NoSQL databases

NoSQL Databases

technology watch #2

Mar 2010

Contents

• Introduction
• Key-Value stores
• Column stores
• Document stores
• Graph Databases
• Benchmarks
• RDF & NoSQL

NoSQL Databases Mar 2010 #2

Contents

Part I

Introduction


Why NoSQL?

• Is NoSQL equal to “SQL “or to “Not Only SQL”?
• ACID doesn’t scale well
• Web apps have different needs (than the apps that RDBMS
were designed for)
– Low and predictable response time (latency)
– Scalability & elasticity (at low cost!)
– High availability
– Flexible schemas / semi-structured data
– Geographic distribution (multiple datacenters)
• Web apps can (usually) do without
– Transactions / strong consistency / integrity
– Complex queries


Some NoSQL use cases

1. Massive data volumes
– Massively distributed architecture required to store the data
– Google, Amazon, Yahoo, Facebook – 10-100K servers
2. Extreme query workload
– Impossible to efficiently do joins at that scale with an RDBMS
3. Schema evolution
– Schema flexibility (migration) is not trivial at large scale
– Schema changes can be gradually introduced with NoSQL


NoSQL pros/cons

• Advantages
– Massive scalability
– Lower cost (than competitive solutions at that scale)
– (usually) predictable elasticity
– Schema flexibility, sparse & semi-structured data
• Disadvantages
– Limited query capabilities (so far)
– Eventual consistency is not intuitive to program for
• Makes client applications more complicated
– No standardizatrion
• Portability might be an issue
– Insufficient access control


CAP theorem

• Requirements to distributed systems
– Consistency – the system is in a consistent state after an operation
• All clients see the same data
• Strong consistency (ACID) vs. eventual consistency (BASE)
– Availability – the system is “always on”, no downtime
• Node failure tolerance – all clients can find some available replica
• software/hardware upgrade tolerance
– Partition tolerance – the system continues to function even when
split into disconnected subsets (by a network disruption)
• Not only for reads, but writes as well!

• CAP Theorem (E. Brewer, N. Lynch)
– You can satisfy at most 2 out of the 3 requirements


CAP (2)

• CA
– Single site clusters (easier to ensure all nodes are always in contact)
– e.g. 2PC
– When a partition occurs, the system blocks
• CP
– Some data may be inaccessible (availability sacrificed), but the rest is
still consistent/accurate
– e.g. sharded database
• AP
– System is still available under partitioning, but some of the data
returned my be inaccurate
– e.g. DNS, caches, Master/Slave replication
– Need some conflict resolution strategy


… and also CAE

• CAE trade-off (Amazon?)
– Cost-efficiency
– High Availability
– Elasticity
• Pick any two (Cost, Availability, Elasticity)
– Cost Efficiency
• By over-provisioning you get HA and E
– High Availability
• Just make the client wait when the system is overloaded (CE and E)
– Elasticity
• If you can predict your workload, you can provide HA + CE by booking
the resources in advance

• Everyone wants HA, so the challenge is providing E in a CE
way

NoSQL taxonomy

• Key-Value stores
– Simple K/V lookups (DHT)
• Column stores
– Each key is associated with many attributes (columns)
– NoSQL column stores are actually hybrid row/column stores
• Different from “pure” relational column stores!

• Document stores
– Store semi-structured documents (JSON)
– Map/Reduce based materialisation, sorting, aggregation, etc.
• Graph databases
– Not exactly NoSQL…
• can’t satisfy the requirements for High Availability and
Scalability/Elasticity very well


Contents

Part II

Key-Value stores
PNUTS, Dynamo, Voldemort


PNUTS

• Yahoo, ~2008, part of the Y! Sherpa platform
• Requirements
– Scale out / elasticity
– Geo-replication
– High availability / Fault tolerance
– Relaxed consistency
• Simplified relational data model
– Flexible schema
– No Referential Integrity
– No joins, aggregation, etc
– Updates/deletes only by Primary Key
– “Multiget” (retrieve many records by PKs)


PNUTS – consistency model

• Timeline consistency
– In-between serializability and eventual consistency
• All replicas apply updates for a record in the same order
• but not necessarily at the same time (eventually)
– One replica is always chosen as a Master (per record!)
• adaptively changed for load re-balancing (usage pattern tracking!)
• updates go only to the Master, then async propagation to other replicas
/ reads go to replicas or Master
– Read consistency levels
• Read any – may return a stale version of a record (any replica, fast)
• Read critical (V) – return a newer (or the same) record version than V
• Read latest – most recent version (goes to Master, slow)
– Write consistency levels
• Write
• Test-and-set (V) – write only if the record version is the same as V
(ensures serialization)

PNUTS – consistency model (2)

(C) Yahoo!


PNUTS - architecture

• Regions
– (usually) geographically distributed replicas (20+ datacenters)
– Pub/Sub asynchronous replication (Tribble)
• Tablets
– Horizontal partitioning of the tables (keyspace)
– One copy of a Tablet within each Region
• Components
– Routers
• Cached copy of the interval mapping for tablets
– Storage Units (SU)
• Filesystem or MySQL
– Tablet Controllers (TC)
• Maintains interval mapping, tablet split, load re-balancing, etc


PNUTS – architecture (2)

(C) Yahoo!


PNUTS – architecture (3)

• Message Broker
– Pub/Sub replication
– Notification service (even for external apps)
– Reliable logging
– 1 broker per tablet, but subscriptions on the table level possible too
• “Mastership”
– Record master – updates, “read latest”
– Tablet master – inserts & deletes
• Query processing
– Replica – “read any”, “read critical”
– Master – updates, “read latest”
– Scatter/Gather engine – range queries, table scans, multi-record
updates


Dynamo

• Amazon, ~2007
• P2P key-value store
– Object versioning
– Consistent hashing
– Gossip – membership & failure detection
– Quorum reads
• Requirements & assumptions
– Simple query model (unique keys, blobs, no schema, no multi-access)
– Scale out (elasticity)
– Eventual consistency (improved availability)
– Decentralized & symmetric (P2P), heterogeneous (load distribution)
– Low latency / high throughput
– Internal service (no security model)

Dynamo - partitioning

• Consistent hashing
– “Ring” of nodes (random position assigned to nodes when joining)
– Node leaving/joining the ring affects only its 2 neighbours
– Virtual nodes (improves the load balancing) – node is assigned
several positions in the ring
• Replication
– N replicas (successor nodes on ring)
• Versioning
– All updates provide version context
– Eventual consistency
– Conflict reconciliation by clients (vector clocks)
– Vector clock size thresholds (truncation)

(C) Amazon

Dynamo – operations

• Read/Write requests
– Client sends a request to a random node, the node forwards it to the
proper node (1st replica responsible for that partition – coordinator)
– Coordinator will forward R/W requests to top N replicas
• Consistency
– Quorum protocol
• R, W – minimum number of nodes that must participate in a Read/ Write
operation (R+W > N)
– Writes
• Coordinator generates the version and the vector clock
• Coordinator sends requests to N replicas, if W replicas confirm then OK
– Reads
• Coordinator sends requests to N replicas, if R replicas respond then OK
• If different versions are returned then reconcile and write back the
reconciled version (read repair)

Dynamo – failure handling

• “Hinted handoff”
– Dynamo is “always writable”
– If a replica node NR is down, the coordinator sends the write data to a
new node NHH (with a hint that NR was the intended recipient)
– Hinted writes are not readable, i.e. read quorum may still fail!
– NHH will keep the data until NR is available again, then send the data
version to NR and delete the local copy
• “Anti-entropy” replica synchronisation
– What if a hinted replica NHH fails before it can sync with NR?
– Merkle trees
• Pros – minimum data transfer (only the root node hash compared when
in-sync), very fast location of out-of-sync keys (tree traversal)
• Cons – trees need to be re-calculated when nodes join/leave the ring
(keyspace partitioning changes)


Dynamo – architecture

• Bootstrapping & node removal
– New node gets a position in the ring
– Several re-balancing strategies
• Partition split - the node responsible for that keyspace partition splits it
and sends half the data to the new node
• Fixed size partitions – one or more partitions are sent to the new node
– Similar process when a node leaves the ring
• Node components
– Request handling & coordination
– Membership & failure detection
– Local persistence engine
• MySQL
• BerkleyDB
• In-memory buffer + persistence


Voldemort

• http://project-voldemort.com/
• Distributed key-value store
– Based on Dynamo
• Originally developed by LinkedIn, now open source
• Features
– Simple data model (no joins or complex queries, no RI, …)
– P2P
– Scale-out / elastic
• Consistent hashing of keyspace
• Fixed partitions (no splits, but owner may change when re-balancing)
– Eventual consistency / High Availability
– Replication
– Failure handling

Voldemort & Dynamo

• Similar to Dynamo
– Versions & Vector clocks -> eventual consistency
– R/W quorum (R+W>N)
– Hinted handoff (failure handling)
– Pluggable persistence engines
• MySQL, BerkeleyDB, in-memory


Contents

Part III

Column stores
BigTable, HBase, Cassandra


BigTable

• Google, ~2006
• Sparse, distributed, persistent multidimensional sorted map
• (row, column, timestamp) dimensions, value is string
• Key features
– Hybrid row/column store
– Single master (stand-by replica)
– Versioning
– Compression

(C) Google


BigTable - data model

• Row
– Keys are arbitrary strings
– Data is sorted by row key
• Tablet
– Row range is dynamically partitioned into tablets (sequence of rows)
– Range scans are very efficient
– Row keys should be chosen to improve locality of data access
• Column, Column Family
– Column keys are arbitrary strings, unlimited number of columns
– Column keys can be grouped into families
– Data in a CF is stored and compressed together (Locality Groups)
– Access control on the CF level


BigTable - data model (2)

• Timestamps
– Each cell has multiple versions
– Can be manually assigned
• Versioning
– Automated garbage collection
• Retain last N versions
• Retain versions newer than TS

• Architecture
– Data stored on GFS
– Relies on Chubby (distributed lock service, Paxos)
– 1 Master server
– thousands of Tablet servers


BigTable - architecture

• Master server
– Assign tablets to Tablet Servers
– Balance TS load
– Garbage collection
– Schema management
– Client data does not move through the MS (directly through TS)
– Tablet location not handled by MS
• Tablet server (many)
– thousands of tablets per TS
– Manages Read / Write / Split of its tablets


BigData - workflow

• Tablet location
– B+ tree index
– root (Chubby file) - location of the Root Metadata tablet
– 1st level (Root Metadata tablet) – locations of all Metadata tablets
– 2nd level (Metadata tablet) – location of a set of tablets
• Each row is SSTable ID + end row ID
– Clients cache tablet locations

(C) Google


BigData – workflow (2)

• Tablet access
– SSTable – persistent, ordered immutable map from keys to values
• Contains a sequence of blocks + block index
• Compression at block level
• 2 phase compression, 10:1 ratio (standard gzip up to 3:1)
– Updates
• 1st added to the redo log
• Then to the in-memory buffer (memtable)
• If memtable is full, then it is flushed into a new SSTable and emptied
– Reads
• Executed over the merged memtable and all SSTables (Bloom filters)
• Merged view is very efficient (data is already sorted)
• Bloom filters – probabilistic maps on whether a specific SSTable contains
a specific cell (row, column), i.e. disk lookups are drastically reduced



(C) Google



• SSTable compactions
– Minor compaction
• Memtable flushed on disk into a new SSTable and emptied
• When: memtable is full
– Merging compaction
• Merge the memtable and several SSTables into 1+ new SSTable(s)
• Then, empty the memtable and delete the input SSTables
• New SSTable(s) can still contain deletion info + deleted data (no purging)
• When: number of SSTables limit reached
– Major compaction
• A merging compaction with exactly 1 output SSTable and all deleted data
purged
• When: regularly (background)


BigTable - performance

• Benchmark with 1, 50, 250 and 500 Tablet Servers

(C) Google


BigTable, real world data volumes (ca. 2006)

(C) Google


HBase

• http://hadoop.apache.org/hbase
• Developed by Powerset, now Apache
• Based on BigTable
– HDFS (GFS), ZooKeeper (Chubby)
– Master Node (Master Server), Region Servers (Tablet Servers)
– HStore (tablet), memcache (memtable), MapFile (SSTable)
• Features
– Data is stored sorted (no real indexes)
– Automatic partitioning
– Automatic re-balancing / re-partitioning
– Fault tolerance (HDFS, 3 replicas)


HBase - architecture

(C) Tom White


HBase - operations

• Workflow
– Hstore/tablet location (== BigTable)
• clients talk to ZooKeeper
• root metadata, metadata tables, client side caching, etc
– Writes (== BigTable)
• First added to the commit log, then to the memcache (memtable),
eventually flushed to MapFile (SSTable)
– Reads (== BigTable)
• First read from memcache, if info is incomplete then read the MapFiles
– Compactions (== BigTable)
• APIs
– Thrift
– REST
• GET | PUT | POST | DELETE <table>/<row>/<column>:<qualifier>/<timestamp>


HBase – MapReduce jobs

• Can be a source/sink for M/R jobs in Hadoop

(C) ???


HBase vs. BigTable

• http://www.larsgeorge.com/2009/11/hbase-vs-bigtable-
comparison.html

Feature BigTable HBase
Access control + -
Master server single single/multiple*
Locality groups + -
Cell cache + -
Commit logs (WAL) primary + secondary single
Skip WAL for bulk loads ? yes
Data isolation + -
replication + In progress
Everything else pretty much the same!


Cassandra

• http://cassandra.apache.org/
• Developed by Facebook (inbox), now Apache
– Facebook now developing its own version again
• Based on Google BigTable (data model) and Amazon Dynamo
(partitioning & consistency)
• P2P
– Every node is aware of all other nodes in the cluster
• Design goals
– Eventual consistency (improves HA)
– Incremental scalability / elasticity
– Optimistic replication


Cassandra – data model, partitioning

• Data model
– Same as BigTable
– Super Columns (nested Columns) and Super Column Families
– column order in a CF can be specified (name, time)
• Cluster membership
– Gossip – every nodes gossips to 1-3 other nodes about the state of
the cluster (merge incoming info with its own)
– Changes in the cluster (node in/out, failure) propagate quickly (LogN)
– Probabilistic failure detection (sliding window, Exp(α) or Nor(μ,σ2))
• Dynamic partitioning
– Ring of nodes
– Nodes can be “moved” on the ring for load balancing


Cassandra - operations

• Writes (== BigTable… almost)
– Client sends request to a random node, the node determines the
actual node responsible for storing the data (cluster awareness)
– Data replicated to N nodes (config)
– Data first added to the commit log, then to the memtable, eventually
flushed to SSTable
• Reads (== BigTable… almost)
– Send request to a random node, which forwards it to all the N nodes
having the data
• Single read – return first response / Quorum read – value that the
majority of nodes agrees on
– First read from memtable, if info is incomplete then read the
SSTables
– Bloom filters for efficient SSTable lookups
• Compactions (== BigTable) Databases
NoSQL Mar 2010 #43

Cassandra @ Facebook

• Inbox search
• ca. 2009 - 50 TB data, 150 nodes, 2 datacenters
• Performance (production)

(C) facebook


Contents

Part IV

Document stores
CouchDB


CouchDB

• http://couchdb.apache.org/ , ~2005
• Schema-free, document oriented database
– Documents stored in JSON format (XML in old versions)
– B-tree storage engine
– MVCC model, no locking
– no joins, no PK/FK (UUIDs are auto assigned)
– Implemented in Erlang
• 1st version in C++, 2nd in Erlang and 500 times more scalable (source:
“Erlang Programming” by Cesarini & Thompson)
– Replication (incremental)
{
• Documents “name”: “Ontotext”,
“url”: “www.ontotext.com”
– UUID, version “employees”: 40
– Old versions retained “products”: [“OWLIM”, “KIM”, “LifeSKIM”, “JOCI”]
}

CouchDB (2)

• REST API
CRUD HTTP params
Create PUT /db/docid
Read GET /db/docid
Update POST /db/docid
Delete DELETE /db/docid

• Views
– Filter, sort, “join”, aggregate, report
– Map/Reduce based, language independent (JavaScript by default)
– K/V pairs from Map/Reduce are also stored in the B-tree engine
– Built on demand
– Can be materialized & incrementally updated


CouchDB – views

(C) Chris Anderson / Apache


Contents

Part V

Benchmarks


NoSQL feature matrix (incomplete)

(C) R. Ramakrishnan / Yahoo


CAP positioning

Consistency

•BigTable
•HBase

•Dynamo
Partition
tolerance •PNUTS Availability
•Cassandra
•Voldemort
•CouchDB?


Yahoo Cloud Serving Benchmark (YCSB)

• Benchmark various NoSQL systems
– Performance
• Measure latency/throughput curve on a fixed hardware
– Scale out (elasticity)
• add hardware, increase data size / workload proportionally
• Measure latency (best case: constant)
• Or… add hardware but same data/workload (latency should drop)
– Later – replication, availability, …
• Testbed
– 6 boxes {2x4 core 2.5GHz, 8GB RAM, 6 HDD (SAS RAID1+0), GB eth}
– 120M records (1K) ~ 20GB data per server
– 100+ client threads


YCSB – performance

• Update heavy workload (50/50 reads/updates)
• Reminders
– Cassandra (Dynamo) is “always writable”
– HBase buffers updates in memory
– PNUTS uses MySQL as a persistence engine

NoSQL Databases (C) B. Cooper et al. / Yahoo #53
Mar 2010

YCSB – performance (2)

• Read heavy workload
• Reminders
– Cassandra (Dynamo) will read several replicas for read consistency
– HBase will access several files during a read (even with Bloom filters)
– PNUTS uses MySQL as a persistence engine

NoSQL Databases (C) B. Cooper et al. / Yahoo #54
Mar 2010

YCSB – scalability

• Can more servers handle proportionally bigger workload?
– Best case: latency should be constant
• Notes
– PNUTS and Cassandra scale very well
– HBase performance varies a lot

(C) B. Cooper et al. / Yahoo


YCSB – elastic speedup

• What is the impact of adding one server (same workload)?
– Best case: latency should drop
• Notes
– 3 servers, add 1 more at 5th min (different latency scales!)
– Cassandra – performance improvement by 10% (quickly!)
– Hbase – large latency spike initially, then 10% performance
improvement (within 20 min)
(C) B. Cooper et al. / Yahoo


Contents

Part VI

RDF & NoSQL?


RDF & NoSQL

• No easy match
– Partitioning by key not so suitable for RDF (S, P, O)
– Data locality insufficient for RDF materialisation
– Full featured query language (SPARQL) cannot be efficiently mapped
to NoSQL key/value lookups and range scans
– Eventual consistency is a problem (materialisation with stale data)
• So far RDF limited to the graph databases subset of NoSQL
– But graph databases don’t provide massive scalability


Useful links

• http://groups.google.com/group/nosql-discussion
• “Bigtable: A Distributed Storage System for Structured Data”
• “Cassandra - A Decentralized Structured Storage System”
• "Dynamo: Amazon’s Highly Available Key-value Store"
• “PNUTS: Yahoo!'s Hosted Data Serving Platform”
• “Benchmarking Cloud Serving Systems with YCSB”
• “HBase vs. BigTable Comparison”


Q&A

Questions?


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