-
Notifications
You must be signed in to change notification settings - Fork 10
Real Time Analysis: LunarMax
Real time computation is becoming imperative in this big data era. For analyzing big throughput data stream, people want a quick result, not a tedious weekly waiting as what out-dated database systems do in old ways.
The computer hardware development empowers the ability of real time computation. Memory price keeps dropping, and CPU cores are growing. Now in a small size data center, server has 32GB+ memory, multiple cores and TBs HDD is very common, which gives us a big room to design efficient in-memory data structure to perform real-time analysis.
LunarMax is the real time module integrated in LunarBase that provides extremely fast calculation for any of your data. Any hot part of properties specified like rt_analysable = string:name, int:payment, int:age, string:product, are kept a compressed copy in an internal in-memory file system, so that any computation on them has no disk latency. You will load any data on demand. Since LunarBase is capable of managing 64TB data, and in most of the cases, not all dimensions are needed in real-time, so LunarBase persists all but loads in memory only the hot part of what you specify.
We talk about it in the following sectors, see how LunarMax greatly accelerate your business and simplify your IT environment:
In-Memory File System: Memory estimation
High Availability
1 Home
1.1 summary
1.2 System Preparation
1.3 When LunarBase is your best choice
1.4 Benchmark
1.5 Power consumption
2 Data Model And Process
2.1 Why internal big cache
2.2 Memory Management: LunarMMU
2.3 Garbage Collection
2.4 Transaction Log
2.5 JOIN via materialized view
3 Real Time Computation: LunarMax
3.1 In-Memory File System: Memory Estimation
3.2 Configuration
3.3 Use SSD as a cheaper memory
3.4 Data Safety
3.5 HE Server VS. Cluster
3.6 High Availability
4 Create a database
4.1 Three modes
4.2 creation.conf settings
4.3 Table space
4.4 Multiple Instance
4.5 Database Status
4.6 Remove and Restore a table
5 Insertion
5.1 Insert as normal record
5.2 Insert to search engine
6 Query
6.1 Point Query
6.2 Result Handler: register your own event handler
6.3 Interpreter Pattern: complex query conditions
6.4 Range Query
6.5 Full-text Search
6.6 Algebraic Logical Query
8 Deletion
9 Materialized view
9.1 Eventual consistency
9.2 Update
9.3 MVCC in LunarBase
9.4 Easy JOIN via denormalization
9.5 CRUD in view
10 Distributed integration with
10.1 Kafka
10.2 Storm
10.3 Spark
11 Storage: Lunar Virtual File System
13 Roadmap of LunarBase future
15 FAQ