prof. dr. stefan edlich nosql in der cloud prof. dr. stefan edlich nosql in der cloud
TRANSCRIPT
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Prof. Dr. Stefan Edlich
NoSQL in der Cloud
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nosqlberlin.de nosqlfrankfurt.de nosql powerdays
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http://nosql-database.org
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NoSQL is specialization!
• Big Data
• Massive Write Performance
• Fast KV Access
• Write Availability
• Flexible Schema (Migration) + Flexible Datatypes
• Easier maintainability, administration and operations
• No single point of failure
• Programmer ease of use
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Theorie?!Map/Reduce Map/Reduce Nachfolger!
ACID / BASE & CAP P liegt in der Regel nie vor!
Consistent Hashing Basis skalierbarer K/V Stores
MVCC non blocking Vorteile
Vector Clocks [122:1][147:2|122:1]
[97:3|147:2|122:1]
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Persistenz
Disk Full Memory Hybrid Pluggable
DatenmodellColumn Family DocumentDB Key / Value Graph
SchnittstellenREST Language
APIThrift/Avro
Map/Reduce Get/Put
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Google Protocol Buffers
=>
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• JSON
• Binary data transfer
• automatic RPC generation
• no code generation
• Client + Server tauschen Schema bei Änderung
unbedingt evaluieren!
Apache Avro!
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Datenmodelle
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Voldemort, Chordless, Scalaris, Dynamo / Dynomite
db4o, Versant, Objectivity, Gemstone, Progress, Mark Logic, EMC Momentum, Tamino, GigaSpaces, Hazelcast, Terracotta, …
Column Family
DocumentDBs
Key/ValueDBs
GraphDBs
andere
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HBase Cassandra
SimpleDB
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+ Skalierung = new node+ Community+ API- Replikation- Aufsetzen, Optimierung, Wartung
+ Skalierung = new node+ Replikation+ Konfiguration (r, w)- Dokumentation- Abfragen
+ stressfreie SaaS Lösung+ transparent scaling- UTF-8 String- Daten liegen bei Amazon+- kein tuning / config
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Document Databases
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any JS-Client
no Middleware!
DB+WebServer+evolving App
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2.Runde+= 6,5 Mio $
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- nicht normalisiert (Duplicates, Delete Orphans, ...)
- (konfigurierbare Zeit Crash anfällig) (Journaling)
- Eventually Consistent
- echte Skalierung nur über Sharding
- (noch nicht kill -9 fest)
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EC2 Node 66 GB EC2 Node 66 GB
67 GBIndex Data
11 hours + 1 day off
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+ nicht normalisiert+ Schema Agilität+ Doku exzellent+ Speed (MemMapped Files)+ Installation+save =28 sek!+ beliebige Indizes+ MapReduce+ Rich Query Language+ GridFS (statt HDFS)+ einfache Replizierung (Master-Slave / Replica Sets)
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db.system.indexes.find();db.friends.getIndexes();db.friends.ensureIndex({friend: 1});db.friends.ensureIndex({friend: 1, zip: 1}); //compounddb.friends.find({friend: „Mario“, zip: „13755“}).explain();
Queries: age: {$gt: 10} food:{$all: [„pizza“, „noodles“]}$gt, $lt, $lte, $ne, $in, $nin, $mod, $all, $size, $exists, $type, , $or, $elem, $elemMatch, regexp, ...
NoSQL Query LockIn?!
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Migrations Architektur-Pattern:
A) Blacklist
Sich veränderndes Schema
rename
try { ...} catch (FirstException | SecondException ex) { // newName = BlackList.checkName(OldName)}
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B) „Rails“ Migration
new name
new name
new name
new name
old namenew name
(nicht wenn zu oft repliziert)
old namenew name
old namenew name
old namenew name
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Duplikate = SpaceAktualität der Daten
„Pre-Joined“ Daten!„pre-computeD“
wachsende Daten raus oder Pre-SPACED
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In die Cloud…
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Clients
Config Serversmongos
ROUTER
Replica Set
Shard A Shard B Shard C
RAM+DISK+
POSSIBLE ARBITER
micro64 bit [extra | double | quadrupel] Large
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Erfahrungen…
• RAID Konfigurationen (00,01,10,03,05, …)
• Journaling-Dateisysteme (ext4, xfs, …)• (Security) Ports, F-Deskriptoren,
Snapshots,…
www.mongodb.org/display/DOCS/Amazon+EC2
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K/V-Stores+ sehr schnell > 100.000 /sek
+ konfigurierbarer Disc sync
+ API für eigene Anbindung
+ einfache Replikation
+ hash, list, set, sorted set,
messages
+ Installation
UNIX: 38 sek
Windows: 18 sek
- cloud-cluster erst in Version
3.*
Datenstrukturen abbilden ->
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Sorted Set
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memcached
API
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• simply dynamic scaling (up & down)• scales linear• bullet proof by Zynga.com• limited membase protocol• Membase Tap (Protocol Interception)• Code-Node:
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Membase in der Cloud
• Fertige RightScale & AMI templates
• Diverse Ports öffnen
• DNS Eintrag und keine verändernden IPs
• Master Node angeben• legt Quota für die Erben fest
• Backups für EBS
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GraphDBs
Property Graph
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player
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Graph DBs in der Cloud• > N Milliarden Knoten? Sharding!
• aber meistens kein „predictable lookup“
• möglich nur bei Domain Specific Knowledge
• ausbalancierte DBs ohne sweet spots kaum möglich
• Access Patterns + Heuristiken (Insert Sharding / Runtime Sharding) => partitionierungs Algorithmen
• (HA) Neo4j Cache Sharding! • Multi-Master Cluster for Consistent
Routing
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> 220 DBs
durchausfrustrierendes Consulting…
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Data
Transactions
Performance
Queries
Architecture
other Non-Functional Requirements
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Analyse your DataDomain-Data, Log-Data, Event-Data, Message-Data, critical Data, Business-Data, Meta-Data, temp Data, Session-Data, Geo Data, etc.
Data- / Storage-Model:relational, column-o, doc-alike, graphs, objects, etc.What Types / Type-System?Data-Navigation, Data Amount, Data Komplexity (Deep XML?)
ACID vs. BASE vs. Mixture?CAP decisions
Performance Dimension AnalysisLatency, Request behaviour, Throughput
Scale-Up vs Scale-Out
Distribution Architecturelocal, parallel, distributed / grid, service, cloud, mobile, p2p, …
Data Access Patternsread / write distribution, random / sequential, Access Design Patterns
Query RequirementsTypical queries, Tools, Ad-Hoc Queries, SQL / LINQ needed, Map/Reduce? …
Non Functional Requirements:Replication, Refactoring Frequency, DB-Support, Qualification / simplicity, Company restrictions, DB diversity (allowed?), Security, Safety / Backup & Restore, Crash Resistance, Licence…
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NoSQL
FAZIT
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Unbedingt RAM & SDD annehmen!
Gustavo
AlonsoLot‘s of >1 PT RAM DBs
in California!
Service, RAM, Cloud, Mobile
RethinkDB
SAP-Strategie?
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DaaS Zeitalter
Alleine für MongoDB weit über 100 „Database-as-a-Service“ Provider!Amazon: SimpleDB, Hadoop, etc.
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Viele clevere hybrid Lösungen!
CouchBase, Hadoop+MySQL
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Database-aaS => best Mix!
OLAP
Availability
Ad Hoc Query
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kritische
Daten
unkritische
Daten
(View, Domain, Stamm, Meta, Log, …)by Couch, MongoDB, Redis, Membase,
…
Management
Analytics
Zahlungsdaten, persönliche Daten, …by classic RDBMS, Vertica, VoltDB,
Database.com, GenieDB, … Hadoop* BI
OLAP BI
Dwight Merriman (10gen)
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Links
• nosql-database.org
• nosqltapes.com
• mynosql.com
.com
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Diskussion!
Thanks for listening!
http://edlich.de
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Schutzpatent
funktionale
(graph)
Dekomposition?
Oder…
Group By
Use Case:Aggregate
pi -> 1015 -> 1000 cluster
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Programmierung top! Programmierung nervt!
Nur `large data indexing`
Starke Konkurrenz: Stratosphere (TUB), ePic, SwissBox, etc.
„A giant step back!
Imcompatible, missing features,
not new, …“ Stonebraker
herrlich
paralellisierbar
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Map
Reduce
Cross
Match
CoGroup
Paralellization Contracts
u.v.m… =>
Graph Ops
compile, analyze, optimize
auf einer atmenden Cloud!
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ACID
BASE• Amazon Dynamo• MySQL Replikation
Eventually Consistent
WATER
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Consistency Models
© Wilfried Springer NoSQL Rollercoaster
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Availability
Partition ToleranceConsistency
CAP Theoreme
ACID / Isolation
Clients see equal data
System is always ‘ on‘
Clients find replicas
Pick 2!
NoSQLKlassiker
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„Don‘t throw C away so easy! It‘s complex.“
What you really have is: 1. Application errors2. Repetable DBMS errors3. Unrepeatable DBMS errors4. Operating System errors5. Hardware failure in cluster6. Network partition in local cluster7. A disaster8. WAN failure
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• 6 = Network Partition is rare
• 3,4,5,6 is mostly a Single Node
• Algorithms can help!
„give up P rather than sacrificing C. Use VoltDB or NimbusDB”
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M:[0,5)
N:[5,10)
O:[10,15)
P:[15,20)
Q:[20,25)
R:[25,30)
HASH KNOTEN REPLIKAT
2 M N,O
8 N O,P
10 O P,Q
17 P Q,R
22 Q R,M
26 R M,N
W = 2*WR = 1*R
Consistent Hashing
• ausfallsicher • leicht erweiterbar • gut verteilt / vnodes
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pessimistisches Locking?
MVCCMulti Version Concurrency
Control
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laufen
L:1
surfen
P:1L:1
surfen
L:2P:1
laufen
A:1L:1
laufen
A:1L:1P:0
surfen
L:2P:1A:0
surfen
P:2A:1L:2
=>
Anna
Paul
Laura
Vector Clocks