NOSQL DATABASE SYSTEMS: DECISION GUIDANCE AND TRENDS. Big Data Technologies: NoSQL DBMS (Decision Guidance) - SoSe
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1 NOSQL DATABASE SYSTEMS: DECISION GUIDANCE AND TRENDS h_da Prof. Dr. Uta Störl Big Data Technologies: NoSQL DBMS (Decision Guidance) - SoSe
2 Performance / Benchmarks Traditional database benchmarks Benchmarks simulate typical usage scenarios (OLTP: TPC-C, OLAP: TPC-H, SAP benchmarks) Metrics: Performance (transactions per minute) Price/performance Benchmarks for NoSQL database systems? What is a typical NoSQL scenario? Facebook? Log analytics? Web Caching? Metrics? Scalability Availability Partition Tolerance h_da Prof. Dr. Uta Störl Big Data Technologies: NoSQL DBMS (Decision Guidance) - SoSe
3 Open research field! Up to now: Specific workloads only Benchmarks for NoSQL Systems Yahoo! Cloud Serving Benchmark YCSB(SoCC 2010) Simple operations only (read, insert, delete, range scans) No specific use-case; different workload scenarios Workload Operations Workload R Workload U Workload I Workload M 100 % Reads 100 % Updates 100 % Inserts 50 % Reads 25 % Updates 25 % Inserts h_da Prof. Dr. Uta Störl Big Data Technologies: NoSQL DBMS (Decision Guidance) - SoSe
4 Yahoo! Cloud Serving Benchmark Yahoo! Cloud Serving Benchmark: Metrics Performance For constant hardware increase throughput measure latency Scaling Scale-up: Increase hardware, data size and workload proportionally measure latency Example: Elastic Speedup: measure latency during dynamically server addition Source: Cooper et al. Benchmarking Cloud Serving Systems with YCSB; SoCC 2010 h_da Prof. Dr. Uta Störl Big Data Technologies: NoSQL DBMS (Decision Guidance) - SoSe
5 Status Quo YCSB Yahoo! Cloud Serving Benchmark Lot of variants publishes (over 560 forks on GitHub ) Including implementation errors regarding measurement as well as analysis of results (see H. Wegert: Benchmarking von NoSQL-Datenbanksystemen, master s thesis, University of Applied Sciences Darmstadt, April 2015) General challenge: Comparison of different configurations of NoSQL database systems No independent Benchmarking Council for NoSQL database benchmarking up to now (like TPC for relational database systems) h_da Prof. Dr. Uta Störl Big Data Technologies: NoSQL DBMS (Decision Guidance) - SoSe
6 Ø Operationen pro Sekunde (log) Benchmarking: Persistence Couchbase (2015) YCSB Thumbtack Technologies Version 4 Server Nodes, 4 Clients (Big Data Cluster h_da) Keine Bestätigung Eine Bestätigung Zwei Bestätigungen Drei Bestätigungen Vier Bestätigungen 10 1 I Workload U Source: Wegert. Benchmarking von NoSQL-Datenbanksystemen; 2015 h_da Prof. Dr. Uta Störl Big Data Technologies: NoSQL DBMS (Decision Guidance) - SoSe
7 Ø Operationen pro Sekunde (log) Benchmarking: Durability Cassandra (2015) YCSB Thumbtack Technologies Version 4 Server Nodes, 4 Clients (Big Data Cluster h_da) Periodic ( ms) Batch (50 ms) WAL deaktiviert 10 1 I U M Workload Source: Wegert. Benchmarking von NoSQL-Datenbanksystemen; 2015 h_da Prof. Dr. Uta Störl Big Data Technologies: NoSQL DBMS (Decision Guidance) - SoSe
8 Select the Right DBMS Choosing between NoSQL or RDBMS?! Select the right (NoSQL) DBMS?! Criteria Data Analysis Estimated size of date Complexity of data Type of navigation Consistency Requirements Query Requirements Performance Requirements (latency, scalability) Non functional Requirements (license, company policies, security, documentation etc.) Costs (including development and administration) More detailed list: Prototyping and performance analysis! h_da Prof. Dr. Uta Störl Big Data Technologies: NoSQL DBMS (Decision Guidance) - SoSe
9 System Properties: Functional Requirements Source: F. Gessert, Baqend, 2017 h_da Prof. Dr. Uta Störl Big Data Technologies: NoSQL DBMS (Decision Guidance) - SoSe
10 System Properties: Non-Functional Requirements Source: F. Gessert, Baqend, 2017 h_da Prof. Dr. Uta Störl Big Data Technologies: NoSQL DBMS (Decision Guidance) - SoSe
11 Techniques for Functional and Non-Functional Requirements Source: F. Gessert, Baqend, 2017 h_da Prof. Dr. Uta Störl Big Data Technologies: NoSQL DBMS (Decision Guidance) - SoSe
12 System Properties: Techniques Source: F. Gessert, Baqend, 2017 h_da Prof. Dr. Uta Störl Big Data Technologies: NoSQL DBMS (Decision Guidance) - SoSe
13 Select the Right DBMS: NoSQL Decision Tree F. Gessert et al. Scalable Data Management, BTW 2017 Source: F. Gessert, Baqend, 2017 h_da Prof. Dr. Uta Störl Big Data Technologies: NoSQL DBMS (Decision Guidance) - SoSe
14 One Size Fits All? Polyglot Persistence Alternative: Choose the right system for the right task! Example: Amadeus Log Service Hundreds of terabyte log data each week (SOA architecture with several servers) Architecture (Prototype, Kossmann:2012) Distributed file system (HDFS) for compressed log data NoSQL system (HBase) for storage and instant random access (indexing by timestamp and SessionID) Full text search engine (Apache Solr) for queries on log messages MapReduce framework (Apache Hadoop) for analysis (usage statistics and error) Relational DBMS (Oracle) for meta data (user infos etc.) Polyglot Persistence (by Martin Fowler) h_da Prof. Dr. Uta Störl Big Data Technologies: NoSQL DBMS (Decision Guidance) - SoSe
15 Polyglot Persistence Example (Source: Sadalage, P. J., & Fowler, M. NoSQL Distilled. Pearson Education, 2013) E-Commerce Plattform Shopping cart and session data Completed Orders Inventory and Item Price Customer social graph Key-Value DBMS Document Store DBMS RDBMS (Legacy DB) Graph DBMS h_da Prof. Dr. Uta Störl Big Data Technologies: NoSQL DBMS (Decision Guidance) - SoSe
16 Polyglot Persistence: Current Best Practice Current Best Practice (F. Gessert et al. Scalable Data Management, BTW 2017) Source: F. Gessert, Baqend, 2017 h_da Prof. Dr. Uta Störl Big Data Technologies: NoSQL DBMS (Decision Guidance) - SoSe
17 One size fits all? NewSQL DBMS Idea behind: Best of both worlds SQL ACID Non locking concurrency control High per-node performance Scale out, shared nothing architecture Opportunity 1: Development of new database systems VoltDB (Michael Stonebraker) Spanner (Google since 2017 as cloud service available!) Opportunity 2: Integration in existing database systems MySQL Cluster JSON integration MapReduce integration ( NoSQL Application Development / SQL on Hadoop) h_da Prof. Dr. Uta Störl Big Data Technologies: NoSQL DBMS (Decision Guidance) - SoSe
18 Trends: JSON Integration PostgreSQL 9.2 Native JSON support since release 9.2 (2012) Proprietary JSON Query API IBM DB2 Native JSON support since 10.5 (June 2013) Using MongoDB API IBM Informix Native JSON support since (September 2013) Using MongoDB API Oracle Native JSON Support since (July 2014) Proprietary JSON Query API h_da Prof. Dr. Uta Störl Big Data Technologies: NoSQL DBMS (Decision Guidance) - SoSe
19 Trends: JSON Integration Example DB2 JSON stored as BSON in BLOB column Source: IBM, 2013 h_da Prof. Dr. Uta Störl Big Data Technologies: NoSQL DBMS (Decision Guidance) - SoSe
20 The Evolving Database Landscape h_da Prof. Dr. Uta Störl Big Data Technologies: NoSQL DBMS (Decision Guidance) - SoSe
21 The Evolving Database Landscape h_da Prof. Dr. Uta Störl Big Data Technologies: NoSQL DBMS (Decision Guidance) - SoSe
22 NoSQL Database Systems Foundations Data Modeling NoSQL id ti Application Development Techniques for Scalability, Availability and Consistency Decision Guidance: Select the Right DBMS h_da Prof. Dr. Uta Störl Big Data Technologies: NoSQL DBMS (Decision Guidance) - SoSe
23 Big Data Technologies Course Goal: Introduction to the Big Data Technology Landscape NoSQL Database Systems Column Store Database Systems In-Memory Database Systems h_da Prof. Dr. Uta Störl Big Data Technologies: NoSQL DBMS (Decision Guidance) - SoSe
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