iservdb The database closest to you IDEAS Institute

Transcription

1 iservdb The database closest to you IDEAS Institute 1

2 Overview 2

3 Long-term Anticipation iservdb is a relational database SQL compliance and a general purpose database Data is reliable and consistency iservdb is a distributed database Designed for distributed system Scalable and Load balanced iservdb is your own database A real team with your service Store data with iservdb, compute data with iservdb, and share data with iservdb iservdb is connecting to the world Able to connect with everything you want Still stay with your lovely SQL 3

4 iservdb faces big problems Big connection Massive connections arrive at the same time Load balanced and to keep alive E.g. Ticketing problem Big computing Much usage of aggregate functions Parallel computing to burst the performance E.g. Statistics problem Big data Huge data set in the storage Distributed storage engine makes data safety and quick access E.g. Data mining problem 4

5 Simple Interface SQL Compliance Only SQL, Always SQL Keep your programs easy and stable iservdb Database iservdb is a DBMS, and iservdb database will endow the database with a new vision. Stay with your own database and coordinate everything. PostgreSQL protocol Wide variety programming language supported 5

6 iservdb Database Multi-tenant technology Every tenant is working in isolation Your intelligence is built-in your database Smart data storage Don t worry about how to store your data. Just know your data is secure, reliable, and easy access. Foreign data integration Connect everything you want. One database for all. 6

7 Keep going Intelligent Data Analysis To learn your data and your service Import and integrate your data Optimized for your service performance Dynamic Resource Allocation Keep load balanced Optimized for your resource utilization Always stay on-line 7

8 Magic Table in iservdb Database UPDATE DELETE Intelligent data analysis SELECT INSERT Magic Table Dynamic resource allocation Table is not only table Table can be dynamic contents Keep your query Nothing needs to be changed in your application Table is still table You can join and aggregate the table 8

9 Scenario iservdb Database My usage statistics My log data of the app My transactional data of ticketing system My shared table from another db My twitter Summary of user log Storage for log application Ticketing module Refer to another db Twitter module 9

10 Example: iservdb+twitter Tweet Something INSERT INTO twitter_table VALUES ( something ); twitter_table Twitter API SELECT * FROM twitter_table WHERE q= keyword ; Social Analysis 10

11 Business Intelligence in iservdb Decision Report Data Collection SELECT INSERT iservdb Information retrieval INSERT UPDATE BI processing INSERT UPDATE Raw data Raw data table is created for fast insert. BI processing table is designed for parallel computing in iservdb. Information retrieval table is set for high performance join and aggregate operations. You can put your business intelligence into iservdb and make everything fast and easy. 11

12 All access Supply Chain Group Your services Social Network Domain Knowledge Data BI Doc Cache iservdb Market Intelligence Partner Service 12

13 Use Case 1: Ticketing System Big connection problem Everyone wants to get the ticket, but only few people can finish as their wish. Too many locks waste too much time. Produce too much false UPDATE. Most users can not get the ticket, which implies database is not updated but still has to acquire pointless locks. When we know the result, iservdb can prevent from it. Better response, more customers in the future iservdb can not increase the numbers of the ticket and the probabilities to get the ticket. If customer receives good response, he would blame the destiny not the service. Otherwise, the customer is lost. 13

14 Users per second Use Case 1: Ticketing System Concurrent users This test is a ticketing simulation to show the performance. Higher is better. There are only 100 tickets. A ticket is for a user. In the scenario of 100 concurrent users, everyone can get a ticket, which is no false UPDATE, and this feature caused about 30% overhead. More concurrent users imply more false UPDATEs. This feature improved about 40% performance. 14 Feature on Feature off

15 Use Case 2: Accounting System Big computing problem Complex computation needs your processors more powerful. A big numbers of row computing causes a big trouble. Powered up by parallel computing iservdb helps to distribute data into many cells, and then every cell can process the query at the same time. With seamless migration, iservdb can scale out on heavy load and scale in on average load. Know your application Data analysis can let iservdb to know your application. iservdb leads distributed computing by distributing data. 15

16 Use Case 3: Multi-tenancy System Big data problem You have many customers, and all of their transactional data are big. Every tenant has different scale. Dynamic resource allocation With scaling technique, iservdb lets administrator feel easy to arrange the resources. Flexible allocation saves your cost. Load Balanced With data analysis and migration, iservdb can keep cells load balanced. Data analysis for each tenant makes individual data closer to the relative application. 16

17 What is your use case? iservdb 17

18 CAP theorem It is impossible to simultaneously provide guarantees for all of three circles. Consistency has to be guaranteed in a relational database Availability is the most important to a database system iservdb can meet any two of them on demand. 18

19 Transactions per second Single host v.s. Distributed system 700 Single host Distributed system Concurrent users Single host holds high performance with light load but breaks down when host limit has reached. Distributed system keeps performance stable. 19

20 Multi-entries comparison One entry ( tps) 3 entries ( tps) Cell 1 Cell 1 Cell 2 Cell 3 Both of the tests run on 3 cells with the same data distribution. Left (One entry) only finished 71% transaction and broke down. Right (3 entries) finished all tasks and load balanced with higher throughput. Network limitation is always with one entry. 20

21 Big Data Revenue 7.2 $US billions $US billions 2017