cs346: advanced databases graham cormode [email protected] distributed databases, base, cap...
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Outline
Chapter: “Distributed Databases” in Elmasri and Navathe
¨ What are distributed databases?¨ Architectural choices¨ ACID vs BASE¨ Consistency, Availability, Partition tolerance: CAP¨ NoSQL systems
Why?¨ As data gets larger, must move to distributed data management¨ Tech companies (Google, Facebook etc.) rely on distributed data
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Distributed Databases
¨ When data gets large and processing is slow, use distribution– A distributed database (DDB) managed by a distributed DBMS– Goal: split the processing into smaller pieces and spread them
¨ DDB technology combines databases with OS/Networks– Manage concurrent access to replicated data
¨ DDB is quite different to e.g. the world-wide web– Similarities: many machines, distributed around the world– Different: each website is (mostly) independent of others
Facebook and YouTube are managed independently– However: many large websites use DDB technology
Facebook can be seen as a massive distributed database
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Distributed Databases: Pros and Cons¨ DDB can be (in principle) more available
– If one machine fails, others can take over¨ DDB can (in principle) be faster
– Parallelize computation, combine results¨ DDB is (in principle) easier to expand
– Just add more machines/storage¨ “In principle” isn’t always the case
– DDB is more complicated to manage– Performance/availability may worsen in unpredictable ways
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Additional functionality of DDB
The DDB has additional or expanded roles to perform:¨ Keeping track of data distribution: where’s my data?¨ Distributed query processing: break up a query into pieces¨ Distributed transaction management: data items are distributed¨ Replicated data management: keep distribute copies of the data¨ Distributed database recovery: manage machine failures¨ Security: manage security of distributed data¨ Distributed catalog management: keep the metadataSaw some of these issues in Hadoop/MapReduce
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Distributed Architectures
¨ Many possible levels of sharing:– Shared memory: multiple processors (cores) share disk, memory– Shared disk: multiple cores share disk, but have separate memory– Shared nothing: no common storage, communicate over network
¨ ‘Shared nothing’ is the model for large distributed systems– Hadoop follows a shared nothing architecture
¨ Shared nothing pros and cons:– Can be slower: network is slower than local disk (is it? fibre is fast)– Easy to expand: add more machines to the network– Allows fragmentation (sharding): breaking the database into pieces
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Fragmentation and Replication
¨ How to split the data up among sites?– Horizontal fragmentation: subset of tuples on each machine
E.g. break up the EMPLOYEE relation by Dno– Vertical fragmentation: different columns on each machine
Name, Bdate, Address on one, Ssn, Salary, Dno on another– Mixed: break up by both horizontal and vertical
¨ How to replicate data around the system? – No replication: a unique copy exists– Fully replicated: data is copied everywhere– Partial replication: in between these two extremes
E.g. HDFS, default number of replicas is 3
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ACID vs BASE systems
¨ Recall the ACID properties of transactions– Atomicity, Consistency, Isolation, Durability
¨ Not every system requires this level of guarantee– Can trade-off guarantees for perfomance
¨ “BASE”: Basically Available, Soft-State, Eventually Consistent (coined by Eric Brewer, founder of Inktomi, 2000)– A weaker set of requirements – Drop consistency and isolation to improve availability, performance– Suits distributed settings without much competition for resources
¨ ACID vs BASE is a spectrum of possible design points– “Real internet systems are a mixture of ACID and BASE subsystems”
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CAP concepts
¨ Consistency: all processes/transactions see the same data– Equivalent to having a single, up to date copy of the data– Not easy to provide, hence much effort on concurrency
¨ Availability: is the system up and responsive to requests?– All processes can find some version of the data they need– Formally: does every request receive a response (allowing fails)
¨ Partition-tolerance: what happens when the network breaks?– Network partition: something breaks and the network divides
E.g. a router fails/crashes: messages can’t traverse the router– Does the system still operate even if messages are lost?
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Points of Comparison
¨ Consistency: strong (ACID) or weak consistency (BASE)?– Weak: processes can see operations in different orders– Weak: synchronization points bring processes into agreement
¨ Eventual consistency: system eventually reaches a consistent state– If no updates are made to an item, then reads will give same value
¨ Compared to ACID, the BASE approach is: – More focused on availability of resources– Tolerates approximate answers rather than exact– More aggressive (optimistic concurrency control)– Aims to be simpler, faster– Provides ‘best effort’ rather than guarantees
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The CAP Conjecture / Theorem
¨ Brewer made a famous “CAP conjecture” in 2000– Consistency, Availability, Partition Tolerance: pick any two– I.e. it is impractical to build a distributed system with all three
¨ Lynch and Gilbert “proved” a CAP theorem in 2002– For a specific set of distributed scenarios
¨ An example of a ‘pick two’ (from three) choice– For university: Good grades, enough sleep or a social life– For products: fast, good or cheap
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Consequences of CAP Theorem
Obtain different results from different choices: ¨ Forfeit partition tolerance (obtain consistency and availability)
– E.g. traditional centralized DBMS¨ Forfeit availability (obtain partition tolerance and consistency)
– E.g. distributed databases, protocols based on majority agreement¨ Forfeit consistency (obtain partition tolerance and availability)
– E.g. Emerging NoSQL systems¨ These concepts cut across many aspects of computer science:
– The OS and network provide availability, but no consistency– Databases are better at consistency than availability– Distributed databases want both
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NoSQL systems
¨ NoSQL systems drop support for the full relational model– Do not provide same level of reliability/availability– Do not necessarily support rich languages like SQL– Aim to have simpler design, better scaling via distribution
¨ Often support analysis via query language or MapReduce on top– Systems primarily support data storage and retrieval
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Types of NoSQL systems
¨ Key-value store: stores and retrieves data in the form (key, value)– E.g. store demographic data (values) for each user (by key)– Data is distributed, and replicated for resilience, e.g. Memcached
¨ Column store: stores data organized by column (instead of row)– Allows faster access to particular entries when data is sparse– Implemented in Hbase (database component of Hadoop system)
¨ Document store: to store and retrieve document data– E.g. to store information for very large websites (Amazon, eBay)– Each “document” can be an arbitrary collection of information– Examples include MongoDB and Apache Cassandra
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NoSQL systems: pros and cons
¨ NoSQL systems are highly popular at the moment– Scale to truly massive amounts of data– Allow analytics on top via MapReduce/Hadoop– Can be very fast to retrieve data
¨ But they also have limitations– Systems still under development, hard to make use of– Some quite primitive: just provide data storage/retrieval– Currently have to write and debug code to implement applications– Can be overkill when your data is not massive
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Summary
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¨ Motivations for Distributed Databases¨ Architectural choices for distributed databases
¨ What is shared? How much replication? ¨ ACID/BASE (Basically Available, Soft-State, Eventually Consistent)¨ Consistency, Availability, Partition tolerance: CAP
¨ Pick any two¨ NoSQL systems: key-value, column, document store
Recommended reading: Brewer’s PODC’00 KeynoteChapter: “Distributed Databases” in Elmasri and Navathe