Benchmarking Hybrid OLTP&OLAP Database · PDF fileI Nothink-time Benchmark Design: OLAP...

Benchmarking Hybrid OLTP&OLAP DatabaseSystems
Florian Funke Alfons Kemper Thomas Neumann
TU Mnchen

The Real-Time Business Intelligence Challenge
++OLAP
--OLTP
Dedicated OLAP-EnginesMonetDB, Vertica,SAP T-REX (BWA),IBM ISAO (BLINK)
++OLTP
--OLAP
Dedicated OLTP-EnginesVoltDB, SAP P*Time,TimesTen, SolidDB,
many Start-ups
I OLTP and OLAP: very different workloadsI Separate systems Extract-Transform-Load process required
I CostlyI Stale data in OLAP system
I Unsuitable for real-time Business Intelligence Hybrid DBMSI Various approaches to reconcile OLAP and OLTP
I Update stagingI VersioningI . . .
Benchmark for hybrid DBMS

The Real-Time Business Intelligence Challenge
Hybrid OLTP&OLAPIn-Memory DB-Systems
SAP NewDB, SanssouciDB, HyPer++OLAP
--OLTP
Dedicated OLAP-EnginesMonetDB, Vertica,SAP T-REX (BWA),IBM ISAO (BLINK)
++OLTP
--OLAP
Dedicated OLTP-EnginesVoltDB, SAP P*Time,TimesTen, SolidDB,
many Start-ups
I OLTP and OLAP: very different workloadsI Separate systems Extract-Transform-Load process required
I CostlyI Stale data in OLAP system
I Unsuitable for real-time Business Intelligence Hybrid DBMSI Various approaches to reconcile OLAP and OLTP
I Update stagingI VersioningI . . .
Benchmark for hybrid DBMS

Related Work
I OLTPI TPC-C & TPC-E
I OLAPI TPC-H & TPC-DS
I Composite Benchmark for Online Transaction Processing by HPII Focus: Comparing DBMS for specific use case
CH-BenCHmark

Benchmark Design
I Primary design goal: Comparability of DBMSI Merge of TPC benchmarks
I Schema: TPC-C + 3 relations of TPC-HI OLTP workload: TPC-C transactionsI OLAP workload: Adapted TPC-H queries

Benchmark Design: Schema
Warehouse
W
Stock
W 100k
District
W 10
Item
100k
Customer
W 30k
Order
W 30k+
Supplier
10k
Orderline
W 300k+
Nation
62
Neworder
W 9k+
Region
5
History
W 30k+
Computed relationships:I Stock SupplierI Customer Nation

Benchmark Design: Schema
Warehouse
W
Stock
W 100k
District
W 10
Item
100k
Customer
W 30k
Order
W 30k+
Supplier
10k
Orderline
W 300k+
Nation
62
Neworder
W 9k+
Region
5History
W 30k+
Computed relationships:I Stock SupplierI Customer Nation

Benchmark Design: OLTP Workload
I 5 TPC-C transactions:I New-Order (44%)I Payment (44%)I Order-Status (4%)I Delivery (4%)I Stock-Level (4%)
I Distribution & semantics: TPC-CI Differences to TPC-C
I No simulated terminalsI No think-time
Sto
ckLe
vel
4%
OrderStatus4% De
livery
(batch
10Or
ders)
4%
Paym
ent
44%
NewOrder(10 OrderLines)
44%

Benchmark Design: OLAP Workload
Q2
Q1
Q3
...Q20
Q21
Q22
Q(1)
......
Q(2)
Q(3)
... ...
Q(20)
Q(21)
Q(22)
I 22 TPC-H queries, adapted to schema, but with originalI Business semanticsI Syntactical structure
I Query 5 TPC-HSELECT n_name , SUM(l_extendedprice * (1 - l_discount)) AS revenueFROM customer , orders , lineitem , supplier , nation , regionWHERE c_custkey = o_custkey AND l_orderkey = o_orderkey
AND l_suppkey = s_suppkey AND c_nationkey = s_nationkeyAND s_nationkey = n_nationkey AND n_regionkey = r_regionkeyAND r_name = [REGION] AND o_orderdate >= DATE [DATE]AND o_orderdate < DATE [DATE] + INTERVAL 1 YEAR
GROUP BY n_name ORDER BY revenue DESC
I Query 5 CH-BenCHmarkSELECT n_name , SUM(ol_amount) AS revenueFROM customer , "order", orderline , stock , supplier , nation , regionWHERE c_id=o_c_id AND c_w_id=o_w_id AND c_d_id=o_d_id
AND ol_o_id=o_id AND ol_w_id=o_w_id AND ol_d_id=o_d_idAND ol_w_id=s_w_id AND ol_i_id=s_i_idAND mod(( s_w_id * s_i_id) ,10000)=su_suppkeyAND ascii(SUBSTRING(c_state , 1, 1))=su_nationkeyAND su_nationkey=n_nationkey AND n_regionkey=r_regionkeyAND r_name=[REGION] AND o_entry_d >=[DATE]
GROUP BY n_name ORDER BY revenue DESC

Benchmark Parameters
StockLevel
4%
OrderStatus4% De
livery
(batch10Orders)
4%
Paym
ent
44%
OLAP Workload(n 1 parallel Query Streams)
OLTP Workload
NewOrder(10 OrderLines)
44%Q2
Q1
Q3
...Q20
Q21
Q22
Q(1)
......
Q(2)
Q(3)
... ...
Q(20)
Q(21)
Q(22)
I Size: Number of warehousesI OLTP sessions: Random TPC-C transactionsI OLAP sessions: 22 TPC-H queriesI Isolation levelI Data freshness

The contestants
I System X: Universal database systemI Popular, commercial DBI Disk-based
I MonetDB: OLAP-focusedI In-memory column storeI 10-year Best Paper Award
(VLDB 2009)I VoltDB: OLTP-focused
I H-Store (Stonebraker) successorI In-memoryI Private partitions with
serial executionI Java stored procedures
I HyPer: Hybrid OLTP & OLAP OLA
P Pe
rform
ance
OLTP Performance
+-
+-
CHBenCHmark
DB2,Oracle 11g,SQL Server
VoltDB, TimesTen
MonetDB,VectorWise,
T-REX (BWA),ISAO (BLINK)
HyPer,SAP NewDB
(SanssouciDB)
TPC-CTP
C-H

The contestants: HyPerI Main-memory hybrid OLTP&OLAP database system
I Kemper&Neumann @ ICDE 2011I Hardware-/OS-based snapshots to reconcile OLTP & OLAP
I Ultra-efficient shadow paging (cf. Lorie 77)I No synchronization necessary between OLAP & OLTP
c
OLTP Requests /Tx
Virtual Memory
abdc
OLAP Queries
dc
*

The contestants: HyPer
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eOLTP Process
Pages
Transactions

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OLTP Process OLAP Process
Pages
Transactions Queries
fork

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AttemptedModification

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Copy on Write: 2s

Multiple Snapshots
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OLTP Process OLAP Process A
Pages

Multiple Snapshots
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OLAP Process B

Multiple Snapshots
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OLAP Process B
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OLAP Process C

Multiple Snapshots
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OLAP Process B
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OLAP Process C
Terminate B

Multiple Snapshots
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OLAP Process C
Terminated

ExperimentsI Setup
I 2 quad-core 2.93 GHz Xeon, 64GB memory, RHEL 5.4I Benchmark size: 12 warehouses
I System XI 3 query sessionsI 25 OLTP sessions, group commit (5 transactions)
I MonetDBI 3 query sessionsI no OLTP
I VoltDBI No OLAPI 12 sitesI No partition crossing transactions
I HyPerI 3 OLAP sessions or 8 OLAP sessionsI 5 OLTP sessions or 1 OLTP session (incl. partition crossing Tx)I Snapshot taken before 1st transaction

Results System (# OLAP Sessions / # OLTP Sessions)System X (3/25) HyPer (8/1) HyPer (3/5) MonetDB (3/0) VoltDB (0/12)
NO tps 222 tps 25 166 tps 112 217 tps 16 274 tpsTotal tps 493 tps 55 924 tps 249 237 tps

Benchmarking Hybrid OLTP&OLAP Database · PDF fileI Nothink-time Benchmark Design: OLAP...

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