Distributed Architecture of - NTNU · Need for a distributed architecture ... SAP HANA and IBM DB2...

Distributed Architecture of Oracle Database In-memory

Håkon ÅmdalSuresh Kumar Mukhiya

Niloy Mukherjee, Shasank Chavan, Maria Colgan, Dinesh Das, Mike Gleeson, Sanket Hase, Allison Holloway, Hui Jin, Jesse Kamp, Kartik Kulkarni, Tirthankar Lahiri, Juan Loaiza, Neil Macnaughton,

Vineet Marwah, Atrayee Mullick, Andy Witkowski, Jiaqi Yan, Mohamed Zait

1/25

Motivation

● Ad-hoc real-time analysis (OLAP)○ High performance○ Large amount of data

● All without explicit optimizer plan changes or query rewrites

● … while still keeping the system suitable for transactional workloads (OLTP)

2/25

Oracle Database In-Memory Dual Format● Row storage for online transactional processing (OLTP)● Column storage for online analytical processing (OLAP)● Database objects optimized for memory, while still being persisted on disk.

○ Memory is cheap○ From cache on disk-access to primary storage.

3/25

Need for a distributed architectureScaling out

● Scaling out allows for elastic expansion● Avoid single point of failure● Hard to program?

Scaling up

● Majority of analytics do not process huge datasets at the same time

● Cheap hardware on a single server can process 90% of Facebook’s jobs.

● Main memory bus might become bottleneck

● Simpler implementation?● Single point of failure● Long recovery period

“We are motivated by these observations to design an extremely scalable, high-available fault-tolerant distributed architecture within the Oracle Database In-Memory Option”

4/25

Distributed Oracle DBIM

● Oracle Real Application Cluster (RAC) allows for scaling ut. across multiple machines.

● “Shared nothing”-architecture● Persisted in row-based blocks

5/25

In-Memory compression units (IMCUs)

● Compressed with user-defined compression levels

○ Optimized for OLTP○ Optimized for OLAP○ Optimized for Storage

● High performance○ Single Input Multiple Data

(SIMD) instructions○ In-Memory Storage indexes○ Bloom filter based joins

6/25

Shared Database Buffer Cache● Global Cache Service (GCS) tracks

and maintains locations and access modes of all data blocks in the global cache.

● Handle all OLTP operations● Guarantees strict ACID and

robustness properties● Cache Fusion Protocol

7/25

In-memory column store● Shared-nothing container of

in-memory segments on each instance

● Distributed together with the underlying row blocks

● Falls back to disk storage if not present in-memory

8/25

In-memory Transaction manager● Each server is responsible for

transactional consistency for incoming DML statements

● When looking up the transactional log causes too much overhead, the IMCUs are rebuilt

9/25

a. extremely scalable application-transparent distribution of IMCUs across a RAC cluster allowing for efficient utilization of collective memory across in-memory column stores

b. high availability of IMCUs across the cluster guaranteeing in-memory fault-tolerance for queries

c. application-transparent distribution of IMCUs across NUMA nodes within a single server to improve vertical scale-up performance

d. efficient recovery against instance failures by guaranteeing minimal rebalancing of IMCUs on cluster topology changes

e. seamless interaction with Oracle’s SQL execution engine ensuring affinitized high performance parallel scan execution at local memory bandwidths, without explicit optimizer plan changes.

Distribution manager

10/25

Distribution scheme● Partition● Subpartition● Block range● Auto

11/25

Distribution mechanism● Global phase for

distribution consensus

● Decentralized population phase

○ Each instance comes up with

the same object location using a hashing algorithm

○ Spread equally across a NUMA node

12/25

Redistribution● If a server goes down, a

new distribution is calculated

● Only the objects that has been redistributed is moved, the other ones stay the same.

13/25

Availability● None● 1-safe● (N-1)-safe

14/25

Distributed SQL Execution

15/25

Uniqueness of Architecture

● Paper Compares uniqueness with: SAP HANA and IBM DB2 with BLU with respect to○ Distribution ○ Scalability○ Availability○ Recovery

● The architecture provides complete scale out solution with collective memory utilization, redundancy, availability and efficient failure handling by redistribution.

16/25

Evaluation

● Hardware Setup● Distribution Experiments● Distributed Query Execution● In-Memory Distribution Awareness● In-Memory fault Tolerance● NUMA Aware Query Execution

Evaluation to Validate Quality Attributes like:

● Performance ● Scalability ● Availability

17/25

Evaluation- Hardware Setup

● Conducted on Oracle Exadata Database Machine, ○ a state of the art database SMP Server and storage cluster system

● NUMA Experiment is conducted on an X4-8 single node machine○ with 8 15-core Intel Xeon processor and 2TB DRAM

● Rest of the experiments are conducted on X4-2 RAC Configuration○ comprising up to 8 database server nodes, each with 2 12-core Intel Xeon processor and

256GB DRAM and 14 shared storage servers amounting to 200TB total storage capacity

18/25

Evaluation- Distribution Experiments

● To verify whether IMCU throughput scales out with the number of database server instances in the RAC Cluster.

● Two experiments were performed:○ Non-partitioned Table Distribution

■ 13-column and 1 billion row non-

partitioned atomic table with size of 64 GB

○ Composite-partitioned Table Distribution

■ TPC-H lineitem schema is chosen for this experiment.

19/25

Evaluation- Distributed Query Execution

● - set of 3 experiments performed in 13-column 64 gb atomics table

● Table is auto-distributed based on block ranges without redundancy

● Four sets of query sets are selected for each of these experiments

20/25

Evaluation- Distributed Query Execution

● Q1, Q2, Q3 non-linear scale out where queries are CPU-Bound

● Queries in set 4 exercise in-memory storages. Throughput of such queries is not expected to scale with number of instances.

21/25

Evaluation - In Memory Distribution Awareness

● To observe and validate impact of in-memory distribution awareness in execution of cluster-wide analytic query performance.

● Performance gains in orders of 20x to 40x over executions with distribution awareness disabled in parallel query granule generation phase.

22/25

Evaluation - In Memory Fault Tolerance

● To validate in-memory fault tolerance of distributed query execution under 1-safe redundancy.

● Single instance failure has no visible effect on the elapsed times of the queries.

23/25

Evaluation - NUMA Aware query Execution

● To observe weather IMCU NUMA-affined query execution yields better throughput compared to the same in-memory execution but without NUMA awareness.

● 150-250% improvements in query elapsed times when execution in IMCU NUMA-aware.

24/25

Conclusion

● This paper summarizes:○ Motivation for development of in-memory database optimized for

OLTAP environment○ Fault-tolerant distributed architecture of Oracle database in-memory

option ○ Uniqueness of the architecture among its peers○ Evaluation with sets of experiments showing how performance can be

enhanced with in-memory option

25/25