: 1. Install Hadoop and HBase OpenTSDB relies on Hadoop and HBase, so they need to be installed first. You can install according to the official document's instructions. 2. Download OpenTSDB Accessing the GitHub page of OpenTSDB（ https://github.com/OpenTSDB/opentsdb ）Download and extract the latest version of the source code. 3. Compile and Build OpenTSDB In the root directory of the OpenTSDB source code, execute the following command to compile and build OpenTSDB: ```shell ./build.sh ``` 4. Configure OpenTSDB In the root directory of the OpenTSDB source code, there is an example configuration file called opentsdbconf. You can copy the file and make corresponding modifications as needed: ```shell cp opentsdb.conf.example opentsdb.conf ``` 5. Start HBase Execute the following command to start HBase: ```shell <hbase-installation-dir>/bin/start-hbase.sh ``` 6. Create HBase table Create an HBase table using the command line tool provided by OpenTSDB: ```shell ./src/create_table.sh ``` 7. Start OpenTSDB In the root directory of the OpenTSDB source code, execute the following command to start the OpenTSDB service: ```shell ./build/tsdb tsd --port=4242 --staticroot=build/staticroot --cachedir=/tmp --auto-metric ``` At this point, OpenTSDB has been successfully installed and started. The following is the implementation of database creation and data addition, deletion, modification, and query: 1. Create a database The OpenTSDB database is dynamically created by writing corresponding data points. Data points refer to structures that contain timestamps, metric names, and corresponding values. By sending HTTP requests to write data points to OpenTSDB, a new database can be created. 2. Increase of data To add data to the database in OpenTSDB, you can send an HTTP request to send the data point as a payload to the write gateway of OpenTSDB ('/api/put'). The format of data points is as follows: ```json { "metric": "cpu_usage", "timestamp": 1620573640, "value": 75.6, "tags": { "host": "server1", "region": "us-west" } } ``` 3. Data deletion To delete data in OpenTSDB, you can send an HTTP request, which sends the deletion condition as a payload to the OpenTSDB deletion gateway ('/api/query'). The deletion criteria can be specified based on time range, indicator name, and label. 4. Data modification The data in OpenTSDB is immutable and cannot be directly modified. If data needs to be modified, the original data needs to be deleted first, and then new data needs to be added. 5. Data Query To query data in OpenTSDB, you can send an HTTP request and send the query criteria as payloads to the OpenTSDB query gateway ('/api/query'). The query criteria can be specified based on time range, indicator name, and label. The query result will return a JSON array containing qualified data points. The above is the installation process of OpenTSDB, as well as the implementation of database creation and data addition, deletion, and modification. Through OpenTSDB, temporal data can be efficiently stored and queried, providing strong support for temporal data analysis.

CrateDB is an open-source distributed NoSQL database designed to handle massive amounts of data and high concurrency read and write operations. It uses SQL language for data operations, providing features such as horizontal scalability, high availability, and real-time queries. The process of installing CrateDB is as follows: Step 1: Check system requirements Firstly, ensure that your system meets the requirements of CrateDB. CrateDB supports operating systems such as Linux, Windows, and Mac OS, and requires the latest version of the Java Runtime Environment (JRE) to be installed. Step 2: Download and unzip CrateDB Visit the official website of CrateDB（ https://crate.io ）Or GitHub page（ https://github.com/crate/crate ）Download the latest version of the CrateDB compressed package. Extract the downloaded files and install CrateDB in your preferred directory. Step 3: Start CrateDB Open a terminal or command prompt, navigate to the installation directory of CrateDB, and execute the following command to start CrateDB: ``` bin/crate ``` Step 4: Access the CrateDB console Open a web browser and enter in the address bar“ http://localhost:4200 Then access the management console of CrateDB. Step 5: Create a database In the CrateDB console, click the "Create Table" button to create a new table, define the fields and data types of the table. Step 6: Add, delete, modify, and query data In the SQL editor of the CrateDB console, you can execute SQL statements to add, delete, modify, and query data. Here are some example actions: Add data: ``` Insert INTO table name (field 1, field 2,...) Values (value 1, value 2,...); ``` Delete data: ``` DELETE From table name WHERE condition; ``` Update data: ``` UPDATE table name SET field=new value WHERE condition; ``` Query data: ``` SELECT * From table name WHERE condition; ``` These operations can be achieved by entering corresponding SQL statements in the SQL editor of the CrateDB console. It should be noted that the "table name", "field", "value", etc. in the above example need to be replaced with the actual table and field names, and operated according to the corresponding grammar rules. Through the above steps, you can complete the installation of CrateDB, database creation, and data addition, deletion, modification, and query operations.