6/28/2015EECS 584, Fall 20111 The Gamma Database Machine DeWitt, Ghandeharizadeh, Schneider, Bricker, Hsiao, Rasmussen Deepak Bastakoty (With slide material

04/18/23 EECS 584, Fall 2011 1

The Gamma Database MachineDeWitt, Ghandeharizadeh, Schneider,

Bricker, Hsiao, Rasmussen

Deepak Bastakoty

(With slide material sourced from Ghandeharizadeh and DeWitt)

04/18/23 EECS 584, Fall 2011 2

Outline

• Motivation • Physical System Designs• Data Clustering• Failure Management• Query Processing• Evaluation and Results• Bonus : Current Progress, Hadoop, Clustera..

04/18/23 EECS 584, Fall 2011 3

Outline


04/18/23 EECS 584, Fall 2011 4

Motivation

What ?– Parallelizing Databases

• How to setup system / hardware?• How to distribute data ?• How to tailor software ?• Where does this rabbit hole lead?

04/18/23 EECS 584, Fall 2011 5

Motivation

Why ?

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Motivation

Why ?– Obtain faster response time– Increase query throughput– Improve robustness to failure– Reduce system cost– Enable Massive Scalability

(Faster, Safer, Cheaper, and More of it !)

04/18/23 EECS 584, Fall 2011 7

Outline


System Design Schemes

Shared Memory• DeWitt’s DIRECT etc

Shared Disk• Storage Networks

Shared Nothing• Gamma, everything else

(PigLatin, MapReduce setups, BigTable, GFS)


Shared Memory– poor scalability (number of nodes)– custom hardware / systems


Shared Disk– storage networks– used to be popular– why ?

Network

Advantages:Advantages:

HighAvailability

DataBackup

DataSharing

– Many clients share storage Many clients share storage and data.and data.

– Redundancy is Redundancy is implemented in one place implemented in one place protecting all clients from protecting all clients from disk failure.disk failure.

– Centralized backup: The Centralized backup: The administrator does not administrator does not care/know how many care/know how many clients are on the network clients are on the network sharing storage.sharing storage.

Shared Disk

Shared Disk Storage Area Storage Area

Network (SAN):Network (SAN):

– Block level access,Block level access,– Write to storage is Write to storage is

immediate,immediate,– Specialized hardware Specialized hardware

including switches, including switches, host bus adapters, disk host bus adapters, disk chassis, battery chassis, battery backed caches, etc.backed caches, etc.

– ExpensiveExpensive– Supports transaction Supports transaction

processing systems.processing systems.

Network Attached Network Attached Storage (NAS):Storage (NAS):

– File level access,File level access,

– Write to storage might Write to storage might be delayed,be delayed,

– Generic hardware,Generic hardware,

– In-expensive,In-expensive,

– Not appropriate for Not appropriate for transaction processing transaction processing systems.systems.

Shared Nothing

Each node has its own processor(s), memory, disk(s)

Network…. ….

CPU1

CPU2

CPUn

DRAM 1

DRAM 2

DRAM D

…

…

CPU1

CPU2

CPUn

DRAM 1

DRAM 2

DRAM D

…

…

Node MNode 1

Shared Nothing

Why ?

– Low Bandwidth Parallel Data Processing

– Commodity hardware (Cheap !!)

– Minimal Sharing = Minimal Interference

– Hence : Scalability (keep adding nodes!)

– DeWitt says : Even writing code is simpler

Shared Nothing

Why ?

– Low Bandwidth Parallel Data Processing

– Commodity hardware (Cheap !!)

– Minimal Sharing = Minimal Interference

– Hence : Scalability (keep adding nodes!)

– DeWitt says : Even writing code is simpler

04/18/23 EECS 584, Fall 2011 16

Outline


No Shared Data Declustering

Physical ViewPhysical View

BobBob 2020 10K10K

ShidehShideh 1818 35K35K

TedTed 5050 60K60K

KevinKevin 6262 120K120K

AngelaAngela 5555 140K140K

MikeMike 4545 90K90K

Logical ViewLogical View

namename ageage salarysalary

Spreading data between disks :

– Attribute-less partitioning• Random • Round-Robin

– Single Attribute Schemes• Hash De-clustering• Range De-clustering

– Multiple Attributes schemes possible• MAGIC, BERD etc


Spreading data between disks :

– Attribute-less partitioning• Random • Round-Robin

– Single Attribute Schemes• Hash De-clustering• Range De-clustering

– Multiple Attributes schemes possible• MAGIC, BERD etc


BobBob 2020 10K10K


TedTed 5050 60K60K





salary % 3salary % 3

TedTed 5050 60K60K



BobBob 2020 10K10K






salary is the salary is the partitioning partitioning

attribute.attribute.

Hash Declustering

Hash Declustering Selections with equality predicates referencing the Selections with equality predicates referencing the

partitioning attribute are directed to a single node:partitioning attribute are directed to a single node:

– Retrieve Emp where salary = 60KRetrieve Emp where salary = 60K

Equality predicates referencing a non-partitioning attribute Equality predicates referencing a non-partitioning attribute and range predicates are directed to all nodes:and range predicates are directed to all nodes:

– Retrieve Emp where age = 20 Retrieve Emp where age = 20

– Retrieve Emp where salary < 20KRetrieve Emp where salary < 20K

SELECT *SELECT *FROM FROM EmpEmpWHERE salary=60KWHERE salary=60K

SELECT *SELECT *FROM FROM EmpEmpWHERE salary<20KWHERE salary<20K

BobBob 2020 10K10K


TedTed 5050 60K60K





salary is the salary is the partitioning partitioning

attribute.attribute.

BobBob 2020 10K10K



TedTed 5050 60K60K






0-50K0-50K 51K-100K51K-100K 101K-101K-∞∞

Range Declustering

Range Declustering Equality and range predicates referencing the partitioning Equality and range predicates referencing the partitioning

attribute are directed to a subset of nodes:attribute are directed to a subset of nodes:

– Retrieve Emp where salary = 60KRetrieve Emp where salary = 60K

– Retrieve Emp where salary < 20KRetrieve Emp where salary < 20K

Predicates referencing a non-partitioning attribute are Predicates referencing a non-partitioning attribute are directed to all nodes.directed to all nodes.

In the example, both queries are In the example, both queries are directed to one node.directed to one node.

Range selection Range selection predicate using a predicate using a clustered Bclustered B++-tree-tree

0.01% selectivity 0.01% selectivity

(10 records)(10 records)

RangeRange

Hash/Random/Round-robinHash/Random/Round-robin

Multiprogramming LevelMultiprogramming Level

Throughput Throughput (Queries/Second)(Queries/Second)

Declustering Tradeoffs

Range selection Range selection predicate using a predicate using a clustered Bclustered B++-tree-tree

1% selectivity 1% selectivity

(1000 records)(1000 records)

RangeRange

Hash/Random/Round-robinHash/Random/Round-robin

Multiprogramming LevelMultiprogramming Level

Throughput Throughput (Queries/Second)(Queries/Second)


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Why the difference ?


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Why the difference ?

• When selection was small, Range spread out load and was ideal

• When selection increased, Range caused high workload on one/few nodes while Hash spread out load


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Outline


04/18/23 EECS 584, Fall 2011 29

Failure Management

Key Questions– Robustness (How much damage recoverable?)– Availability (How likely? Hot Recoverable?)– MTTR (Mean Time To Recovery)

Consider two declustering schemes– Interleaved Declustering (TeraData)– Chained Declustering (Gamma DBM)

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Interleaved Declustering

A partitioned table has a primary and a backup copy.

The primary copy is constructed using one of the partitioning techniques.

The secondary copy is constructed by:– Dividing the nodes into clusters (cluster size 4 here),– Partition a primary fragment (R0) across the remaining nodes of the

cluster: 1, 2, and 3. Realizing r0.0, r0.1, and r0.2.

Interleaved Declustering

On failure, query load re-directed to backup nodes in cluster MTTR :

– replace node– reconstruct failed primary from backups– reconstruct backups stored in failed node

Second failure before this can cause unavailability Large cluster size improves failure load balancing but increases risk of

data being unavailable

Chained Declustering (Gamma)

Nodes are divided into disjoint groups called relation clusters.

A relation is assigned to one relation cluster and its records are declustered across the nodes of that relation cluster using a partitioning strategy (Range, Hash).

Given a primary fragment Ri, its backup copy is assigned to node (i+1) mod M (M is the number of nodes in the relation cluster).


During normal operation:

– Read requests are directed to the fragments of primary copy,

– Write requests update both primary and backup copies.


In presence of failure:– Both primary and backup fragments are used for read operations,

• Objective: Balance the load and avoid bottlenecks!– Write requests update both primary and backup copies.

Note:– Load of R1 (on node 1) is pushed to node 2 in its entirety.– A fraction of read request from each node is pushed to the others

for a 1/8 load increase attributed to node 1’s failure.


MTTR involves:– Replace node 1 with a new node,– Reconstruct R1 (from r1 on node 2) on node 1,– Reconstruct backup copy of R0 (i.e., r0) on node 1.

Note:– Once Node 1 becomes operational, primary copies are used

to process read requests.


Any two node failures in a relation cluster does not result in data un-availability.

Two adjacent nodes must fail in order for data to become unavailable

04/18/23 EECS 584, Fall 2011 37

Outline


Query Processing

General Idea : Divide And Conquer

Parallelizing Hash Join

Consider Hash Join– Join of Tables A and B using attribute j (A.j = B.j)

consists of two phase:

1.Build phase: Build a main-memory hash table on Table A using the join attribute j, e.g., build a hash table on the Toy department using dno as the key of the hash table.

2.Probe phase: Scan table B one record at a time and use its attribute j to probe the hash table constructed on Table A, e.g., probe the hash table using the rows of the Emp department.

R join S where R is the inner table.

Parallelism and Hash-Join

Example Join of Emp and Dept

Emp join Dept (using dno)

SS# Name Age Salary dno

1 Joe 24 20000 2

2 Mary 20 25000 1

3 Bob 22 27000 1

4 Kathy 30 30000 2

5 Shideh 4 4000 1

EMP

dno dname floor mgrss#

1 Toy 1 5

2 Shoe 2 1

Dept

SS# Name Age Salary dno dname floor mgrss#

1 Joe 24 20000 2 Shoe 2 1

2 Mary 20 25000 1 Toy 1 5

3 Bob 22 27000 1 Toy 1 5

4 Kathy 30 30000 2 Shoe 2 1

5 Shideh 4 4000 1 Toy 1 5

Hash-Join: Build Read rows of Dept table one at a time and place

in a main-memory hash table

1 Toy 1 5

2 Shoe 2 1dno % 7dno % 7

Hash-Join: Build Read rows of Emp table and probe the hash table.

1 Toy 1 5



1 Joe 24 20000 2

Hash-Join: Build Read rows of Emp table and probe the hash table and

produce results when a match is found. Repeat until all rows processed


1 Joe 24 20000 2

1 Toy 1 5


SS# Name Age Salary dno dname floor mgrss#

1 Joe 24 20000 2 Shoe 2 1

Hash-Join (slide for exam etc review only)

Prob: Table used to build hash table larger than memory

A divide-and-conquer approach:– Use the inner table (Dept) to construct n memory buckets where each

bucket is a hash table.– Every time memory is exhausted, spill a fixed number of buckets to the

disk.– The build phase terminates with a set of in-memory buckets and a set of

disk-resident buckets.– Read the outer relation (Emp) and probe the in-memory buckets for

joining records. For those records that map onto the disk-resident buckets, stream and store them to disk.

– Discard the in memory buckets to free memory space.– While disk-resident buckets of inner-relation exist:

• Read as many (say i) of the disk-resident buckets of the inner-relation into memory as possible.

• Read the corresponding buckets of the outer relation (Emp) to probe the in-memory buckets for joining records.

• Discard the in memory buckets to free memory space.• Delete the i buckets of the inner and outer relations.

Hash-Join: Build Two buckets of Dept table. One in memory and the

second is disk-resident.

1 Toy 1 5


Hash-Join: Probe Read Emp table and probe the hash table for joining records when

dno=1. With dno=2, stream the data to disk.

1 Toy 1 5



1 Joe 24 20000 2

2 Mary 20 25000 1

3 Bob 22 27000 1

4 Kathy 30 30000 2

5 Shideh 4 4000 1

Hash-Join: Probe Those rows of Emp table with dno=1 probed the

hash table and produce 3 joining records.

1 Toy 1 5



1 Joe 24 20000 2

4 Kathy 30 30000 2

Hash-Join: While loop Read the disk-resident bucket of Dept into

memory. Probe it with the disk-resident buckets of Emp

table to produce the remaining two joining records.

2 Shoe 2 1

dno % 7dno % 7


1 Joe 24 20000 2

4 Kathy 30 30000 2


Each node may perform hash-join independently when:

– The join attribute is the declustering attribute of the tables participating in the join operation.

– The participating tables are declustered across the same number of nodes using the same declustering strategy.

– The system may re-partition the table (see the next bullet) if its aggregate memory exceeds the size of memory the tables are declustered across.

Otherwise, the data must be re-partitioned to perform the join operation correctly.

R join S where R is the inner table.


04/18/23 EECS 584, Fall 2011 52

Outline


System Evaluation

Two Key Metrics

– Speed-up• Given a system with 1 node, does adding n

nodes speed it up with a factor of n ?

– Scale-up• Given a system with 1 node, does the response

time remain the same with n nodes ?

Ideal Parallelism

Speedup (Selection)

Scaleup (Selection)

Speedup (Join)

Scaleup (Join)

04/18/23 EECS 584, Fall 2011 59

Outline

• Motivation • Physical System Designs• Data Clustering• Failure Management• Query Processing• Overview of Results• Bonus: Current Progress, Hadoop, Clustera..

Modern Systems are Gamma too?

Ghandeharizadeh’s 2009 summary :

– All systems below, like Gamma, are “share nothing” and run on 1000s of nodes

Google File SystemGoogle File System

Google’s Bigtable Data ModelGoogle’s Bigtable Data Model

Google’s Map/Reduce FrameworkGoogle’s Map/Reduce Framework

Yahoo’s Pig Latin Yahoo’s Pig Latin

HadoopHadoop

PigPig

GFSGFS

BigTableBigTable

DeWitt’s Clustera :

– DBMS centric cluster management system

– Evolved from Gamma

– Like MapReduce but• Full DBMS support• SQL optimization

– Outperforms Hadoop!

Old is the New New?

Data complexity

Job complexity

Condor

Parallel SQL

Map/Reduce

Old is the New New?

04/18/23 EECS 584, Fall 2011 63

Outline

• Motivation • Physical System Designs• Data Clustering• Failure Management• Query Processing• Overview of Results• Bonus: Current Progress, Hadoop, Clustera..

Questions ?

Documents

6/28/2015EECS 584, Fall 20111 The Gamma Database Machine DeWitt, Ghandeharizadeh, Schneider, Bricker, Hsiao, Rasmussen Deepak Bastakoty (With slide material