Prácticas recomendadas en materia de arquitectura y errores que debes evitar

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Crece con confianza. Desde la implementación de un pequeño nodo de desarrollo para la búsqueda de aplicaciones hasta la gestión de una gran implementación de cientos de nodos, nuestros expertos en Elastic te contarán todo lo que necesites saber.

Technology

1
Matias Cascallares
Principal Solutions Architect
Scale with confidence

2
From your laptop to
lots of nodes.
So let's go...

3
840 Pages of Elasticsearch Documentation

4
There are 840 Pages of Elasticsearch Documentation
30 minute presentation
2.142857143 seconds per page

5
Cluster Size
3
nodes
10
nodes
100’s
nodes

6
Omni-nodes
Elasticsearch
Master, Data, Ingest, Coordinating, Machine Learning, Alerting, Cross Cluster Search… Nodes
Input
Query
Response

7
Prevent Mapping Explosions
Elasticsearch Mapping
• "dynamic": true
‒ Newly detected fields are added to the mapping. (default)
• "dynamic": false
‒ Newly detected fields are silently ignored.
• "dynamic": strict
‒ If new fields are detected, an exception is thrown and the document is rejected. New fields
must be explicitly added to the mapping.
https://www.elastic.co/guide/en/elasticsearch/reference/master/dynamic.html

9
Sharding Basics
DATA NODE
DATA NODE
INDEX
SHARD 0
PRIMARY
SHARD 1
REPLICA
SHARD 1
PRIMARY
SHARD 0
REPLICA
SHARD
SE
G
M
E
N
T

10
Sharding Tips
• How many shards should I have?
‒ Well, it depends.
‒ But no more than 25 per GB of Java heap space on the data node.
• How big should a shard be?
‒ Well, it depends.
‒ But between 1-10 GB for a search-heavy use case, and 10-50 GB for a time series use
case.
‒ We are changing the default # of shards from 5 to 1 in Elasticsearch 7.0
For clusters of any size
• Shards take up CPU, RAM
• Segments take up CPU, RAM, File Handles

11
Index Operations
SHARD
SE
G
M
E
N
T
SHARD
SEGMENT
_forcemerge
SHARD
_shrink
SHARDSHARD
SHARDSHARDSHARD
SHARD
SHARD
INDEX _reindex
INDEX-ALT
INDEX

Rollups
Automatically roll up data into coarser
time buckets as it ages
- Save on storage space & costs
- Smaller indices = faster analytics
Manage rollup jobs in UI
Visualize rolled up indices
Aggregation functions:
- Avg, min, max, sum, count

14
Cluster Size
3
nodes
10
nodes
100’s
nodes

16
Dedicated Nodes & Vertical Scaling
Elasticsearch
Master Nodes
Data Nodes
Machine
Learning
Coordinating
Input
Query
Response

18
Moar Index Operations
INDEX-001
100M docs
INDEX-001
INDEX-002_rollover
_alias
INDEX
INDEX
INDEX-ALT

19
Cluster Size
3
nodes
10
nodes
100’s
nodes

20
Data Tiering
(aka Hot / Warm and more..)

21
Moar Dedicated Node Types
ElasticsearchMaster
Hot Data Warm Data
Ingest
Machine
Learning
Coordinating
Input
Query
Alerting
Response
Curator

Frozen Indices
Enable higher storage:memory ratio
Trades off search speeds for lower
memory footprint (i.e. lower costs)
Keep data searchable (online) in an cost-
efficient way
Operationally much simpler than
alternatives like snapshots or archival.
Data
Rollups
(6.3)
Hot / Warm
Snapshots
Minimal
Snapshot
(6.4)
Index
Lifecycle
Mgmt
(6.6)
Frozen
Indices
(6.6)

25
Specific Cluster Sizing
https://github.com/elastic/rally
https://www.elastic.co/webinars/using-rally-to-get-your-elasticsearch-cluster-size-right
https://www.elastic.co/blog/announcing-rally-benchmarking-for-elasticsearch

27
"Here, Hold My Beer."
Customer Stories to
Learn from As Told by
Support
https://www.elastic.co/elasticon/conf/2018/sf/here-hold-my-
beer-customer-stories-to-learn-from-as-told-by-support

28
Additional Resources
• Scaling Uber’s Elasticsearch cluster
‒ Danny Yuan about how Uber achieve:
‒ 850k to 1.3M messages/second
‒ Ingest 12TB / day
‒ Data Size: 1PB
‒ Link: https://www.infoq.com/presentations/uber-elasticsearch-clusters
• Elastic Discuss forum:
‒ Link: https://discuss.elastic.co/

29
Thank you...
Come and find us all at the
AMA booth

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It has now been four years since the beta release of Elastic Cloud Enterprise which kicked off a wave of the Elastic public sector community running Elastic as a service within Government rather than utilizing purely hosted solutions. Fast forward to 2021 and we have multiple options for multiple mission needs. Learn top tips from Elastic architects and their experience enabling their teams with the automation and provisioning of Elastic tech to change the game in how government delivers solutions.

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The presentation explores the development and application of artificial intelligence (AI) from its inception to its current status in the modern world. The term "artificial intelligence" was first coined by John McCarthy in 1956 to describe efforts to develop computer programs capable of performing tasks that typically require human intelligence. This concept was first introduced at a conference held at Dartmouth College, where programs demonstrated capabilities such as playing chess, proving theorems, and interpreting texts. In the early stages, Alan Turing contributed to the field by defining intelligence as the ability of a being to respond to certain questions intelligently, proposing what is now known as the Turing Test to evaluate the presence of intelligent behavior in machines. As the decades progressed, AI evolved significantly. The 1980s focused on machine learning, teaching computers to learn from data, leading to the development of models that could improve their performance based on their experiences. The 1990s and 2000s saw further advances in algorithms and computational power, which allowed for more sophisticated data analysis techniques, including data mining. By the 2010s, the proliferation of big data and the refinement of deep learning techniques enabled AI to become mainstream. Notable milestones included the success of Google's AlphaGo and advancements in autonomous vehicles by companies like Tesla and Waymo. A major theme of the presentation is the application of generative AI, which has been used for tasks such as natural language text generation, translation, and question answering. Generative AI uses large datasets to train models that can then produce new, coherent pieces of text or other media. The presentation also discusses the ethical implications and the need for regulation in AI, highlighting issues such as privacy, bias, and the potential for misuse. These concerns have prompted calls for comprehensive regulations to ensure the safe and equitable use of AI technologies. Artificial intelligence has also played a significant role in healthcare, particularly highlighted during the COVID-19 pandemic, where it was used in drug discovery, vaccine development, and analyzing the spread of the virus. The capabilities of AI in healthcare are vast, ranging from medical diagnostics to personalized medicine, demonstrating the technology's potential to revolutionize fields beyond just technical or consumer applications. In conclusion, AI continues to be a rapidly evolving field with significant implications for various aspects of society. The development from theoretical concepts to real-world applications illustrates both the potential benefits and the challenges that come with integrating advanced technologies into everyday life. The ongoing discussion about AI ethics and regulation underscores the importance of managing these technologies responsibly to maximize their their benefits while minimizing potential harms.

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Enterprise Knowledge’s Urmi Majumder, Principal Data Architecture Consultant, and Fernando Aguilar Islas, Senior Data Science Consultant, presented "Driving Behavioral Change for Information Management through Data-Driven Green Strategy" on March 27, 2024 at Enterprise Data World (EDW) in Orlando, Florida. In this presentation, Urmi and Fernando discussed a case study describing how the information management division in a large supply chain organization drove user behavior change through awareness of the carbon footprint of their duplicated and near-duplicated content, identified via advanced data analytics. Check out their presentation to gain valuable perspectives on utilizing data-driven strategies to influence positive behavioral shifts and support sustainability initiatives within your organization. In this session, participants gained answers to the following questions: - What is a Green Information Management (IM) Strategy, and why should you have one? - How can Artificial Intelligence (AI) and Machine Learning (ML) support your Green IM Strategy through content deduplication? - How can an organization use insights into their data to influence employee behavior for IM? - How can you reap additional benefits from content reduction that go beyond Green IM?

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This presentations targets students or working professionals. You may know Google for search, YouTube, Android, Chrome, and Gmail, but did you know Google has many developer tools, platforms & APIs? This comprehensive yet still high-level overview outlines the most impactful tools for where to run your code, store & analyze your data. It will also inspire you as to what's possible. This talk is 50 minutes in length.

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Prácticas recomendadas en materia de arquitectura y errores que debes evitar

1. 1 Matias Cascallares Principal Solutions Architect Scale with confidence

2. 2 From your laptop to lots of nodes. So let's go...

3. 3 840 Pages of Elasticsearch Documentation

4. 4 There are 840 Pages of Elasticsearch Documentation 30 minute presentation 2.142857143 seconds per page

5. 5 Cluster Size 3 nodes 10 nodes 100’s nodes

6. 6 Omni-nodes Elasticsearch Master, Data, Ingest, Coordinating, Machine Learning, Alerting, Cross Cluster Search… Nodes Input Query Response

7. 7 Prevent Mapping Explosions Elasticsearch Mapping • "dynamic": true ‒ Newly detected fields are added to the mapping. (default) • "dynamic": false ‒ Newly detected fields are silently ignored. • "dynamic": strict ‒ If new fields are detected, an exception is thrown and the document is rejected. New fields must be explicitly added to the mapping. https://www.elastic.co/guide/en/elasticsearch/reference/master/dynamic.html

8. 8 Sharding

9. 9 Sharding Basics DATA NODE DATA NODE INDEX SHARD 0 PRIMARY SHARD 1 REPLICA SHARD 1 PRIMARY SHARD 0 REPLICA SHARD SE G M E N T

10. 10 Sharding Tips • How many shards should I have? ‒ Well, it depends. ‒ But no more than 25 per GB of Java heap space on the data node. • How big should a shard be? ‒ Well, it depends. ‒ But between 1-10 GB for a search-heavy use case, and 10-50 GB for a time series use case. ‒ We are changing the default # of shards from 5 to 1 in Elasticsearch 7.0 For clusters of any size • Shards take up CPU, RAM • Segments take up CPU, RAM, File Handles

11. 11 Index Operations SHARD SE G M E N T SHARD SEGMENT _forcemerge SHARD _shrink SHARDSHARD SHARDSHARDSHARD SHARD SHARD INDEX _reindex INDEX-ALT INDEX

12. 12 Pre-aggregating Data

13. Rollups Automatically roll up data into coarser time buckets as it ages - Save on storage space & costs - Smaller indices = faster analytics Manage rollup jobs in UI Visualize rolled up indices Aggregation functions: - Avg, min, max, sum, count

14. 14 Cluster Size 3 nodes 10 nodes 100’s nodes

15. 15 Dedicated Nodes & Vertical Scaling

16. 16 Dedicated Nodes & Vertical Scaling Elasticsearch Master Nodes Data Nodes Machine Learning Coordinating Input Query Response

17. 17 Index Operations

18. 18 Moar Index Operations INDEX-001 100M docs INDEX-001 INDEX-002_rollover _alias INDEX INDEX INDEX-ALT

19. 19 Cluster Size 3 nodes 10 nodes 100’s nodes

20. 20 Data Tiering (aka Hot / Warm and more..)

21. 21 Moar Dedicated Node Types ElasticsearchMaster Hot Data Warm Data Ingest Machine Learning Coordinating Input Query Alerting Response Curator

22. 22

23. Frozen Indices Enable higher storage:memory ratio Trades off search speeds for lower memory footprint (i.e. lower costs) Keep data searchable (online) in an cost- efficient way Operationally much simpler than alternatives like snapshots or archival. Data Rollups (6.3) Hot / Warm Snapshots Minimal Snapshot (6.4) Index Lifecycle Mgmt (6.6) Frozen Indices (6.6)

24. 24 Demo

25. 25 Specific Cluster Sizing https://github.com/elastic/rally https://www.elastic.co/webinars/using-rally-to-get-your-elasticsearch-cluster-size-right https://www.elastic.co/blog/announcing-rally-benchmarking-for-elasticsearch

26. 26

27. 27 "Here, Hold My Beer." Customer Stories to Learn from As Told by Support https://www.elastic.co/elasticon/conf/2018/sf/here-hold-my- beer-customer-stories-to-learn-from-as-told-by-support

28. 28 Additional Resources • Scaling Uber’s Elasticsearch cluster ‒ Danny Yuan about how Uber achieve: ‒ 850k to 1.3M messages/second ‒ Ingest 12TB / day ‒ Data Size: 1PB ‒ Link: https://www.infoq.com/presentations/uber-elasticsearch-clusters • Elastic Discuss forum: ‒ Link: https://discuss.elastic.co/

29. 29 Thank you... Come and find us all at the AMA booth

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