18 eyes-Evaluation of a database _ database

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Background

Whether you are an investor, a CTO, or an architect, if you need to choose a database product at a very central place, or if you are investing in a database product or team, this article will certainly provide you with a good guide.

Now the database classification has been dbranking in detail:

Https://db-engines.com/en/ranking

Complete ranking  
relational DBMS  
Key-value stores  
Document stores  
Graph DBMS time  
Series DBMS  
RDF Stores  
Object oriented DBMS  
Search engines  
multivalue DBMS  
Wide column stores  
Native XML DBMS  
Content Stores  
Event stores  
navigational DBMS  

This article mainly evaluates the relational database, and the relational database is divided into:

OLTP: Online transaction processing  
  
OLAP: Analytic database  
  
Htap: Mixed database (both to support online transactions and to support online analysis)  

Evaluate database products with 18 eyes 1, SQL compatibility

SQL syntax (Multidimensional Analysis, windows, subqueries, CTE, DDL transactions, triggers, rules, event triggers)

concurrency control capabilities

Transaction ISOLATION LEVEL Support

Supported data types

Supported operators

Supported indexes

Client Language interface

Service-side programming interfaces

Partitioning table Capabilities

Manage GUI, admin interface, management function, manage view etc 2, optimizer capability

Cost-based execution plan

Implementation plan based on genetic algorithm

How many methods of data access

Which SQL rewrite rules are supported

Which execution nodes are supported in parallel

Which join algorithms are supported

What sort algorithms are supported 3, scaling capabilities

Parallel capability (single machine parallel, multiple machines parallel)

Storage capacity (Row and column storage, compression, stockpiling, tree Save)

Extensibility-scale up capability

Extensibility-scale out capability

Data replication capacity 4, computing power

Vector Computing

Jit

FPGA, GPU Computing expansion capacity 5, kernel expansion capabilities

Kernel Scalability (custom udf,idx,op,type, Windows, aggregations, external data sources, PL extension Interface) 6, reliability, availability, stability

Multi-copy capability

Backup capabilities

Recovery capabilities (Point-in-time recovery, parallel backup recovery, etc.)

Disaster tolerance capacity

Cross-border rollback capability

Ha capacity

Crash recovery Capability 7, Security (authentication method, encryption type, transparent encryption type, transparent encrypted storage) 8, other features

(Flow calculation, graph calculation, GIS capability, recommendation algorithm, timing, NOSQL, search, etc.)

Valuation 9, Kernel development language, model, platform compatibility, product weakness

C

threading model, Process model,

Linux,unix, Windows, ...

What are the product design, architectural weaknesses. 10, code maturity, completion degree 11, roadmap 12, main code contributor 13, performance

Tpc-b, Tpc-h, Tpc-ds, Tpc-c, Sysbench (OLTP), TCO 14, application scenarios, case 15, ecological

Business Ecology: Universities, database manufacturers, Technology service vendors, cloud vendors, user groups, application developers, language ecology, investment side of the ecological

Community status

Community Active Degree

The ability to integrate with other ecosystems, Hadoop, Spark, .... 16, the future development potential 17, cost

Learning costs

Development costs

Cost of operation and maintenance

Management costs

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