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Making Making Fast Fast

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OLTP Through the Looking Glass,OLTP Through the Looking Glass,and What We Found There and What We Found There SIGMOD 2008SIGMOD 2008

CPU Cycles

TPC-C NewOrder

8.1%

21.1%

18.7%

10.2%

29.6%

12.3%

Legacy Systems

FastRepetitiveSmall

OLTP Transactio

ns

Main Memory • Parallel • Shared-NothingTransaction Processing

H-Store: A High-Performance, DistributedH-Store: A High-Performance, DistributedMain Memory Transaction Processing SystemMain Memory Transaction Processing SystemVLDB VLDB vol. 1, issue 2, 2008vol. 1, issue 2, 2008

ClientApplication

Database Cluster

Procedure NameInput

Parameters

Stored Procedure Execution

Database Cluster

Transaction

Result

TPC-C NewOrder

Skew-Aware Automatic Database PartitioningSkew-Aware Automatic Database Partitioningin Shared-Nothing, Parallel OLTP Systemsin Shared-Nothing, Parallel OLTP SystemsSIGMOD 2012SIGMOD 2012

Partition Partition database to database to reduce the reduce the number of number of distributed distributed txns.txns.

OptimizatioOptimization #1:n #1:

o_id o_c_id

o_w_id

…

78703 1004 5 -

78704 1002 3 -

78705 1006 7 -

78706 1005 6 -

78707 1005 6 -

78708 1003 12 -

c_id c_w_id

c_last

…

1001 5 RZA -

1002 3 GZA -

1003 12 Raekwon

-

1004 5 Deck -

1005 6 Killah -

1006 7 ODB -

CUSTOMER ORDERS

CUSTOCUSTOMERMERORDERORDER

SS

CUSTOCUSTOMERMERORDERORDER

SS

CUSTOCUSTOMERMERORDERORDER

SS

ITEMi_id i_na

mei_price

…

603514

XXX 23.99 -

267923

XXX 19.99 -

475386

XXX 14.99 -

578945

XXX 9.98 -

476348

XXX 103.49

-

784285

XXX 69.99 -

ITEMITEM ITEMITEM ITEMITEM

CUSTOMERc_id c_w_i

dc_las

t…

1001 5 RZA -

1002 3 GZA -

1003 12 Raekwon

-

1004 5 Deck -

1005 6 Killah -

1006 7 ODB -

CUSTOCUSTOMERMERORDERORDER

SS

CUSTOCUSTOMERMERORDERORDER

SS

CUSTOCUSTOMERMERORDERORDER

SSITEMITEM ITEMITEM ITEMITEM

Client Application

NewOrder(5, “Method Man”,

1234)

CUSTCUSTOMEROMERORDEORDE

RSRSITEMITEM

CUSTCUSTOMEROMERORDEORDE

RSRSITEMITEM

CUSTCUSTOMEROMERORDEORDE

RSRSITEMITEM

CUSTCUSTOMEROMERORDEORDE

RSRSITEMITEM

…

Large-Neighorhood Large-Neighorhood Search AlgorithmSearch Algorithm

Schema

Workload

-------

-------

-------

-------

-------

-------

DDL

DDL

SELECT * FROM WAREHOUSE WHERE W_ID = 10;

INSERT INTO ORDERS (O_W_ID, O_D_ID, O_C_ID) VALUES (10, 9, 12345);

⋮

SELECT * FROM WAREHOUSE WHERE W_ID = 10;

INSERT INTO ORDERS (O_W_ID, O_D_ID, O_C_ID) VALUES (10, 9, 12345);

⋮

SELECT * FROM WAREHOUSE WHERE W_ID = 10;

INSERT INTO ORDERS (O_W_ID, O_D_ID, O_C_ID) VALUES (10, 9, 12345);

⋮

SELECT * FROM WAREHOUSE WHERE W_ID = 10;

INSERT INTO ORDERS (O_W_ID, O_D_ID, O_C_ID) VALUES (10, 9, 12345);

⋮

SELECT * FROM WAREHOUSE WHERE W_ID = 10;

SELECT * FROM DISTRICT D_W_ID = 10 AND D_ID =9;

INSERT INTO ORDERS (O_W_ID, O_D_ID, O_C_ID) VALUES (10, 9, 12345);

⋮

SELECT * FROM WAREHOUSE WHERE W_ID = 10;

SELECT * FROM DISTRICT D_W_ID = 10 AND D_ID =9;

INSERT INTO ORDERS (O_W_ID, O_D_ID, O_C_ID) VALUES (10, 9, 12345);

⋮

SELECT * FROM WAREHOUSE WHERE W_ID = 10;

SELECT * FROM DISTRICT WHERE D_W_ID = 10 AND D_ID =9;

INSERT INTO ORDERS (O_W_ID, O_D_ID, O_C_ID,…) VALUES(10, 9, 12345,…);

⋮

SELECT * FROM WAREHOUSE WHERE W_ID = 10;

SELECT * FROM DISTRICT WHERE D_W_ID = 10 AND D_ID =9;

INSERT INTO ORDERS (O_W_ID, O_D_ID, O_C_ID,…) VALUES(10, 9, 12345,…);

⋮

NewOrdNewOrd

ererNewOrdNewOrd

ererDDLDDLCUSTOMERCUSTOMER

ITEMITEM

ORDERSORDERS

Workload

-------

-------

-------

-------

-------

-------

Schema

DDL

DDL

Initial DesignRelaxationLocal Search

Restart

Large-Neighborhood

Search

TATP TPC-CTPC-C Skewed

(txn/s)

+88% +16% +183%

HorticultureState-of-the-Art

Throughput

TATP SEATS

TPC-C TPC-C Skewed

AuctionMark TPC-E

Search Times

% S

ing

le-P

art

itio

ned

Tra

nsa

ctio

ns

Database Cluster

Database Cluster

ClientApplication

Undo Log

On Predictive Modeling for OptimizingOn Predictive Modeling for OptimizingTransaction Execution in Parallel OLTP SystemsTransaction Execution in Parallel OLTP SystemsVLDB, vol 5. issue 2, October 2011VLDB, vol 5. issue 2, October 2011

Predict what Predict what txns will do txns will do beforebefore they they execute.execute.

OptimizatioOptimization #2:n #2:

Database Cluster

Database Cluster

ClientApplication

» Partitions Touched?» Undo Log?

» Done with Partitions?

w_id=0i_w_ids=[0,1] i_ids=[1001,1002]

w_id=0i_w_ids=[0,1] i_ids=[1001,1002]

Input Parameters:Current State:

SELECT * FROM WAREHOUSE WHERE W_ID = ?SELECT * FROM WAREHOUSE WHERE W_ID = ?

GetWarehouse:

Confidence Coefficient:

0.96

Best Partition: 0

Partitions Accessed:

{ 0 }

Use Undo Logging:

Yes

Transaction Estimate:

Estimated Execution Path

w_id=0i_w_ids=[0,1] i_ids=[1001,1002]

w_id=0i_w_ids=[0,1] i_ids=[1001,1002]

Input Parameters:

TATP TPC-CAuctionMark

(txn/s)

+57% +126%+117%

HoudiniAssume Single-Partitioned

Throughput

TATP TPC-CAuctionMark

Prediction Overhead

Future WorkFuture Work::Reduce distributed txn overheadReduce distributed txn overheadthrough creative scheduling.through creative scheduling.

Conclusion:Conclusion:Achieving fast performance isAchieving fast performance ismore than just using only RAM.more than just using only RAM.

h-h-storestorehstore.cs.br

own.eduhstore.cs.br

own.edugithub.com/apavlo/h-storegithub.com/apavlo/h-store

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