The document discusses problems with the existing Redis implementation and requirements for a new solution. It evaluates Aerospike and Apache Ignite as potential replacements. Aerospike is highlighted as a good fit due to its support for sharding, ACID compliance, and ability to store data on disk or RAM for high availability and reduced costs compared to RAM-only solutions.

2. Problems
- Multiple Redis instances on 1 server
- Each instance contains some types of data
- 200000 users active /s
- About 5 milions records in memory
- Master-Slave structures
* Redis 1 instances → 1 core → Bottleneck read/write
* No cluster
* Scale out ???

4. Requirements
● Horizontal Scale
● Fast read for real time access
● Use full core of CPU
● High availability

5. Replacements
● Aerospike
● Redis cluster
● Apache ignite

6. Aerospike
● Written in C
● Opensource
● Community Edition (free) and Enterprise
Edititon
● 1177 stars on Github

7. Aerospike
● Sharding supported
● ACID (Atomicity, Consistency, Isolation, Durability)
●
Data can be saved to disk or RAM or hybrid storage
(indexes in RAM, data in disk)
● Self managing
● Optimized for SSD
● Reduce money (Less server, SSD is more cheaper than
RAM)
● Proved by AppNexus, Inmobi,...

9. Aerospike
● Multicast
● No master/slave node. Each node contains
some different primary shards and some
different replica shards

10. Storage
● Pure DRAM without persistence
● DRAM with persistence
● Flash storage (SSDs)

11. Namespace
– 1 database = many namespaces
– How data is stored: RAM or DISK
– How many replicas
– When record expire

12. Sets
● Group of records
● 1 namespace = many sets

13. Record
● Key
● Meta-data (generation, time to live)
● Bins (1 or more bins) → as rows and their value
in DBMS

14. Apache Ignite
Many features
● Written in java
● In-memory database
● Data grid
● Accelator for Hadoop and Spark
….

17. Shared RDDs among Spark jobs

18. Indexed SQL Query
● SparkSQL does not index RDD
● IgniteSQL can index its cached data by field or
using lucene index for text.
Examples:
val cacheRdd = igniteContext.fromCache("partitioned")
cacheRdd.savePairs(sparkContext.parallelize(1 to 10000,
10).map(i => (i, i)))
val cacheRdd = igniteContext.fromCache("partitioned")
val result = cacheRdd.sql(
"select _val from Integer where val > ? and val < ?",10,100)

19. public class Person implements Serializable {
/** Person ID (indexed). */
@QuerySqlField(index = true)
private long id;
/** First name (not-indexed). */
@QuerySqlField
private String name;
/** Resume text (create LUCENE-based TEXT index for this
field). */
@QueryTextField
private String resume;
/** Salary (indexed). */
@QuerySqlField(index = true)
private double salary;
…..
}