An Approach for Hybrid-Memory Scaling Columnar In-Memory Databases
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1 An Approach for Hybrid-Memory Scaling Columnar In-Memory Databases *Bernhard Höppner, Ahmadshah Waizy, *Hannes Rauhe * SAP SE Fujitsu Technology Solutions GmbH ADMS 4 in conjunction with 4 th VLDB Hangzhou, China September, 4
2 Memory Linking Desire and Reality Cost $/GB $/GB Bandwidth GB/s MB/s μs ns Latency GB TB Capacity 4 SAP SE and Fujitsu Technology Solutions GmbH. All rights reserved.
3 Memory Linking Desire and Reality DRAM Cost HDD $/GB $/GB Bandwidth GB/s MB/s μs ns Latency GB TB Capacity 4 SAP SE and Fujitsu Technology Solutions GmbH. All rights reserved.
4 Memory Linking Desire and Reality DRAM Cost HDD $/GB $/GB Bandwidth GB/s MB/s μs ns Latency GB TB SSD 4 SAP SE and Fujitsu Technology Solutions GmbH. All rights reserved. Capacity 4
5 Memory Linking Desire and Reality DRAM Cost HDD $/GB Cheaper than DRAM Latency close to DRAM $/GB Bandwidth GB/s MB/s μs ns Latency GB Bandwidth close to DRAM TB +Non-Volatile Denser than DRAM +Byte-addressable +More energy efficient than DRAM SSD Storage Class Memory ( SCM) 4 SAP SE and Fujitsu Technology Solutions GmbH. All rights reserved. Capacity 5
6 Memory Linking Desire and Reality DRAM Cost HDD Available 8+ $/GB $/GB Cheaper than DRAM Latency close to DRAM Bandwidth GB/s MB/s μs ns Latency GB Bandwidth close to DRAM TB +Non-Volatile Denser than DRAM +Byte-addressable +More energy efficient than DRAM SSD Storage Class Memory ( SCM) 4 SAP SE and Fujitsu Technology Solutions GmbH. All rights reserved. Capacity 6
7 Memory Linking Desire and Reality DRAM Fixed amount of DRAM to buffer data from SSD $/GB Cost Offer separate memory allocator Accessed data via load/store operations HDD $/GB Bandwidth GB/s MB/s μs ns Latency SSD Hybrid-Memory PCIe SSD as backing storage 4 SAP SE and Fujitsu Technology Solutions GmbH. All rights reserved. GB TB Capacity Use paging to buffer data of SSD on DRAM Increase addressable amount of memory SCM 7
8 IMDBMS SAP HANA based Architecture Read Operations Data Modifying Operations Main Store Delta Store Dictionary Alpha Beta Charlie Delta Persistence Data Volumes Merge Log Volumes Dictionary Beta Delta Alpha Charlie Memory Storage 4 SAP SE and Fujitsu Technology Solutions GmbH. All rights reserved. 8
9 IMDBMS SAP HANA based Architecture Read Operations Data Modifying Operations Main Store Dictionary Alpha Beta Charlie Delta Persistence Data Volumes Main Memory Loaded Delta Store Column(s) Merge Limited amount of Memory Unloaded Table Data Request on Column Log Volumes Dictionary Beta Delta Alpha Charlie Memory Storage 4 SAP SE and Fujitsu Technology Solutions GmbH. All rights reserved. 9
10 IMDBMS SAP HANA based Architecture Read Operations Data Modifying Operations Main Store Dictionary Alpha Beta Charlie Delta Persistence Data Volumes Main Memory Loaded Delta Store Column(s) Merge Limited amount of Memory Unloaded Table Data Request on Column Log Volumes Dictionary Table Load Time [s] 8 Beta Delta 6 Alpha Charlie 4 Memory Storage In-Memory Table Size [GB] 4 SAP SE and Fujitsu Technology Solutions GmbH. All rights reserved.
11 IMDBMS Scaling the Traditional Way Scale-Up Increase the amount of available memory in a single server by adding DRAM Limitations: Fujitsu RX-6 S6 TB Fujitsu RX-9 S 4 TB Fujitsu 8B TB Scale-Out Add additional cooperating instances Instances are separate (shared nothing) Additional complexity and cost e.g. on the infrastructure level 4 SAP SE and Fujitsu Technology Solutions GmbH. All rights reserved.
12 IMDBMS Scaling w/ Hybrid-Memory Read Operations Data Modifying Operations Main Store Load Hybrid-Memory Loaded Column(s) On Demand Dictionary Alpha Unload Beta Charlie Delta Persistence Data Volumes Main Memory Loaded Delta Store Column(s) Merge Limited amount of Memory Unloaded Table Data Request on Column Log Volumes Dictionary Beta Delta Alpha Charlie Memory Storage 4 SAP SE and Fujitsu Technology Solutions GmbH. All rights reserved.
13 IMDBMS Scaling w/ Hybrid-Memory Read Operations Data Modifying Operations Main Store Load Hybrid-Memory Loaded Column(s) On Demand Dictionary Alpha Unload Beta Charlie Delta Persistence Data Volumes Main Memory Loaded Delta Store Column(s) Merge Limited amount of Memory Unloaded Table Data Request on Column Log Volumes Dictionary Approximated Hardware Cost [Euro] Beta 6 Delta 5 Alpha Charlie 4 IMDBMS using DRAM only IMDBMS using DRAM and Hybrid-Memory Memory Storage Database Size [TB] 4 SAP SE and Fujitsu Technology Solutions GmbH. All rights reserved.
14 IMDBMS Scaling w/ Hybrid-Memory DRAM Read Operations Data Modifying Operations SSD Hybrid- Memory Main Store Dictionary Alpha Beta Charlie Delta Persistence Data Volumes Merge Delta Store Dictionary Beta Delta Alpha Charlie Log Volumes Memory Storage 4 SAP SE and Fujitsu Technology Solutions GmbH. All rights reserved. 4
15 Hybrid-Memory Integrating DRAM SSD Hybrid- Memory Main Store Persistence Read Operations Data Volumes Delta Store Dictionary Dictionary Higher access skew Mergeon column level Beta increases the probability Alpha of cache hits Delta Beta Alpha Charlie Charlie Reduce Deltalarge range selects on Hybrid-Memory as data is Log Volumes Data Modifying Operations Hybrid-Memory is introduced on column level if data does not fully fit into DRAM Minimize latency overhead by storing less frequently used columns on Hybrid-Memory first paged in 4kB chunks Avoid scan operations as sequential operations are not beneficial on Hybrid-Memory Memory Storage 4 SAP SE and Fujitsu Technology Solutions GmbH. All rights reserved. 5
16 Hybrid-Memory Integrating While nodes in AST Generate LFU list for columns Analyze column access Not indexed Add to SeqAcc Fetch SQL Plan Cache Parse SQL Plan Cache, generate AST Get next node from AST Column used for Projection Indexed Add to Column used for Selection Add to Use existing information, do not generate additional statistics Parse SQL statements executed in the past Append SeqAcc to AccFreq Append to AccFreq Sort SeqAcc, ascending Calc. usage frequency for SeqAcc, Aggregate SeqAcc, 4 SAP SE and Fujitsu Technology Solutions GmbH. All rights reserved. 6
17 Evaluation Partitioning TPC-C 8% of all columns used in less than % of all queries Less frequently used columns cause 6% of the data volume 4 SAP SE and Fujitsu Technology Solutions GmbH. All rights reserved. 7
18 Evaluation TPC-C Hybrid-Memory 8% : DRAM % 8% of all columns on Hybrid-Memory, % on DRAM Upper touchpoint, no paging DBMS Throughput [%] % DRAM buffer reduces tpm by %, index vector size reduced by 54% TPC-C performance in tpm% compared to unchanged system DRAM buffer size for Hybrid-Memory is adjusted Hybrid-Memory Buffer Size [%] 4 SAP SE and Fujitsu Technology Solutions GmbH. All rights reserved. 8
19 Conclusion and Future Work Conclusion: DRAM + SSD combined to Hybrid-Memory Hybrid-Memory made available to applications via a separate allocator Extended SAP HANA to use Hybrid-Memory as an additional data store Analyzed TPC-C to be partitioned for two data stores Ran TPC-C based evaluation reducing the index vector size by 54% while keeping the IMDBMS performance at 88% 4 SAP SE and Fujitsu Technology Solutions GmbH. All rights reserved. 9
20 Conclusion and Future Work Future Work: Evaluate our approach based on real data and workload Integrate column partitioning directly into HANA Introduce paging to other data stores (e.g. dictionary) Design a cost model for columns to be stored on Hybrid-Memory including row level information 4 SAP SE and Fujitsu Technology Solutions GmbH. All rights reserved.
21 Thank You Questions? Comments? An Approach for Hybrid-Memory Scaling Columnar In-Memory Databases *Bernhard Höppner, Ahmadshah Waizy, *Hannes Rauhe * SAP SE Fujitsu Technology Solutions GmbH ADMS 4 in conjunction with 4 th VLDB Hangzhou, China September, 4 4 SAP SE and Fujitsu Technology Solutions GmbH. All rights reserved.
22 Hybrid-Memory Integrating While nodes in AST Analyze column access Not Indexed indexed Add to SeqAcc Fetch SQL Plan Cache Parse SQL Plan Cache, generate AST Get next node from AST Column used for Projection Not Indexed indexed Add to Column used for Selection Add to Analyze SQL Plan Cache on column level Separate into random and sequential access Maintain statistics in two independent lists Append SeqAcc to AccFreq Append to AccFreq Sort SeqAcc, ascending Calc. usage frequency for SeqAcc, Aggregate SeqAcc, 4 SAP SE and Fujitsu Technology Solutions GmbH. All rights reserved.
23 Hybrid-Memory Integrating While nodes in AST Analyze column access Not indexed Add to SeqAcc Fetch SQL Plan Cache Parse SQL Plan Cache, generate AST Get next node from AST Column used for Projection Indexed Add to Column used for Selection Add to Append SeqAcc to AccFreq Append to AccFreq Sort SeqAcc, ascending Calc. usage frequency for SeqAcc, Aggregate SeqAcc, 4 SAP SE and Fujitsu Technology Solutions GmbH. All rights reserved.
24 Hybrid-Memory Integrating While nodes in AST Analyze column access Not indexed Add to SeqAcc Fetch SQL Plan Cache Parse SQL Plan Cache, generate AST Get next node from AST Column used for Projection Indexed Add to Column used for Selection Add to Workload adapted priority list for Hybrid-Memory Add randomly accessed columns first as they are more suited for Hybrid-Memory Reduce access on Hybrid- Memory by keeping frequently used columns on DRAM Append SeqAcc to AccFreq Append to AccFreq Sort SeqAcc, ascending Calc. usage frequency for SeqAcc, Aggregate SeqAcc, 4 SAP SE and Fujitsu Technology Solutions GmbH. All rights reserved. 4
25 4 SAP SE or an SAP affiliate company. All rights reserved. No part of this publication may be reproduced or transmitted in any form or for any purpose without the express permission of SAP SE or an SAP affiliate company. SAP and other SAP products and services mentioned herein as well as their respective logos are trademarks or registered trademarks of SAP SE (or an SAP affiliate company) in Germany and other countries. Please see for additional trademark information and notices. Some software products marketed by SAP SE and its distributors contain proprietary software components of other software vendors. National product specifications may vary. These materials are provided by SAP SE or an SAP affiliate company for informational purposes only, without representation or warranty of any kind, and SAP SE or its affiliated companies shall not be liable for errors or omissions with respect to the materials. The only warranties for SAP SE or SAP affiliate company products and services are those that are set forth in the express warranty statements accompanying such products and services, if any. Nothing herein should be construed as constituting an additional warranty. In particular, SAP SE or its affiliated companies have no obligation to pursue any course of business outlined in this document or any related presentation, or to develop or release any functionality mentioned therein. This document, or any related presentation, and SAP SE s or its affiliated companies strategy and possible future developments, products, and/or platform directions and functionality are all subject to change and may be changed by SAP SE or its affiliated companies at any time for any reason without notice. The information in this document is not a commitment, promise, or legal obligation to deliver any material, code, or functionality. All forward-looking statements are subject to various risks and uncertainties that could cause actual results to differ materially from expectations. Readers are cautioned not to place undue reliance on these forward-looking statements, which speak only as of their dates, and they should not be relied upon in making purchasing decisions. 4 SAP SE and Fujitsu Technology Solutions GmbH. All rights reserved.
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