Database ,18 Current Issues

Distributed DBMS © M. T. Özsu & P. Valduriez Ch.18/1
Outline
• Introduction
• Background
• Distributed Database Design
• Database Integration
• Semantic Data Control
• Distributed Query Processing
• Multidatabase Query Processing
• Distributed Transaction Management
• Data Replication
• Parallel Database Systems
• Distributed Object DBMS
• Peer-to-Peer Data Management
• Web Data Management
• Current Issues
➡ Data Stream Management
➡ Cloud Data Management

Inputs & Outputs
• Inputs: One or more sources generate data continuously, in real time, and
in fixed order
➡ Sensor networks – weather monitoring, road traffic monitoring, motion
detection
➡ Web data – financial trading, news/sports tickers
➡ Scientific data – experiments in particle physics
➡ Transaction logs – telecom, point-of-sale purchases
➡ Network traffic analysis (IP packet headers) – bandwidth usage, routing
decisions, security
• Outputs: Want to collect and process the data on-line
➡ Environment monitoring
➡ Location monitoring
➡ Correlations across stock prices
➡ Denial-of-service attack detection
• Up-to-date answers generated continuously or periodically

Traditional DBMS
Persistent data
- stored until deleted
by user or application
Transient queries
- issued once,
then forgotten

Data Stream Management
System (DSMS)
Persistent queries
- generate up-to-date
answers as time goes on
Transient data
- deleted as window
slides forward

DSMSs – Novel Problems
• Push-based (data-driven), rather than pull-based (query-driven)
computation model
➡ New data arrive continuously and must be processed
➡ Query plans require buffers, queues, and scheduling mechanisms
➡ Query operators must be non-blocking
➡ Must adapt to changing system conditions throughout the lifetime of a query
➡ Load shedding may be required if the system can’t keep up with the stream
arrival rates

DSMS Implementation Choices
• Application on top of a relational DBMS
➡ Application simulates data-driven processing
➡ Inefficient due to the semantic gap between the DBMS and the DSMS-like
application
• Use advanced features of the DBMS engine
➡ Triggers, materialized views, temporal/sequence data models
➡ Still based upon query-driven model, triggers don’t scale and are not
expressive enough
• Specialized DSMS
➡ Incorporate streaming semantics and data-driven processing model inside the
engine

Abstract System Architecture

Stream Data Models
• Append-only sequence of timestamped items that arrive in some order.
• More relaxed definitions are possible
➡ Revision tuples
➡ Sequence of events (as in publish/subscribe systems)
➡ Sequence of sets (or bags) of elements with each set storing elements that have
arrived during the sameunit of time.
➡ …
• Possible models
➡ Unordered cash register
➡ Ordered cash register
➡ Unordered aggregate
➡ Ordered aggregate

Processing Model
• Stream-in-stream-out
• Problem
➡ Streams have unbounded length (system point of view)
➡ New data are more accurate/interesting (user point of view)
• Solution
➡ Windows
Data Stream
System

Windows
• Based on direction of movement of endpoints
➡ Two endpoints can be fixed, moving forward, or moving backward
➡ Nine possibilities, interesting ones
✦ Fixed window
✦ Sliding window
✦ Landmark window
• Based on direction of window size
➡ Logical (or time-based) window
➡ Physical (or count-based) window
➡ Predicate window
• Based on windows within windows
➡ Elastic window
➡ N-of-N window
• Based on window update interval
➡ Jumping window
➡ Tumbling window

Stream Query Languages
• Queries are persistent
• They may be monotonic or non-monotonic
➡ Monotonic: result always grows
✦ If Q(t) is the result of a query at time t, given two executions at time ti and tj,
Q(ti) Q(tj) for all ti> tj
➡ Non-monotonic: deletions from the result are possible
• Monotonic query semantics:
➡
• Non-monotonic query semantics:
➡

Declarative Languages
• Syntax similar to SQL + window specifications
• Examples: CQL, GSQL, StreaQuel
• CQL
➡ Three types of operators:
✦ Relation-to-realtion
✦ Stream-to-relation
✦ Relation-to-stream
➡ Join of one-minute windows on the a-attribute:
SELECT *
FROM S1 [RANGE 1 min], S2 [RANGE 1 min]
WHERE S1.a=S2.a
➡ ROWS for count-based windows, RANGE for time-based windows

Declarative Languages (cont’d)
• GSQL
➡ Input and output are streams (composability)
➡ Each stream should have an ordering attribute (e.g., timestamp)
➡ Subset of operators of SQL (selection, aggregation with group-by, join)
➡ Stream merge operator
➡ Only landmark windows, sliding windows may be simulated
• StreaQuel
➡ SQL syntax
➡ Query includes a for-loop construct with a variable t that iterates over time
➡ Sliding window over stream S with size 5 that should run for 50 time units:
for(t=ST; t<ST+50; t++)
WindowIs(S, t-4, t)

Object-based Languages
• Use abstract data typing and/or type hierarchies
• Examples: Tribeca, Cougar
• Tribeca
➡ Models stream contents according to a type hierarchy
➡ SQL-like syntax, accepts a stream as input and generates one or more output
streams
➡ Operations: projection, selection, aggregation (over the entire input stream or
over a sliding window), multiplex and demultiplex (corresponding to union
and group-by)
• Cougar
➡ Model sources as ADTs
➡ SQL-like syntax + $every() clause to specify re-execution frequency

Procedural Languages
• Let the user specify how the data should flow through the system
• Example: Aurora
• Aurora
➡ Accepts streams as inputs and generates output streams
➡ Static data sets may be incorporated into query plans via connection points
➡ SQuAl algebra
✦ Seven operators: projection, union, map, buffered sort, windowed aggregate,
binary band join, resample
➡ Interface includes
✦ Boxes that correspond to operators
✦ Edges that connect boxes that correspond to data flow
✦ User creates the execution plan

Comparison of Languages
Language/Sys
tem
Allowed
inputs
Allowed
outputs
Novel
operators
Supported
windows
Execution
frequency
CQL/STREA
M
Streams and
relations
Streams and
relations
Relation-to-
stream,
stream-to-
relation
Sliding Continuous
or periodic
GSQL/
Gigascope
Streams Streams Order-
preserving
union
Landmark Periodic
StreaQuel/
TelegraphCQ
Streams and
relations
Sequences of
relations
WindowIs Fixed,
landmark,
sliding
Continuous
or periodic
Tribeca Single
stream
Streams Multiplex,
demultiplex
Fixed,
landmark,
sliding
Continuous
SQuAl/Auror
a
Streams and
relations
Streams Resample,
map,
buffered sort
Fixed,
landmark,
sliding
Continuous
or periodic

Operators over Unbounded
Streams
• Simple relational operators (selection,
projection) are fine
• Other operators (e.g., nested loop join)
are blocking
➡ You need to see the entire inner
operand
• For some blocking operators, non-
blocking versions exist
➡ Symmetric hash join
a
a
a
a
b
pass
or drop
S1
a
b
d
a
c
e
f
g
d
b
d
b
f
f
insert
probe
generate
result
S1 S2

Blocking Operators
• Alternatives if no non-blocking version exists
➡ Constraints over the input streams
✦ Schema-level
✦ Data-level
✓ Punctuations
➡ Approximation
✦ Summaries
✓ Counting methods
✓ Sketches
➡ Windowed operations

Operators over Sliding Windows
• Joins and aggregation may require unbounded state, so they typically
operate over sliding windows
• E.g., track the maximum value in an on-line sequence over a sliding
window of the last N time units
75 53 67 6871 67 73 70 68 65 64 62 61
time
Max = 75Max = ?

Operators over Sliding Windows
• Issues
➡ Need to store the window so that we “remember what to forget” and when
➡ Need to undo previous results by way of negative tuples

Query Processing
• Queuing and scheduling
➡ Queues allow sources to push data into the query plan and operators to pull
data when they need them
➡ Timeslicing
➡ Allowing multiple operators to process one or multiple tuples
• Tuple expiration
➡ Removing old tuples from their state buffers and (possibly) update answers
➡ Time-based window: simple – when time moves
✦ Join results have interesting expiration times
✦ Negation operator may force tuples to expire earlier
➡ Count-based window: no. of tuples constant  overwrite the oldest tuple
with the new arriving tuple

Query Processing (cont’d)
• Continuous query processing over sliding windows
➡ Negative tuple approach
➡ Direct approach

Negative Tuple Approach
• Negative tuples flow through the
plan
• Corresponding “real” tuples
deleted from operator state
• Updated answer generated, if
necessary
• Each tuple is processed twice
Stream 1 Stream 2
MAX
Index
lookup
Index
lookup
Negative
tuple

Direct Approach
• No negative tuples
• Operator states are scanned each
time window moves
• Updated answer generated, if
necessary
• Each tuple is processed once, but
state maintenance expensive
Stream 1 Stream 2
MAX
Check
timestamps
Check
timestamps

Periodic Query Evaluation
• Generate output periodically rather than continuously
• No need to react to every insertion/expiration
• E.g., compute MAX over a 10-minute window that slides every minute
➡ Store MAX over each non-overlapping one-minute chunk
➡ Take the max of the MAXes stored in each chunk
time
max = max(10,17,…,37,32) = 37
10171333352815163732

DSMS Optimization Framework
• General idea: similar to cost-based DBMS query optimization
• Generate candidate query plans
➡ New DSMS-specific rewritings: selections and time-based sliding windows
commute, but not selections and count-based windows
• Compute the cost of some of the plans and choose the cheapest plan
➡ New cost model for persistent queries:
✦ per unit time
✦ queries typically evaluated in main memory, so disk I/O is not a
concern

Additional DSMS Optimizations –
Scheduling
• Scheduling
• Many tuples at a time:
➡ Each operator gets a timeslice and processes all the tuples in its input queue
• Many operators at a time:
➡ Each tuple is processed by all the operators in the pipeline
• Choice of scheduling strategy depends upon optimization goal
➡ Minimize end-to-end latency?
➡ Minimize queue sizes?

Adaptivity
• System conditions can change throughout the lifetime of a persistent query
➡ Query workload can change
➡ Stream arrival rates can change
• Adjust the query plan on-the-fly
➡ Or do away with the query plan and route tuples through the query operators
according to some routing strategy
✦ Eddies approach

Load Shedding
• Random load shedding
➡ Randomly drop a fraction of arriving tuples
• Semantic load shedding
➡ Examine the contents of a tuple before deciding whether or not to drop it
➡ Some tuples may have more value than others
• Or, rather than dropping tuples:
➡ Spill to disk and process during idle times
➡ Shorten the windows
➡ Update the answer less often

Multi-Query Processing
• DBMS: queries are typically issued individually
• DSMS: many persistent queries may be in the system at any given time
➡ Some of them may be similar and could be executed together
➡ E.g., similar SELECT and WHERE clauses, but different window length in the
FROM clause
➡ Or, same SELECT and FROM clauses, but different predicate in the WHERE
clause

Cloud Computing
• The vision
➡ On demand, reliable services provided over the Internet (the “cloud”) with
easy access to virtually infinite computing, storage and networking resources
• Simple and effective!
➡ Through simple Web interfaces, users can outsource complex tasks
✦ Data mgt, system administration, application deployment
➡ The complexity of managing the infrastructure gets shifted from the users'
organization to the cloud provider
• Capitalizes on previous computing models
➡ Web services, utility computing, cluster computing, virtualization, grid
computing

Great Opportunities for
Research!
• Technical Grand Challenge
➡ Cost-effective support of the very large scale of the infrastruture to manage
lots of users and resources with high QoS
• Current solutions are ad-hoc and proprietary
➡ Developed by Web industry giants such as Amazon, Google, Microsoft and
Yahoo
✦ E.g. Google File System (GFS)
➡ Specific, simple applications with low consistency needs
• But the research community is catching up
➡ Many new conferences and journals on Cloud Computing
✦ Distributed systems, OS, data management communities
➡ Open Source alternatives, e.g. Hadoop HDFS
➡ As the complexity of applications increases, the implication of the research
community is needed

Cloud Definition
• Working def.: a cloud provides on demand resources and services over the
Internet, usually at the scale and with the reliability of a data center
• Everything gets delivered as a service
➡ Pay-as-you-go pricing model, whereby users only pay for the resources they
consume
➡ Service Level Agreement (SLA) to govern the use of services by customers
and support pricing
✦ E.g. the service uptime during a billing cycle (e.g. a month) should be at least
99%, and if the commitment is not met, the customer should get a service credit

Cloud Taxonomy
• Infrastructure-as-a-Service (IaaS)
• Computing, networking and storage resources, as a service
• Provides elasticity: ability to scale up (add more resources) or scale down (release resources)
as needed
➡ E.g. Amazon Web Services
• Software-as-a-Service (SaaS)
➡ Application software as a service
➡ Generalizes the earlier ASP model with tools to integrate other applications, e.g. developed
by the customer (using the cloud platform)
➡ Hosted applications: from simple (email, calendar) to complex (CRM, data analysis or social
network)
➡ E.g. Safesforce CRM system
• Platform-as-a-Service (PaaS)
➡ Computing platform with development tools and APIs as a service
➡ Enables developers to create and deploy custom applications directly on the cloud
infrastructure and integrate them with applications provided as SaaS
➡ Ex. Google Apps

Cloud Benefits
• Reduced cost
➡ Customer side: the IT infrastructure needs not be owned and managed, and
billed only based on resource consumption
➡ Cloud provider side: by sharing costs for multiple customers, reduces its cost
of ownership and operation to the minimum
• Ease of access and use
➡ Customers can have access to IT services anytime, from anywhere with an
Internet connection
• Quality of Service (QoS)
➡ The operation of the IT infrastructure by a specialized, experienced provider
(including with its own infrastructure) increases QoS
• Elasticity
➡ Easy for customers to deal with sudden increases in loads by simply creating
more virtual machines (VMs)

The Main Issue: Security and
Privacy
• Current solutions
➡ Internal cloud (or private cloud) : the use of cloud technologies but in a
private network behind a firewall
✦ Much tighter security
✦ Reduced cost advantage because the infrastructure is not shared with other
customers (as in public cloud)
✦ Compromise: hybrid cloud (internal cloud for OLTP + public cloud for OLAP)
➡ Virtual private cloud: Virtual Private Network (VPN) within a public cloud
with security services
✦ Promise of a similar level of security as an internal cloud and tighter integration
with internal cloud security
✦ But such security integration is complex and requires talented security administrators

• OLTP
➡ Operational databases of average sizes
(TB), write-intensive
➡ ACID transactional properties, strong
data protection, response time
guarantees
• Not very suitable for cloud
➡ Requires shared-disk multiprocessors
➡ Corporate data gets stored at
untrusted host
• OLAP
➡ Historical databases of very large
sizes (PB), read-intensive, can accept
relaxed ACID properties
• Suitable for cloud
➡ Shared-nothing clusters of commodity
servers are cost-effective
➡ Sensitive data can be hidden
(anonymized) in the cloud

Grid Architecture
• Access through Web
services to distributed,
heterogeneous resources
➡ supercomputers, clusters,
databases, etc.
• For Virtual Organizations
➡ which share the same
resources, with common
rules and access rights
• Grid middleware
➡ security, database,
provisioning, job
scheduling, workflow
management, etc.
Cluster 1
Service Compute
nodes nodes
User 1 User 2
Cluster 2
Service Compute
nodes nodes
Cluster 3
Service Storage
nodes nodes
reserve
deploy
run
clean
store
clean
reserve
store
WS calls
MPI calls
WS calls
WS calls

Cloud Architecture
User 1 User 2
Cluster 1
Service Compute Storage
nodes nodes nodes
Create VMs
start VMs
terminate
pay
WS calls
Cluster 2
Service Compute Storage
nodes nodes nodes
reserve
store
pay
• Like grid, access to
resources using Web
services
➡ But less distribution,
more homogeneity, and
bigger clusters
• For different customers
➡ Including individuals
• Replication across sites
for high availability
• Scalability, SLA,
accounting and pricing
essential

Cloud Data Management: why
not RDBMS?
• RDBMS all have a distributed and parallel version
➡ With SQL support for all kinds of data (structured, XML, multimedia,
streams, etc.)
• But the “one size fits all” approach has reached the limits
➡ Loss of performance, simplicity and flexibility for applications with specific,
tight requirements
➡ New specialized DBMS engines better: column-oriented DBMS for OLAP,
DSMS for stream processing, etc.
• For the cloud, RDBMS provide both
➡ Too much: ACID transactions, complex query language, lots of tuning knobs
➡ Too little: specific optimizations for OLAP, flexible programming model,
flexible schema, scalability

Cloud Data Management
Solutions
• Cloud data
➡ Can be very large (e.g. text-based or scientific applications), unstructured or
semi-structured, and typically append-only (with rare updates)
• Cloud users and application developers
➡ In very high numbers, with very diverse expertise but very little DBMS
expertise
• Therefore, current cloud data management solutions trade consistency for
scalability, simplicity and flexibility
➡ New file systems: GFS, HDFS, …
➡ New DBMS: Amazon SimpleDB, Google Base, Google Bigtable, Yahoo Pnuts,
etc.
➡ New parallel programming: Google MapReduce (and its many variations)

Google File System (GFS)
• Used by many Google applications
➡ Search engine, Bigtable, Mapreduce, etc.
• The basis for popular Open Source implementations: Hadoop HDFS
(Apache & Yahoo)
• Optimized for specific needs
➡ Shared-nothing cluster of thousand nodes, built from inexpensive harware =>
node failure is the norm!
➡ Very large files, of typically several GB, containing many objects such as web
documents
➡ Mostly read and append (random updates are rare)
✦ Large reads of bulk data (e.g. 1 MB) and small random reads (e.g. 1 KB)
✦ Append operations are also large and there may be many concurrent clients that
append the same file
✦ High throughput (for bulk data) more important than low latency

Design Choices
• Traditional file system interface (create, open, read, write, close, and delete
file)
➡ Two additional operations: snapshot and record append.
• Relaxed consistency, with atomic record append
➡ No need for distributed lock management
➡ Up to the application to use techniques such as checkpointing and writing
self-validating records
• Single GFS master
➡ Maintains file metadata such as namespace, access control information, and
data placement information
➡ Simple, lightly loaded, fault-tolerant
• Fast recovery and replication strategies

GFS Distributed Architecture
• Files are divided in fixed-size partitions, called chunks, of large size, i.e. 64
MB, each replicated at several nodes
Application
GFS client
Get chunk location
Get chunk data
GFS chunk server
Linux file system
GFS
Master
GFS chunk server
Linux file system

Google Bigtable
• Database storage system for a shared-nothing cluster
➡ Uses GFS to store structured data, with fault-tolerance and availability
• Used by popular Google applications
➡ Google Earth, Google Analytics, Orkut, etc.
• The basis for popular Open Source implementations
➡ Hadoop Hbase on top of HDFS (Apache & Yahoo)
• Specific data model that combines aspects of row-store and column-store
DBMS
➡ Rows with multi-valued, timestamped attributes
✦ A Bigtable is defined as a multidimensional map, indexed by a row key, a
column key and a timestamp, each cell of the map being a single value (a string)
• Dynamic partitioning of tables for scalability

A Bigtable Row
Row key Contents: Anchor: Language:
"google.com"
"google.com"
"Google"
"<html> …<html>""com.google.www" "english"
"<html> …<html>"
inria.fr
t5
t1t2
t3
t4
t1
uwaterloo.ca
Row unique id Column family Column key
Column family = a kind of multi-valued attribute
• Set of columns (of the same type), each identified by a key
- Colum key = attribute value, but used as a name
• Unit of access control and compression

Bigtable DDL and DML
• Basic API for defining and manipulating tables, within a programming
language such as C++
➡ Various operators to write and update values, and to iterate over subsets of
data, produced by a scan operator
➡ Various ways to restrict the rows, columns and timestamps produced by a
scan, as in relational select, but no complex operator such as join or union
➡ Transactional atomicity for single row updates only

Dynamic Range Partitioning
• Range partitioning of a table on the row key
➡ Tablet = a partition corresponding to a row range.
➡ Partitioning is dynamic, starting with one tablet (the entire table range) which
is subsequently split into multiple tablets as the table grows
➡ Metadata table itself partitioned in metadata tablets, with a single root tablet
stored at a master server, similar to GFS’s master
• Implementation techniques
➡ Compression of column families
➡ Grouping of column families with high locality of access
➡ Aggressive caching of metadata information by clients

Yahoo! PNUTS
• Parallel and distributed database system
• Designed for serving Web applications
➡ No need for complex queries
➡ Need for good response time, scalability and high availability
➡ Relaxed consistency guarantees for replicated data
• Used internally at Yahoo!
➡ User database, social networks, content metadata management and shopping
listings management apps

Design Choices
• Basic relational data model
➡ Tables of flat records, Blob attributes
➡ Flexible schemas
✦ New attributes can be added at any time even though the table is being queried or
updated
✦ Records need not have values for all attributes
• Simple query language
➡ Selection and projection on a single relation
➡ Updates and deletes must specify the primary key
• Range partitioning or hashing of tables into tablets
➡ Placement in a cluster (at a site)
➡ Sites in different geographical regions maintain a complete copy of the system and
of each table
• Publish/subscribe mechanism with guaranteed delivery, for both reliability and
replication
➡ Used to replay lost updates, thus avoiding a traditional database log

Relaxed Consistency Model
• Between strong consistency and eventual consistency
➡ Motivated by the fact that Web applications typically manipulate only one
record at a time, but different records may be used under different geographic
locations
• Per-record timeline consistency: guarantees that all replicas of a given
record apply all updates to the record in the same order
• Several API operations with different guarantees
➡ Read-any: returns a possibly stale version of the record
➡ Read-latest: returns the latest copy of the record
➡ Write: performs a single atomic write operation

MapReduce
• For data analysis of very large data sets
➡ Highly dynamic, irregular, schemaless, etc.
➡ SQL or Xquery too heavy
• New, simple parallel programming model
➡ Data structured as (key, value) pairs
✦ E.g. (doc-id, content), (word, count), etc.
➡ Functional programming style with two functions to be given:
✦ Map(k1,v1) list(k2,v2)
✦ Reduce(k2, list (v2)) list(v3)
• Implemented on GFS on very large clusters

MapReduce Typical Usages
• Counting the numbers of some words in a set of docs
• Distributed grep: text pattern matching
• Counting URL access frequencies in Web logs
• Computing a reverse Web-link graph
• Computing the term-vectors (summarizing the most important words) in a
set of documents
• Computing an inverted index for a set of documents
• Distributed sorting

MapReduce Processing
Map
…
(k1,v)
(k2,v)
Group
by k
Map
(k2,v)
(k2,v)
Map (k1,v)
Map
(k1,v)
(k2,v)
(k1,(v,v,v))
(k2,(v,v,v,v))
Reduce
Reduce
Group
by k
Inputdataset
Outputdataset

MapReduce Example
EMP (ENAME, TITLE, CITY)
Query: for each city, return the number of employees whose name is "Smith"
SELECT CITY, COUNT(*)
FROM EMP
WHERE ENAME LIKE "%Smith"
GROUP BY CITY
With MapReduce
Map (Input (TID,emp), Output: (CITY,1))
if emp.ENAME like "%Smith" return (CITY,1)
Reduce (Input (CITY,list(1)), Output: (CITY,SUM(list(1)))
return (CITY,SUM(1*))

Fault-tolerance
• Fault-tolerance is fine-grain and well suited for large jobs
• Input and output data are stored in GFS
➡ Already provides high fault-tolerance
• All intermediate data is written to disk
➡ Helps checkpointing Map operations, and thus provides tolerance from soft
failures
• If one Map node or Reduce node fails during execution (hard failure)
➡ The tasks are made eligible by the master for scheduling onto other nodes
➡ It may also be necessary to re-execute completed Map tasks, since the input
data on the failed node disk is inaccessible

MapReduce vs Parallel DBMS
• [Pavlo et al. SIGMOD09]: Hadoop MapReduce vs two parallel DBMS, one row-
store DBMS and one column-store DBMS
➡ Benchmark queries: a grep query, an aggregation query with a group by clause on a
Web log, and a complex join of two tables with aggregation and filtering
➡ Once the data has been loaded, the DBMS are significantly faster, but loading is
much time consuming for the DBMS
➡ Suggest that MapReduce is less efficient than DBMS because it performs repetitive
format parsing and does not exploit pipelining and indices
• [Dean and Ghemawat, CACM10]
➡ Make the difference between the MapReduce model and its implementation which
could be well improved, e.g. by exploiting indices
• [Stonebraker et al. CACM10]
➡ Argues that MapReduce and parallel DBMS are complementary as MapReduce
could be used to extract-transform-load data in a DBMS for more complex OLAP.

Issues in Cloud Data
Management
• Main challenge: provide ease of programming, consistency, scalability and
elasticity at the same time, over cloud data
• Current solutions
➡ Quite successful for specific, relatively simple applications
➡ Have sacrificed consistency and ease of programming for the sake of
scalability
➡ Force applications to access data partitions individually, with a loss of
consistency guarantees across data partitions
• For more complex apps. with tighter consistency requirements
➡ Developers are faced with a very difficult problem: providing isolation and
atomicity across data partitions through careful engineering

Research Directions in Data
Management
• Declarative programming languages for the cloud
➡ E.g. BOOM project (UC Berkeley] using Overlog
• Parallel OLAP query processing with consistency guarantees wrt
concurrent updates
➡ E.g. using snapshot isolation
• Scientific workflow management
➡ E.g. with P2P worker nodes
• Data privacy preserving query processing
➡ E.g. queries on encrypted data
• Autonomic data management
➡ E.g. automatic management of replication to deal with load changes
• Green data management
➡ E.g. optimizing for energy efficiency

Database ,18 Current Issues

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