2. Future Database Challenges
Katarzyna Dziedziniewicz-Wojcik aa

3. Future Database Challenges
Agenda
Where are we now?
Community requirement challenges
Technical challenges
Short overview of what is the current status of DB services
08/12/2016
Future Database Challenges

4. Future Database Challenges
Agenda
Where are we now?
Community requirement challenges
Technical challenges
08/12/2016
Future Database Challenges

5. Future Database Challenges
Databases
3 main pillars
Oracle
For critical, transactional load
Administered by DBA team (CERN IT-DB)
DBoD - Database as a Service
Different database engines
MySQL, PostgreSQL, InfluxDB
Instance owners have the most of DBA rights
Hadoop
For sequential workload
I will refer to all the 3 pillars as database services.
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Future Database Challenges

6. Questions?
20/04/2016
DB Services

7. Future Database Challenges
Size more than trippled In the last 4 years.
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Future Database Challenges

8. Future Database Challenges
Agenda
Where are we now?
Community requirement challenges
Technical challenges
What is our challenge
To respond to challenges and needs stated by HEP projects by adopting our service to the needs of our users
Constant evolution
By providing consultancy to facilitate the choice of optimal solution
Your projects and needs are our challenges
The presentation will focus on examples of challenges and how we see the service evolution to respond to them
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Future Database Challenges

9. Time series workload challenge
Workload assumptions
Billions of individual data points
High write and read throughput
250k rec/s from LHC sensors
Mostly an insert/append workload
Large deletes
Multiple use-cases
Solution:
Currently Influx DB
Large scale time series solution is to be developed
LHCb Monitoring
Sensors data from LHC
And this will lead us to a long term challenge
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Future Database Challenges

10. High ingest rate challenge
Accelerator logging 2.0
Data coming from different sources
Possibly 250k records/s
Data Latency <~30sec
No data losses acceptable
Temporary data store in the ingestion layer
Provide data transformation features
Enhancing by adding some context
Filtering
Different data sources Stores acquisition device/property data
Mainly NUMERIC timeseries, not text logs…
Temp store – to prevent data loss in case storage layer is not avaialable
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Future Database Challenges

11. Future Database Challenges
Data ingestion
Short term:
Provisioning of Kafka
Long term:
Find the best solution covering all requirements
Multitude of coming and going solutions
Assess both enterprise and open source possibilities
Make sure that APIs/interfaces are as stable as possible
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Future Database Challenges

12. High frequency query challenge
ALICE file catalog
Currently
Single instance of MySQL
Few TBs of data, ~8.5B rows
Read access rate – Hz
Run 3
10X increase in data size and insert rate
Robust solution is needed
Used to find files containing certain events in CASTOR and grid
What could be a possible solution?
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Future Database Challenges

13. Future Database Challenges
Memory evolution
NVMe
Flash over PCI express
High bandwidth at low latency
3D XPoint
Upcoming NV technology
Faster and more stable than traditional PCM
Density 4-times one of DRAM
An option for huge In-Memory databases, SPARK analysis
Imagine an event index in memory
Oracle is using flash for in-memory databases for their engineered systems. We can imagine running in-memory.
OS database using NV memory as storage.
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Future Database Challenges

14. Physics analysis in DB type of workload
Ongoing projects in experiments
CMS Big Data project
Speed up HEP analysis 100 times
Process an input sample of 1PB within 5 hours
SWAN Project
Cloud HEP data analysis
Analyse data without the need to install any software using Jupyter notebook interface Access experiments' and user data in the CERN cloud
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Future Database Challenges

15. Future Database Challenges
Support for Spark
Our goal: To expand support for Spark based physics analysis
Spark addresses use cases for machine learning at scale
Pilot in the pipeline using private cloud at CERN
collaboration with OpenStack team
Future: test public cloud?
Analyse data without the need to install any software using Jupyter notebook interface Access experiments' and user data in the CERN cloud
08/12/2016
Future Database Challenges

16. Optimizing data placement
Ongoing investigations in CMS
Intelligent dataset placement across sites
70 sites
~20 Petabytes of data produces/year
For HL-LHC
Event size x3
Event number x5
Reduction of transfer and storage costs
A base for a study on optimal job scheduling
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Future Database Challenges

17. Future Database Challenges
Machine learning
Linking machine learning with big data
Optimizing algorithms for HEP needs
library to integrate Keras + Spark
The analytics infrastructure will be evolving
Reviewing application and architecture choices on short cycles (~2 years?)
New components will are evaluated
More in Luca Canali’s talk tomorrow
Possible field of cooperation with Oracle Labs
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Future Database Challenges

18. Future Database Challenges
Agenda
Where are we now?
Community requirement challenges
Technical challenges
08/12/2016
Future Database Challenges

19. Introduction of Oracle In-Memory
Huge performance boost for full table scans
Deployment is transparent to applications
Limited impact on DML operations
Challenges:
Selection of proper workload
Requires machines with loads of memory
Already mentioned NV memory solutions will be evaluated
Impact on DML will be diminished, when 12.2 will be in place. In-mem structures saved to TBS.
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Future Database Challenges

20. High availability challenge
Ensuring disaster recovery
Increasing backup capabilities
Providing sufficient and flexible computing resource
Resource expansion for peak usage
Optimizing cost of operations
Cost: hardware, licences, manpower – needed for cloud migration vs need for on-premise support
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Future Database Challenges

21. Future Database Challenges
Cloud
One of the options
Oracle cloud is being currently tested
Key factors
Feasibility of using cloud with complex environments
Experiment’s and technical network, SSO…
Performance/latency
Effort needed for data migration
Cost
Models differ between companies and even regions
We are having a look at other options.
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Future Database Challenges

22. Changing technologies
The change is more rapid than ever
Which technologies will stay on the market?
We have to provide consistent interface to the developers community
Oracle Big Data discovery example
Background analytics engine change, but interfaces remain
Key challenge
Select proper technologies for all 3 pillars of the service
Combine commercial and open source
As you have notices I have not mentioned many technologies as solutions to our challenges.
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Future Database Challenges

23. Future Database Challenges
Summary
Data size and ingest requirements will grow
Possibly from 5bln records/day to 21bln in a few years for accelerator logging
Number and size of experiment event will also grow
Systems we plan today need to answer to future requirements
New technologies are at hand
We need to be prepared for evolving needs
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Future Database Challenges

24. Future Database Challenges
Acknowledgments
I would like to thank
The Experiments and BE department for coming up with challenges and input
Colleagues from IT-DB for solutions and help in preparing this presentation
08/12/2016
Future Database Challenges