In Memory Cahce Structure

Jun 7, 20171 like452 views

Cache solutions that can be used when developing applications have been examined. Redis, MemCache, JCache, and Hazelcast comparisons were made. Performance, Security, Storage Capability and Eviction Policy, Maintenance, Reliability, Cost and also Who's using what.

IN-MEMORY DATA STRUCTURE
Mehmet Ali TAŞTAN

IN-MEMORY DATA STRUCTURE
01
What is the
problem?
02
Which Advice?
03
How to better?
04
How to benefit?
05
Q & A
• Various Architect
• Distributed System
• Management
Complexity
• Recommendation
• Evidence
• Comparison
• Memcahce
• Redis
• Hazelcast
• Performance
• Back-up
• Management
• ???

• Valuable Data
• Different Location
• Various Architect
• Distributed System
• Management Complexity
• Share Some Information
• Multi End-points
• Uninterrupted Connection
IN-MEMORY DATA STRUCTURE

• Memcached
• Redis
• Hazelcast
• How to make? (ie. )
IN-MEMORY DATA STRUCTURE

IN-MEMORY DATA STRUCTURE
• Expensive (RAM Price)

IN-MEMORY DATA STRUCTURE
And alse, We need
• Atomicity
• Easy & Fast Support
• Change & Upgrade [Smoothly]
Solution(s)
• Stack Overflow (last answer)
• Twitter (also uses Memcached to store 15
month-old tweets)
• AT&T – (customer support)

IN-MEMORY DATA STRUCTURE
• Redis is the proven product
• Read [fast]
• Write [faster]
• Bulk Insertion
• Use REST API
• Atomicity
• Similar Structure, supports data structures
• Strings, Hashes, Lists, Sets, Sorted Sets with range queries
• Bitmaps, Hyperloglogs
• Geospatial indexes with radius querie
• Replication, Transaction [like Rollback], different levels of on-disk persistence,
Cluster Solution

• NoSQL
• Data Models (Need-Based)
• Back-up
• Backward_Compatibility
IN-MEMORY DATA STRUCTURE

Top 5 Redis use cases *
• Session Cache
• Full Page Cache (FPC)
• [to use Magento]
• Queues
• in memory storage engine to do list
and set operations
• Leaderboards/Counting
• Sets and sorted sets
• Ex: So to pull the top 10 users
from a sorted set
• Pub/Sub
• Ex: Social network connections events
[* http://objectrocket.com/blog/how-to/top-5-redis-use-cases]
IN-MEMORY DATA STRUCTURE

Conclusion
• Suitable for cache if RAM is not issue
• Infrastructure will not commodity hardware
• Distributed and Centralizes as Service
• Better than Mem-cache
• Near Real Time Cache
IN-MEMORY DATA STRUCTURE

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Exceptions allow developers to handle error cases expected to occur infrequently. Ideally, good test suites should test both normal and exceptional behaviors to catch more bugs and avoid regressions. While current research analyzes exceptions that propagate to tests, it does not explore other exceptions that do not reach the tests. In this paper, we provide an empirical study to explore how frequently exceptional behaviors are tested in real-world systems. We consider both exceptions that propagate to tests and the ones that do not reach the tests. For this purpose, we run an instrumented version of test suites, monitor their execution, and collect information about the exceptions raised at runtime. We analyze the test suites of 25 Python systems, covering 5,372 executed methods, 17.9M calls, and 1.4M raised exceptions. We find that 21.4% of the executed methods do raise exceptions at runtime. In methods that raise exceptions, on the median, 1 in 10 calls exercise exceptional behaviors. Close to 80% of the methods that raise exceptions do so infrequently, but about 20% raise exceptions more frequently. Finally, we provide implications for researchers and practitioners. We suggest developing novel tools to support exercising exceptional behaviors and refactoring expensive try/except blocks. We also call attention to the fact that exception-raising behaviors are not necessarily “abnormal” or rare.

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