For a long time, relational database management systems have been the only solution for persistent data store. However, with the phenomenal growth of data, this conventional way of storing has become problematic. To manage the exponentially growing data traffic, largest information technology companies such as Google, Amazon and Yahoo have developed alternative solutions that store data in what have come to be known as NoSQL databases. Some of the NoSQL features are flexible schema, horizontal scaling and no ACID support. NoSQL databases store and replicate data in distributed systems, often across datacenters, to achieve scalability and reliability. The CAP theorem states that any networked shared-data system (e.g. NoSQL) can have at most two of three desirable properties: • consistency(C) - equivalent to having a single up-to-date copy of the data • availability(A) of that data (for reads and writes) • tolerance to network partitions(P) Because of this inherent tradeoff, it is necessary to sacrifice one of these properties. The general belief is that designers cannot sacrifice P and therefore have a difficult choice between C and A. In this seminar two NoSQL databases are presented: Amazon's Dynamo, which sacrifices consistency thereby achieving very high availability and Google's BigTable, which guarantees strong consistency while provides only best-effort availability.

1. Dynamo and BigTable
In light of the CAP Theorem
22953 Research Seminar: Databases and Data Mining
November 2013
Open University
Grisha Weintraub

2. Overview
• Introduction to DDBS and NoSQL
• CAP Theorem
• Dynamo (AP)
• BigTable (CP)
• Dynamo vs. BigTable

3. Distributed Database Systems
• Data is stored across several sites that share no
physical component.
• Systems that run on each site are independent of each
other.
• Appears to user as a single system.

4. Distributed Data Storage
Partitioning :
Data is partitioned into
several fragments and
stored in different sites.
Horizontal – by rows.
Vertical – by columns.
Replication :
System maintains multiple
copies of data, stored in
different sites.
Replication and partitioning can be combined !

5. Partitioning
A
B
key
key
value
x
10
w
12
value
x
5
y
7
z
10
A
B
12
5
7
10
12
value
w
x
w
key
value
y
C
key
z
7
z
key
5
y
value
Horizontal
Vertical
Locality of reference – data is most likely to be updated and queried locally.

6. Replication
A
B
key
value
x
x
5
y
7
z
10
10
w
5
7
z
value
5
y
key
x
key
value
12
C
D
key
value
key
value
y
7
z
10
w
12
w
12
Pros – Increased availability of data and faster query evaluation.
Cons – Increased cost of updates and complexity of concurrency control.

7. Updating distributed data
• Quorum voting (Gifford SOSP’79) :





N – number of replicas.
At least R copies should be read.
At least W copies should be written.
R+W > N
Example (N=10, R=4, W=7)
• Read-any write-all :
 R=1, W=N

8. NoSQL
• No SQL :
– Not RDBMS.
– Not using SQL language.
– Not only SQL ?
• Flexible schema
• Horizontal scalability
• Relaxed consistency  high performance & availability

9. Overview
• Introduction to DDBS and NoSQL √
• CAP Theorem
• Dynamo (AP)
• BigTable (CP)
• Dynamo vs. BigTable

10. CAP Theorem
• Eric A. Brewer. Towards robust distributed systems (Invited Talk)
, July 2000
• S. Gilbert and N. Lynch, Brewer’s Conjecture and the Feasibility of
Consistent, Available, Partition-Tolerant Web Services, June 2002
• Eric A. Brewer. CAP twelve years later: How the 'rules' have
changed, February 2012
• S. Gilbert and N. Lynch, Perspectives on the CAP Theorem, February
2012

11. CAP Theorem
• Consistency – equivalent to having a single up-to-date
copy of the data.
• Availability - every request received by a non-failing node
in the system must result in a response.
• Partition tolerance - the network will be allowed to lose
arbitrarily many messages sent from one node to another.
Theorem – You can have at most two of these properties
for any shared-data system.

12. CAP Theorem
Consistency
x=5
x?
5
Availability
Partition tolerance

13. CAP Theorem - Proof
x=0
x=0
x=0
x=5
x?
1. x=0  Not consistent
2. No response  Not available

14. CAP – 2 of 3
Consistency
Availability
Partition
Tolerance
• Trivial:
– The trivial system that ignores all requests meets these requirements.
• Best-effort availability :
– Read-any write-all systems will become unavailable only when messages are
lost.
• Examples :
– Distributed database systems, BigTable

15. CAP – 2 of 3
Consistency
Availability
Partition
Tolerance
• Trivial:
– The service can trivially return the initial value in response to every request.
• Best-effort consistency :
– Quorum-based system, modified to time-out lost messages, will only return
inconsistent(and, in particular, stale) data when messages are lost.
• Examples :
– Web cashes, Dynamo

16. CAP – 2 of 3
Consistency
Availability
Partition
Tolerance
• If there are no partitions, it is clearly possible to provide
consistent, available data (e.g. read-any write-all).
• Does choosing CA make sense ?
Eric Brewer :
– “The general belief is that for wide-area systems, designers cannot forfeit P
and therefore have a difficult choice between C and A.“
– “If the choice is CA, and then there is a partition, the choice must revert to C or
A. ”

17. Overview
• Introduction to DDBS and NoSQL √
• CAP Theorem √
• Dynamo (AP)
• BigTable (CP)
• Dynamo vs. BigTable

18. Dynamo - Introduction
•
•
•
•
Highly available key-value storage system.
Provides an “always-on” experience.
Prefers availability over consistency.
“Customers should be able to view and add
items to their shopping cart even if disks are
failing, network routes are flapping, or data
centers are being destroyed by tornados.”

19. Dynamo - API
• Distributed hash table :
– put(key, context, object) – Associates given object with
specified key and context.
• context – metadata about the object, includes information as the
version of the object.
– get(key) – Returns the object to which the specified key is
mapped or a list of objects with conflicting versions along
with a context.

20. Dynamo - Partitioning
• Naive approach :
– Hash the key.
– Apply modulo n (n=number of nodes).
key = “John Smith”
hash(key) = 19
Adding/deleting nodes  totally mess !
19 mod 4 = 3
1
2
3
4

21. Dynamo - Partitioning
• Consistent hashing (STOC’97) :
– Each node is assigned to a random position on the ring.
– Key is hashed to the fixed point on the ring.
– Node is chosen by walking clockwise from the hash location.
hash(key)
A
B
G
C
F
E
D
Adding/deleting nodes  uneven partitioning !

22. Dynamo - Partitioning
• Virtual nodes :
– Each physical node is assigned to multiple points in the ring.

23. Dynamo - Replication
•
Each data item is replicated at N nodes.
•
Preference list - the list of nodes that store a particular key.
•
Coordinator - node which handles read/write operations, typically the first in the preference list.
hash(key)
A
B
G
N=3
F
E
C
D

24. Dynamo - Data Versioning
• Eventual consistency(replicas are updated
asynchronously) :
– A put() call may return to its caller before the update has
been applied at all the replicas.
– A get() call may return many versions of the same object.
• Reconciliation :
– Syntactic – system resolves conflicts automatically(e.g.
new version overwrite the previous)
– Semantic – client resolves conflicts(e.g. by merging
shopping cart items).

25. Dynamo - Data Versioning
• Semantic Reconciliation :
Item 1
Item 6
get()
{ item1, item4}, {item1, item6}
put({ item1, item4,item6})
What about delete ?!
Item 1
Item 4

26. Dynamo - Data Versioning
•
Vector clocks(Lamport CACM’78)
– List of <node, counter> pairs.
– One vector clock is associated with every
version of every object.
– If the counters on the first object’s clock are
less-than-or-equal to all of the nodes in the
second clock, then the first is an ancestor of the
second and can be forgotten.
– Part of the “context” parameter of put() and
get()

27. Dynamo – get() and put()
• Two strategies that a client can use :
– Load balancer that will select a node based on load
information :
 client does not have to link any code specific to Dynamo in its
application.
– Partition-aware client library.
 can achieve lower latency because it skips a potential forwarding
step.

28. Dynamo – get() and put()
• Quorum-like system :
 R - number of nodes that must read a key.
 W - number of nodes that must write a key.
• put() :
 Coordinator generates the vector clock for the new version and
writes the new version locally.
 Coordinator sends new version to N nodes.
 If at least W-1 nodes respond, then write is successful.
• get() :
 Coordinator requests all data versions from N nodes.
 If at least R-1 nodes respond, returns data to client.

29. Dynamo – Handling Failures
• Sloppy Quorum and Hinted Handoff :
– All read and write operations are performed on the first N healthy nodes.
– If node is down, its replica is sent to another node. The data received in this
node is called “hinted replica”.
– When original node is recovered, the hinted replica is written back to the
original node.
A
B
G
C
F
E
D

30. Dynamo – Handling Failures
• Anti-entropy :
– Protocol to keep the replicas synchronized.
– To detect inconsistencies – Merkle Tree :
• Hash tree where leaves are hashes of the values of individual keys.
• Parent nodes higher in the tree are hashes of their respective children.

31. Dynamo – Membership detection
• Gossip-based protocol :
– Periodically, each node contacts another node in the network,
randomly selected.
– Nodes compare their membership histories and reconcile them.
A
B
G
C
F
E
D
A F
BG
CE
DG
EA
FB
GC

32. Dynamo - Summary

33. Overview
• Introduction to DDBS and NoSQL √
• CAP Theorem √
• Dynamo (AP) √
• BigTable (CP)
• Dynamo vs. BigTable

34. BigTable - Introduction
• Distributed storage system for managing
structured data that is designed to scale to a
very large size.

35. BigTable – Data Model
• Bigtable is a sparse, distributed, persistent
multidimensional sorted map.
• The map is indexed by a row key, column key,
and a timestamp.
• (row_key,column_key,time)  string

36. BigTable – Data Model
user_id
phone
15
row_key
name
John
178145
(29, name, t2)  “Robert”
user_id
column_key
name
29
Bob
Robert
email
t1
bob@gmail.com
t2
timestamp
user_id
RDBMS
Approach
name
phone
email
15
John
178145
null
29
Bob
null
bob@gmail.com

37. BigTable – Data Model
• Columns are grouped into Column Families:
– family : optional qualifier
Column
Family
Optional
Qualifier
user_id
name:
contactInfo : phone
contactInfo : email
15
John
17814552
john@yahoo.com
RDBMS user_id
Approach
15
name
user_id
type
value
John
15
phone
178145
15
email
john@yahoo.com

38. BigTable – Data Model
• Rows :
– The row keys in a table are arbitrary strings.
– Every read or write of data under a single row key is
atomic.
– Data is maintained in lexicographic order by row key.
– Each row range is called a tablet, which is the unit of
distribution and load balancing.
row_key
user_id
name:
contactInfo : phone
contactInfo : email
15
John
178145
john@yahoo.com

39. BigTable – Data Model
• Column Families :
– Column keys are grouped into sets called column families.
– A column family must be created before data can be stored
under any column key in that family.
– Access control and both disk and memory accounting are
performed at the column-family level.
column_key
user_id
name:
contactInfo : phone
contactInfo : email
15
John
178145
john@yahoo.com

40. BigTable – Data Model
• Timestamps :
– Each cell in a Bigtable can contain multiple versions of the
same data; these versions are indexed by timestamp.
– Versions are stored in decreasing timestamp order.
– Timestamps may be assigned :
• by BigTable(real time in ms)
• by client application
– Older versions are garbage-collected.
user_id
29
name
Bob
Robert
email
t1
t2
timestamp
bob@gmail.com

41. BigTable – Data Model
• Tablets :
– Large tables broken into tablets at row boundaries.
– Tablet holds contiguous range of rows.
– Approximately 100-200 MB of data per tablet.
id
15000
….
…..
…..
20001
Tablet 2
…..
20000
Tablet 1
…..
…..
….
…..
25000
…..

42. BigTable – API
• Metadata operations :
– Creating and deleting tables, column families, modify access
control rights.
• Client operations :
– Write/delete values
– Read values
– Scan row ranges
// Open the table
Table *T = OpenOrDie("/bigtable/users");
// Update name and delete a phone
RowMutation r1(T, “29");
r1.Set(“name:", “Robert");
r1.Delete(“contactInfo:phone");
Operation op;
Apply(&op, &r1);

43. BigTable – Building Blocks
• GFS – large-scale distributed file system.
GHEMAWAT, GOBIOFF, LEUNG, The Google file system. (Dec. 2003)
• Chubby – distributed lock service.
BURROWS, The Chubby lock service for loosely coupled distributed systems (Nov. 2006)
• SSTable – file format to store BigTable data.

44. BigTable – Building Blocks
• GFS :
– Files broken into chunks (typically 64 MB)
– Master manages metadata.
– Data transfers happens directly between
clients/chunkservers.
Master
Client

45. BigTable – Building Blocks
• Chubby :
– Provides a namespace of directories and small files :
• Each directory or file can be used as a lock.
• Reads and writes to a file are atomic.
– Chubby clients maintain sessions with Chubby :
• When a client's session expires, it loses any locks.
– Highly available :
• 5 active replicas, one of which is elected to be the master.
• Live when a majority of the replicas are running and can communicate with
each other.

46. BigTable – Building Blocks
• SSTable :
– Stored in GFS.
– Persistent, ordered, immutable map from keys to values.
– Provided operations :
• Get value by key
• Iterate over all key/value pairs in a specified key range.

47. BigTable – System Structure
• Three major components:
– Client library
– Master (exactly one) :
•
•
•
•
•
Assigning tablets to tablet servers.
Detecting the addition and expiration of tablet servers.
Balancing tablet-server load.
Garbage collection of files in GFS.
Schema changes such as table and column family creations.
– Tablet Servers(multiple, dynamically added) :
• Manages 10-100 tablets
• Handles read and write requests to the tablets.
• Splits tablets that have grown too large.

48. BigTable – System Structure

49. BigTable – Tablet Location
• Three-level hierarchy analogous to that of a B+ tree to store
tablet location information.
• Client library caches tablet locations.

50. BigTable – Tablet Assignment
•
Tablet Server:
– When a tablet server starts, it creates, and acquires an exclusive lock on a uniquelynamed file in a specific Chubby directory - servers directory.
•
Master :
– Grabs a unique master lock in Chubby, which prevents concurrent master
instantiations.
– Scans the servers directory in Chubby to find the live servers.
– Communicates with every live tablet server to discover what tablets are already
assigned to each server.
– If a tablet server reports that it has lost its lock or if the master was unable to reach
a server during its last several attempts – deletes the lock file and reassigns
tablets.
– Scans the METADATA table to find unassigned tablets and reassigns them.

51. BigTable - Tablet Assignment
Master
Metadata
Table
Tablet Server 1
Tablet Server 2
Tablet 1 Tablet 8
Tablet 7 Tablet 2
Chubby

52. BigTable – Tablet Serving
•
Writes :
– Updates committed to a commit log.
– Recently committed updates are stored in memory – memtable.
– Older updates are stored in a sequence of SSTables.
•
Reads :
– Read operation is executed on a merged view of the sequence of SSTables and the memtable.
– Since the SSTables and the memtable are sorted, the merged view can be formed efficiently.

53. BigTable - Compactions
• Minor compaction:
– Converts the memtable into SSTable.
– Reduces memory usage.
– Reduces log reads during recovery.
• Merging compaction:
– Merges the memtable and a few SSTable.
– Reduces the number of SSTables.
• Major compaction:
– Merging compaction that results in a single SSTable.
– No deletion records, only live data.
– Good place to apply policy “keep only N versions”

54. BigTable – Bloom Filters
Bloom Filter :
1. Empty array a of m bits, all set to 0.
2. Hash function h, such that h hashes each element to one of
the m array positions with a uniform random distribution.
3. To add element e – a[h(e)] = 1
Example :
S1 = {“John Smith”, ”Lisa Smith”, ”Sam Doe”, ”Sandra Dee”}
0 1 1 0 1 0 0 0 0 0 0 0 0 0 1 0

55. BigTable – Bloom Filters
• Drastically reduces the number of disk seeks
required for read operations !

56. Overview
• Introduction to DDBS and NoSQL √
• CAP Theorem √
• Dynamo (AP) √
• BigTable (CP) √
• Dynamo vs. BigTable

57. Dynamo vs. BigTable
Dynamo
BigTable
data model
key-value
multidimensional map
operations
by key
by key range
partition
random
ordered
replication
sloppy quorum
only in GFS
architecture
decentralized
hierarchical
consistency
eventual
strong (*)
access control
no
column family

58. Overview
• Introduction to DDBS and NoSQL √
• CAP Theorem √
• Dynamo (AP) √
• BigTable (CP) √
• Dynamo vs. BigTable √

59. References
•
R.Ramakrishnan and J.Gehrke, Database Management Systems, 3rd edition, pp. 736-751
•
S. Gilbert and N. Lynch, "Brewer's Conjecture and the Feasibility of Consistent, Available,
Partition-Tolerant Web Services," ACM SIGACT News, June 2002, pp. 51-59.
•
Brewer, E. CAP twelve years later: How the 'rules' have changed. IEEE Computer 45, 2 (Feb. 2012),
23–29.
•
S. Gilbert and N. Lynch, Perspectives on the CAP Theorem. IEEE Computer 45, 2 (Feb. 2012), 30-36.
•
G. DeCandia et al., "Dynamo: Amazon's Highly Available Key-Value Store," Proc. 21st ACM SIGOPS
Symp. Operating Systems Principles (SOSP 07), ACM, 2007, pp. 205-220.
•
F. Chang et al., "Bigtable: A Distributed Storage System for Structured Data" Proc. 7th Usenix
Symp. Operating Systems Design and Implementation (OSDI 06), Usenix, 2006, pp. 205-218.
•
Ghemawat, S., Gobioff, H., and Leung, S. The Google File system. In Proc. of the 19th ACM SOSP
(Dec.2003), pp. 29.43.