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Das Große Einmaleins Der Nosql Datenbanken Das große Einmaleins der NoSQL Datenbanken Felix Gessert [email protected] @baqendcom Slides: slideshare.net/felixgessert Article: medium.com/baqend-blog About me NoSQL research for PhD dissertation CEO of startup for high-performance serverless development based on caching and NoSQL Why NoSQL? Architecture Typical Data Architecture: Analytics Reporting Data Mining Analytics Data Warehouse The era Data of one-size-fits-all database systems is over Specialized dataNoSQLsystems Operative Database Applications Data Data Management The Database Explosion Sweetspots RDBMS Parallel DWH NewSQL General-purpose Aggregations/OLAP for High throughput ACID transactions massive data amounts relational OLTP Wide-Column Store Document Store Key-Value Store Long scans over Deeply nested Large-scale structured data data models session storage Graph Database In-Memory KV-Store Wide-Column Store Graph algorithms Counting & statistics Massive user- & queries generated content The Database Explosion Cloud-Database Sweetspots Realtime BaaS Managed RDBMS Managed Cache Communication and General-purpose Caching and collaboration ACID transactions transient storage Azure Tables Wide-Column Store Wide-Column Store Backend-as-a-Service Very large tables Massive user- Small Websites generated content and Apps Google Cloud Amazon Elastic Storage MapReduce Managed NoSQL Object Store Hadoop-as-a-Service Full-Text Search Massive File Big Data Analytics Storage How to choose a database system? Many Potential Candidates Question in thisApplicationtutorialLayer: Nested Billing Data Session data Files Application Data How to approach the decision problem? Friend Google Cloud Storage network requirements database Recommen- Cached data Search Index & metrics dation Engine Amazon Elastic MapReduce NoSQL Databases „NoSQL“ term coined in 2009 Interpretation: „Not Only SQL“ Typical properties: ◦ Non-relational ◦ Open-Source ◦ Schema-less (schema-free) ◦ Optimized for distribution (clusters) ◦ Tunable consistency NoSQL-Databases.org: Current list has over 150 NoSQL systems NoSQL Databases Two main motivations: Scalability Impedance Mismatch ID Line Item 1: … Customer Line Item2: … Payment: Credit Card, … User-generated data, Request load Line Items ? Orders Payment Customers Scale-up vs Scale-out Scale-Up (vertical Scale-Out (horizontal scaling): scaling): More RAM More CPU More HDD Commodity Hardware Shared-Nothing Architecture Schemafree Data Modeling RDBMS: NoSQL DB: Item[Price] - Item[Discount] SELECT Name, Age FROM Customers Implicit schema Customers Explicit schema Open Source & Commodity Hardware Commercial DBMS Open-Source DBMS Specialized DB hardware Commodity hardware (Oracle Exadata, etc.) Highly available network Commodity network (Infiniband, Fabric Path, etc.) (Ethernet, etc.) Highly Available Storage (SAN, Commodity drives (standard RAID, etc.) HDDs, JBOD) NoSQL System Classification Two common criteria: Data Consistency/Availability Model Trade-Off Key-Value AP: Available & Partition Tolerant Wide-Column CP: Consistent & Partition Tolerant Document CA: Not Partition Graph Tolerant Key-Value Stores Data model: (key) -> value Interface: CRUD (Create, Read, Update, Delete) users:2:friends {23, 76, 233, 11} Value: users:2:inbox [234, 3466, 86,55] An opaque blob users:2:settings Theme → "dark", cookies → "false" Key Examples: Amazon Dynamo (AP), Riak (AP), Redis (CP) Wide-Column Stores Data model: (rowkey, column, timestamp) -> value Interface: CRUD, Scan Versions (timestamped) Row Key Column content : "<html>…" com.cnn.www contentcontent: :"< "<htmlhtml>…">…" title : "CNN" crawled: … Examples: Cassandra (AP), Google BigTable (CP), HBase (CP) Document Stores Data model: (collection, key) -> document Interface: CRUD, Querys, Map-Reduce JSON Document ID/Key order-12338 { order-id: 23, customer: { name : "Felix Gessert", age : 25 } line-items : [ {product-name : "x", …} , …] } Examples: CouchDB (AP), Amazon SimpleDB (AP), MongoDB (CP) Graph Databases Data model: G = (V, E): Graph-Property Modell Interface: Traversal algorithms, querys, transactions Nodes WORKS_FOR Properties company: since: 1999 Apple salary: 140K name: value: John Doe 300Mrd Edges Examples: Neo4j (CA), InfiniteGraph (CA), OrientDB (CA) Soft NoSQL Systems Not Covered Here Search Platforms (Full Text Search): ◦ No persistence and consistency guarantees for OLTP ◦ Examples: ElasticSearch (AP), Solr (AP) Object-Oriented Databases: ◦ Strong coupling of programming language and DB ◦ Examples: Versant (CA), db4o (CA), Objectivity (CA) XML-Databases, RDF-Stores: ◦ Not scalable, data models not widely used in industry ◦ Examples: MarkLogic (CA), AllegroGraph (CA) CAP-Theorem Only 2 out of 3 properties are Consistency achievable at a time: ◦ Consistency: all clients have the same view on the data Partition Availability ◦ Availability: every request to a non- Tolerance failed node most result in correct response ◦ Partition tolerance: the system has to continue working, even under arbitrary network partitions Impossible Eric Brewer, ACM-PODC Keynote, Juli 2000 Gilbert, Lynch: Brewer's Conjecture and the Feasibility of Consistent, Available, Partition-Tolerant Web Services, SigAct News 2002 CAP-Theorem: simplified proof Problem: when a network partition occurs, either consistency or availability have to be given up Block response until Response before ACK arrives successful replication Consistency Availability Value = V1 Replication Value = V0 N1 N2 Network partition NoSQL Triangle Relational Data models Key-Value Every client can always Wide-Column Document-Oriented read and write A CA AP Oracle, MySQL, … Dynamo, Redis, Riak, Voldemort Cassandra SimpleDB, C CP P All clients share the Postgres, MySQL Cluster, Oracle RAC All nodes continue same view on the data BigTable, HBase, Accumulo, Azure Tables working under network MongoDB, RethinkDB partitions Nathan Hurst: Visual Guide to NoSQL Systems http://blog.nahurst.com/visual-guide-to-nosql-systems PACELC – an alternative CAP formulation Idea: Classify systems according to their behavior during network partitions yes no Partiti on No consequence of the CAP theorem Avail- Con- Laten- Con- ability sistency cy sistency AL - Dynamo-Style AC - MongoDB CC – Always Consistent Cassandra, Riak, etc. HBase, BigTable and ACID systems Abadi, Daniel. "Consistency tradeoffs in modern distributed database system design: CAP is only part of the story." Serializability Not Highly Available Either Global serializability and availability are incompatible: Write A=1 Write B=1 Read B Read A 푤1 푎 = 1 푟1(푏 = ⊥) 푤2 푏 = 1 푟2(푎 = ⊥) Some weaker isolation levels allow high availability: ◦ RAMP Transactions (P. Bailis, A. Fekete, A. Ghodsi, J. M. Hellerstein, und I. Stoica, „Scalable Atomic Visibility with RAMP Transactions“, SIGMOD 2014) S. Davidson, H. Garcia-Molina, and D. Skeen. Consistency in partitioned networks. ACM CSUR, 17(3):341–370, 1985. Where CAP fits in Negative Results in Distributed Computing Asynchronous Network, Asynchronous Network, Unreliable Channel Reliable Channel Atomic Storage Atomic Storage Impossible: Possible: CAP Theorem Attiya, Bar-Noy, Dolev (ABD) Algorithm Consensus Consensus Impossible: Impossible: 2 Generals Problem Fisher Lynch Patterson (FLP) Theorem Lynch, Nancy A. Distributed algorithms. Morgan Kaufmann, 1996. ACID vs BASE ACID BASE „Gold standard“ Model of many Basically for RDBMSs Atomicity NoSQL systems Available Consistency Soft State Eventually Isolation Consistent Durability http://queue.acm.org/detail.cfm?id=1394128 Data Models and CAP provide high-level classification. But what about fine-grained requirements, e.g. query capabilites? Outline NoSQL Foundations and • Techniques for Functional Motivation and Non-functional Requirements • Sharding The NoSQL Toolbox: • Replication Common Techniques • Storage Management • Query Processing NoSQL Systems Decision Guidance: NoSQL Decision Tree Functional Techniques Non-Functional enable Sharding enable Data Scalability Scan Queries Range-Sharding Hash-Sharding Entity-Group Sharding Write Scalability ACID Transactions Consistent Hashing Shared-Disk Functional Central Read Scalability Replication Conditional or Atomic Writes Elasticity Require- Committechniques/Consensus Protocol Operational Synchronous ments from AsynchronousNoSQL ConsistencyRequire- Joins Primary Copy Update Anywhere the databases Write Latencyments Storage Management applicationSorting employ Logging Read Latency Update-in-Place Caching Filter Queries In-Memory Storage Write Throughput Append-Only Storage Query Processing Read Availability Full-text Search Global Secondary Indexing Local Secondary Indexing Write Availability Query Planning Analytics Framework Aggregation and Analytics Materialized Views Durability http://www.baqend.com /files/nosql-survey.pdf Functional Techniques Non-Functional Sharding Data Scalability Scan Queries Range-Sharding Hash-Sharding Entity-Group Sharding Write Scalability ACID Transactions Consistent Hashing Shared-Disk Read Scalability Conditional or Atomic Writes Elasticity Joins Sorting Sharding (aka Partitioning, Fragmentation) Horizontal distribution of data over nodes Shard 1 [G-O] FranzPeter Shard 2 Shard 3 Partitioning strategies: Hash-based vs. Range-based Difficulty: Multi-Shard-Operations (join, aggregation) Sharding Hash-based Sharding Implemented in ◦ Hash of data values (e.g. key) determinesMongoDB, Riakpartition, Redis(, shard) ◦ Pro: Even distribution Cassandra, Azure Table, ◦ Contra: No data locality Dynamo Implemented in Range-based Sharding ◦
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