1 Introduction to NOSQL Université Paris-Est Marne la Vallée, LIGM UMR CNRS 8049, France January 31, 2014
2 Motivations NOSQL stands for Not Only SQL
3 Motivations Exponential growth of data set size (161Eo (2006) to 988Eo (2010) created and replicated data) Connectivity of data Structure of documents (structured, semi-structured, unstructured) RDBMS performance Architecture of database oriented applications
4 Presentation Example Solutions CAP and NOSQL stores CAP conjecture from Brewer 1 and theorem 2 : A service that operates fully or not (in fact more like Atomic) Availability: The service is available Partitioning tolerance: no set of failures less than total network failure is allowed to cause the system to respond incorrectly. 1 talk at ACM PODC S. Gilbert, N.Lynch: Brewer s conjecture and the feasibility of consistent, available, partition-tolerant web services. SIGACT News 33(2): (2002)
5 Presentation Example Solutions CAP and NOSQL stores
6 Presentation Example Solutions CAP and NOSQL stores Example from 3 2 nodes (N 1 and N 2 sharing a piece of info V with value V 0. A (writer) and B (reader) are reliable algos. 3 Julian Browne s blog:
7 Presentation Example Solutions CAP and NOSQL stores (1) A writes a new value V1, (2) Message is passed from N 1 to N 2, (3) B reads the new value V1
8 Presentation Example Solutions CAP and NOSQL stores In case of a partitioned network (2), at (3) B reads an inconsistent value.
9 Presentation Example Solutions CAP and NOSQL stores if M is synchronous: latency issues if M is asynchronous, N 1 as no way to know whether N 2 has received the message. if we want high availability of A and B, N 1 and N 2 to be tolerant to partition partitioning then we must accept that B reads inconsistent data.
10 Presentation Example Solutions CAP and NOSQL stores CA, i.e. drop partition tolerance : everything on one machine. No scaling out. CP, i.e. drop availability: latency issues. Complex recovery issues AP, i.e. drop consistency. In fact there is a spectrum of consistency. This approach is quite popular in NOSQL stores.
11 Presentation Example Solutions CAP and NOSQL stores
12 Presentation ACID Client side Server side Different solutions to relax consistency are presented in 4 (Internet) systems must always be available. With CAP theorem, you have the choice of either CA or AP. The developer then has to deal with the adopted solution: CA: what to do in the case of a network failure? AP: does the client needs th absolute latest update all the time? Many applications can handle stale data. 4 Werner Vogels: Eventually consistent. Commun. ACM 52(1): (2009)
13 Presentation ACID Client side Server side A transaction is a sequence of database operations (read/write) Atomicity: all or none updates are executed : DB instance must go from one consistent state to another Isolation: Results of a transaction are visible to other users after a commit Durability: Commited transactions are persisted Is the responsibility of the developer but is assisted by the RDBMS.
14 Presentation ACID Client side Server side ACID in distributed RDBMS 2 Phase Commit (2PC): Phase 1: transaction coordinator asks each involved DB to precommit the operation and tell if commit is possible Phase 2: transaction coordinator asks each involved DB to commit the data. If any involved DB votoes commit then they all roll back.
15 Presentation ACID Client side Server side Example: S is a storage system, A, B and C are processes. A updates a given value in S Strong consistency: any subsequent access (by A, B or C) will return the updated value. Weak consistency: no guarantee that subsequent accesses will return the updated value. Period between update and the time at which any process is guaranteed to access it is the inconsistency window.
16 Presentation ACID Client side Server side An interesting form of weak consistency is eventual consistency where the storage system guarantees that if no other updates are made to the object then eventually all accesses will return the last updated value (e.g. DNS). Different variations of eventual consistency: Causal consistency: A communicates to B that a new update is available. Subsequent accesses by B will return the last value. Uncertain for C. Read-your-writes consistency: A will always access the last update and will never see an older value. Session consistency: same as previous but in the context of a session.
17 Presentation ACID Client side Server side More variations of eventual consistency: Monotonic read consistency: Never read an older value that you one accessed Monotonic write consistency: System guarantees to serialize the writes by the same process. These variations can be combined. For instance, monotonic reads + monotonic writes consistency is desirable.
18 Presentation ACID Client side Server side N: the number of nodes that store replicas of the data. W: the number of of replicas that need to acknowledge the receipt of the update before the update is completed R: the number of replicas that are contacted when a data object is accessed through a read operation. W+R > N: guarantees strong consistency. Ex: N=2, W=2, R=1. such a quorum protocol fails if the system can t write the W nodes.
19 Presentation ACID Client side Server side In distributed storage systems, generally N 2 Typical scenarios: Focus on fault tolerance: N=3, W=2 and R=2 Focus on very high read loads: N can be 10 or 100 and R=1 Focus on consistency: W=N Focus on fault tolerance and not consistency: W=1 with associated replica mechanisms.
20 Presentation ACID Client side Server side Configuring N, W and R: R=1, W=N for fast reads W=1, N=R for fast writes Weak/Eventual consistency when W+ R N: read/write may not overlap
21 5 stands for Basic Availability, Soft state, Eventually Consistent. requires an in-depth analysis of the operations within a logical transaction. based on consistency patterns 5 Dan Pritchett: Base An ACID alternative. ACM Queue may 2008
22 Comparison ACID Strong Weak Approach Pessimistic Optimisitic Focus on commit on availability Isolation Schema evolution difficult flexible faster Simpler Best effort
24 Standard (consistent) transaction
25 Relaxed consistency transaction In case of failure, user table may be permanently inconsistent
26 with persistent message queue with queue stored on the same machine as the database (to avoid 2PC when queueing) but we have a 2PC when dequeueing.
27 Idempotence at the rescue An idempotent operation can be applied one or several times with the same result. Update operations are generally not idempotent. In the case of balance updates, you need a way to track which updates have been applied successfully and which are still outstanding. One technique is to use a table that records the transaction identifiers that have been applied.
28 Solution handling partial failure with no 2PC transaction
29 Shared nothing OLTP OLAP Database Management systems (DBMS) are candidates for deployment in the cloud. 6 studies which DBMS are most likely to succeed on the cloud Two main approaches to study: OLTP and OLAP Decision is motivated by the architecture found on the cloud. 6 D. Abadi. Data management in the cloud: limitations and opportunities. Data Engineering. Vol 32 no
30 Shared nothing OLTP OLAP Architecture generally adopted for cloud computing Efficient for high scalability but with a high cost of data partitioning
31 Shared nothing OLTP OLAP Shared nothing How to effectively distribute the data across the nodes is crucial for performance and scalability. Important for leveraging data parallelism and to reduce the amount of data that needs to be transferred over the network during query processing.
32 Shared disk Introduction Shared nothing OLTP OLAP no need to a priori partition data across nodes since all nodes access data via shared storage. no need to move data across nodes. load balancing is easy since all nodes can service any request
33 Shared nothing OLTP OLAP Shared disk the nodes need to communicate in order to ensure data consistency (via distributed lock manager). Network must support the combined IO bandwidth of all processors, and can become a bottleneck. Shared disk are relatively cost effective for small to medium sized data warehouse.
34 Shared nothing OLTP OLAP Shared memory
35 Shared nothing OLTP OLAP Database Management systems (DBMS) are generally used to On-Line Transactional Processing (OLTP) On operationaldb of average sizes (few TB), write intensiveand requiring complete ACID transactional properties, strong data properties and response time guarantees. Typical use cases: item reservations (airline, concerts, etc.), on-line e-commerce, supply chain management, financial activities.
36 Shared nothing OLTP OLAP OLTP operations are structured and repetitive require detailed and up-to-date database are short, aomtic and isolated transactions
37 Shared nothing OLTP OLAP On-Line Analytical Processing deals with historical DBs of very large sizes (up to PB), read-intensive and hence relax ACID properties. Typical use case: business planning, problem solving and decision making/support. OLAP was a 4 billion $ market of the 14.6 billion $ of DB market with an annual growth of 10.3%. OLAP data are typically extracted from operational OLTP DBs sensitive data can be anonymized. So called ETL (Extract Tranform Load).
38 Shared nothing OLTP OLAP OLAP is supported by data warehouses (typically RDBMS with extended opeations (cube, roll-up, drill-down, etc.). has some historical (temporal), summarized, integrated, consolidated and multidimensional data. used for business intelligence. Read Information platform and the rise of the data scientist. J. Hammerbacher in Beautiful data (O reilly 2009) fo Facebook s evolution on this subject.
39 Shared nothing OLTP OLAP Currently OLAP is more suitable than OLTP for cloud computing fro the following reasons: Elasticity requires a shared nothing cluster architecture OLAP : effective data partitioning and parallel query processing. ACID no needed. OLTP : complex concurreny control. Shared disk is more efficient. Hard to maintain data replication across geographically distributed data centers. Security OLAP : anonymization of sensitive data coming from ETL process. OLTP : no anonymization is possible. Resistance of customers.
40 Key-value Column family Document Graph 4 categories Key-value Column family (aka Bigtable-like) Document Graph DB
41 Key-value Column family Document Graph Key-Value Origin: Amazon 7 Data model: global key-value mapping. Distributed hash mapp Systems : Voldemort (LinkedIn), Tokyo (Cabinet, Tyrant), Riak (Basho), Oracle NOSQL 7 G. De Candia et al. Dynamo: Amazon s highly available key-value store. SOSP 2007
42 Key-value Column family Document Graph Column family Origin: Google 8 Data model: a big table with column families Systems : HBase (Apache), Cassandra (Apache), HyperTable 8 F. Chang et al. Bigtable: a distributed storage system for structured data. OSDI 2006
43 Key-value Column family Document Graph Origin: Lotus notes Data model: Collections of documents where a document is a key-value collection Systems: CouchDB (Apache), MongoDB (10gen), Terrastore
44 Key-value Column family Document Graph Origin: Graph theory Data model: Nodes with properties. Typed relationships with properties Systems: Neo4J, InfiniteGraph, Sones GraphDB, Trinity (Microsoft), FlockDB (Apache), Pregel (Google)
45 Key-value Column family Document Graph
46 Key-value Column family Document Graph One can generally represent an instance in one model into another model. Implemented systems: Common features: Schemaless, no joins Main differences: consistency approach, conflict detection, concurrency control, integration of parallelization
47 More ACIDity 9 MongoDB adding durable logging storage in 1.7 Cassandra adding stronger consistency in Emil Eifrem at NOSQL exchange 2011
48 More Query languages MongoDB had one right from the start Cassandra : CQL Couchbase : UnQL Neo4J : Cypher
49 More Schema MongoDB had one right from the start Cassandra : CQL Couchbase : UnQL Neo4J : Cypher
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