SlideShare a Scribd company logo
1 of 12
Download to read offline
Performance and Feature Comparison
on InfiniFlux, MySQL, MongoDB,
Splunk, and Elasticsearch
www.infiniflux.com
Feature - Query Language
2
Product Query Language
InfiniFlux Easy to process statistics as it supports standard SQL
MySQL Easy to process statistics as it supports standard SQL
Elasticsearch Require to enter conditional clauses of SQL queries in the REST API and JSON format
MongoDB Difficult to use it since it requires to enter conditional clauses in the “Find” method
Splunk Easy to process statistics as it supports search processing language (SPL)
Conclusion
Easy and efficient to process unstructured big data with Splunk or Elasticsearch, and
structured big data with Infiniflux which supports SQL.
For statistical processing, difficult to use Elasticsearch and MongoDB due to
complicated query.
Feature - Time Series Query
3
Product Time Series Query
InfiniFlux
Use “DURATION” clause and hidden column, “_ARRIVAL_TIME”.
Able to process high-speed query as input data are partitioned based on
input time.
MySQL Not supported
Elasticsearch Not supported
MongoDB Not supported
Splunk Possible to use “DURATION” clause for searching
For time series query, InfiniFlux and Splunk are easy and efficient to use.
In terms of search performance, InfiniFlux is the best for searching time series data.
Conclusion
Feature - Full Text Search
4
Product Full Text Search
InfiniFlux
Able to search by using “SEARCH” operator.
Able to search data at a high-speed by using the inverted index.
MySQL
Use “LIKE” operator.
It may not able to use index based on search patterns, and as a result, slow down in search
speed.
Elasticsearch Supported
MongoDB Supported
Splunk Supported
InfiniFlux is the only available DBMS for full text search at a high-speed against
structured data.
Conclusion
Feature - Extended Data Type
5
Product Extended Data Type
InfiniFlux Support IPv6 and IPv4 types, and operators (contains and contained)
MySQL Not supported
Elastic search Not supported
MongoDB Not supported
Splunk Support functions such as “cidrmatch”
Conclusion
InfiniFlux is the only available DBMS for supporting network data type and related
functions.
6
Field
Time of log
creation
Source IP
Source
port
Destination
IP
Destination
port
Protocol
type
Log text Status code Data size
Field Name arrivaltime srcip srcport dstip dstport protocol eventlog eventcode eventsize
Field Type datetime ipv4 integer ipv4 integer short
varchar
(1024)
short long
Evaluate data input and analyze performance of each product by 100,000,000 records
(13GB) on basic hardware environment
Hardware
Specifications
- CentOS 6.6
- Intel(R) Core(TM) i7-4790
CPU @ 3.60GHz (4 core)
- 32GB Memory
- SATA DISK
Test
Targets
- InfiniFlux 2.0
- MySQL 5.2
- Splunk 6.2.3
- Elasticsearch 1.5.3
- MongoDB 3.0.3
[DATA]
Performance
7
4334
13848
698
1624
389
0 2000 4000 6000 8000 10000 12000 14000 16000
Elasticsearch
MySQL
Splunk
MongoDB
INFINIFLUX
DATA LOADING TIME (sec)
Performance
Conclusion
InfiniFlux ables to load 100,000,000 data records only within 389 seconds, the fastest time ever
among the competitors.
8
3
1
85
208
4
0 50 100 150 200 250
Elasticsearch
MySQL
Splunk
MongoDB
INFINIFLUX
COMPLEX SEARCH (sec)
Performance
Conclusion
InfiniFlux only takes 4 seconds to perform composite operations.
MySQL takes a second to search, which is the fastest, however, it takes a long time to load data.
9
4337
13849
783
1832
393
Elasticsearch
MySQL
Splunk
MongoDB
INFINIFLUX
OVERALL RESULT (sec)
Performance
Conclusion
Overall, InfiniFlux ables to conduct 100,000,000 records of data input and composite operations
only within 393 seconds.
10
Performance
20.4
17.52
21.6
42.11
4.1
0 5 10 15 20 25 30 35 40 45
Elasticsearch
MySQL
Splunk
MongoDB
INFINIFLUX
STORAGE SIZE (GB)
Conclusion InfiniFlux can compress 13GB of data including indices into 4.1GB showing compression rate of
68.5%.
Source size
11
Performance
InfiniFlux MongoDB Splunk MySQL Elasticsearch
Duration (sec) 389 (00:06:29) 1624 (00:10:16) 698 (00:11:38) 13848 (03:50:48) 4334 (01:12:14)
Inserted csv size (GB) 13G
Data size (GB) 4.1G 42.1157G 8.6G 17.52G 20.4G
Compression ratio (%) 76.92%
Decompressed
(223.97%)
33.95%
Decompressed
(130.77%)
Decompressed
(156.92%)
Memory usage (%) 29.22 73.75 40.78 87.59 89.82
Memory usage (GB) 9.0756 22.9073 12.667 27.20 27.8965
Data search
Search Text (2.6M) 2s 213s (00:03:33) 424s (00:07:04) 31s 2s
IP search (2.66M) 1s 212s (00:03:32) 40s 1s 3s
Time search <1s 211s (00:03:31) 8s 1s 2s
Statistic
Sum 25s 217s (00:03:37) 435s (00:07:15) 35s 1s
Average 25s 219s (00:03:39) 436s (00:07:16) 46s 4s
Count 17s 218s (00:03:38) 382s (00:06:22) 45s 3s
Complex query 4s 208s 85s 1s 3s
OVERALL RESULT 393s 1832s 783s 13849s 4337s
*For more information on the results, please visit: http://www.infiniflux.com/performance
The World's Fastest
Time Series DBMS
for IoT and Big Data
www.infiniflux.com
info@infiniflux.com
InfiniFlux

More Related Content

What's hot

Toronto High Scalability meetup - Scaling ELK
Toronto High Scalability meetup - Scaling ELKToronto High Scalability meetup - Scaling ELK
Toronto High Scalability meetup - Scaling ELKAndrew Trossman
 
Replicate Elasticsearch Data with Cross-Cluster Replication (CCR)
Replicate Elasticsearch Data with Cross-Cluster Replication (CCR)Replicate Elasticsearch Data with Cross-Cluster Replication (CCR)
Replicate Elasticsearch Data with Cross-Cluster Replication (CCR)Elasticsearch
 
DOD 2016 - Rafał Kuć - Building a Resilient Log Aggregation Pipeline Using El...
DOD 2016 - Rafał Kuć - Building a Resilient Log Aggregation Pipeline Using El...DOD 2016 - Rafał Kuć - Building a Resilient Log Aggregation Pipeline Using El...
DOD 2016 - Rafał Kuć - Building a Resilient Log Aggregation Pipeline Using El...PROIDEA
 
Log analysis using Logstash,ElasticSearch and Kibana
Log analysis using Logstash,ElasticSearch and KibanaLog analysis using Logstash,ElasticSearch and Kibana
Log analysis using Logstash,ElasticSearch and KibanaAvinash Ramineni
 
Search Analytics with ELK (Elastic Stack)
Search Analytics with ELK (Elastic Stack)Search Analytics with ELK (Elastic Stack)
Search Analytics with ELK (Elastic Stack)MC+A
 
Lessons Learned in Deploying the ELK Stack (Elasticsearch, Logstash, and Kibana)
Lessons Learned in Deploying the ELK Stack (Elasticsearch, Logstash, and Kibana)Lessons Learned in Deploying the ELK Stack (Elasticsearch, Logstash, and Kibana)
Lessons Learned in Deploying the ELK Stack (Elasticsearch, Logstash, and Kibana)Cohesive Networks
 
What's new in MongoDB 2.6 at India event by company
What's new in MongoDB 2.6 at India event by companyWhat's new in MongoDB 2.6 at India event by company
What's new in MongoDB 2.6 at India event by companyMongoDB APAC
 
SYNCING IN JAVASCRIPT: MULTI-CLIENT COLLABORATION THROUGH DATA SHARING (Steve...
SYNCING IN JAVASCRIPT: MULTI-CLIENT COLLABORATION THROUGH DATA SHARING (Steve...SYNCING IN JAVASCRIPT: MULTI-CLIENT COLLABORATION THROUGH DATA SHARING (Steve...
SYNCING IN JAVASCRIPT: MULTI-CLIENT COLLABORATION THROUGH DATA SHARING (Steve...Future Insights
 
Using Elastic to Monitor Everything - Christoph Wurm, Elastic - DevOpsDays Te...
Using Elastic to Monitor Everything - Christoph Wurm, Elastic - DevOpsDays Te...Using Elastic to Monitor Everything - Christoph Wurm, Elastic - DevOpsDays Te...
Using Elastic to Monitor Everything - Christoph Wurm, Elastic - DevOpsDays Te...DevOpsDays Tel Aviv
 
Effective Searching by Dominik Kornas
Effective Searching by Dominik KornasEffective Searching by Dominik Kornas
Effective Searching by Dominik KornasAEM HUB
 
Don't change the partition count for kafka topics!
Don't change the partition count for kafka topics!Don't change the partition count for kafka topics!
Don't change the partition count for kafka topics!Dainius Jocas
 
Clickhouse at Cloudflare. By Marek Vavrusa
Clickhouse at Cloudflare. By Marek VavrusaClickhouse at Cloudflare. By Marek Vavrusa
Clickhouse at Cloudflare. By Marek VavrusaValery Tkachenko
 
213 event processingtalk-deviewkorea.key
213 event processingtalk-deviewkorea.key213 event processingtalk-deviewkorea.key
213 event processingtalk-deviewkorea.keyNAVER D2
 
Fluentd and Docker - running fluentd within a docker container
Fluentd and Docker - running fluentd within a docker containerFluentd and Docker - running fluentd within a docker container
Fluentd and Docker - running fluentd within a docker containerTreasure Data, Inc.
 
Lessons Learned While Scaling Elasticsearch at Vinted
Lessons Learned While Scaling Elasticsearch at VintedLessons Learned While Scaling Elasticsearch at Vinted
Lessons Learned While Scaling Elasticsearch at VintedDainius Jocas
 
Efficient Scalable Search in a Multi-Tenant Environment: Presented by Harry H...
Efficient Scalable Search in a Multi-Tenant Environment: Presented by Harry H...Efficient Scalable Search in a Multi-Tenant Environment: Presented by Harry H...
Efficient Scalable Search in a Multi-Tenant Environment: Presented by Harry H...Lucidworks
 
Elasticsearch Distributed search & analytics on BigData made easy
Elasticsearch Distributed search & analytics on BigData made easyElasticsearch Distributed search & analytics on BigData made easy
Elasticsearch Distributed search & analytics on BigData made easyItamar
 

What's hot (20)

Toronto High Scalability meetup - Scaling ELK
Toronto High Scalability meetup - Scaling ELKToronto High Scalability meetup - Scaling ELK
Toronto High Scalability meetup - Scaling ELK
 
Replicate Elasticsearch Data with Cross-Cluster Replication (CCR)
Replicate Elasticsearch Data with Cross-Cluster Replication (CCR)Replicate Elasticsearch Data with Cross-Cluster Replication (CCR)
Replicate Elasticsearch Data with Cross-Cluster Replication (CCR)
 
Log analytics with ELK stack
Log analytics with ELK stackLog analytics with ELK stack
Log analytics with ELK stack
 
DOD 2016 - Rafał Kuć - Building a Resilient Log Aggregation Pipeline Using El...
DOD 2016 - Rafał Kuć - Building a Resilient Log Aggregation Pipeline Using El...DOD 2016 - Rafał Kuć - Building a Resilient Log Aggregation Pipeline Using El...
DOD 2016 - Rafał Kuć - Building a Resilient Log Aggregation Pipeline Using El...
 
Log analysis using Logstash,ElasticSearch and Kibana
Log analysis using Logstash,ElasticSearch and KibanaLog analysis using Logstash,ElasticSearch and Kibana
Log analysis using Logstash,ElasticSearch and Kibana
 
Search Analytics with ELK (Elastic Stack)
Search Analytics with ELK (Elastic Stack)Search Analytics with ELK (Elastic Stack)
Search Analytics with ELK (Elastic Stack)
 
ELK introduction
ELK introductionELK introduction
ELK introduction
 
Lessons Learned in Deploying the ELK Stack (Elasticsearch, Logstash, and Kibana)
Lessons Learned in Deploying the ELK Stack (Elasticsearch, Logstash, and Kibana)Lessons Learned in Deploying the ELK Stack (Elasticsearch, Logstash, and Kibana)
Lessons Learned in Deploying the ELK Stack (Elasticsearch, Logstash, and Kibana)
 
What's new in MongoDB 2.6 at India event by company
What's new in MongoDB 2.6 at India event by companyWhat's new in MongoDB 2.6 at India event by company
What's new in MongoDB 2.6 at India event by company
 
SYNCING IN JAVASCRIPT: MULTI-CLIENT COLLABORATION THROUGH DATA SHARING (Steve...
SYNCING IN JAVASCRIPT: MULTI-CLIENT COLLABORATION THROUGH DATA SHARING (Steve...SYNCING IN JAVASCRIPT: MULTI-CLIENT COLLABORATION THROUGH DATA SHARING (Steve...
SYNCING IN JAVASCRIPT: MULTI-CLIENT COLLABORATION THROUGH DATA SHARING (Steve...
 
Using Elastic to Monitor Everything - Christoph Wurm, Elastic - DevOpsDays Te...
Using Elastic to Monitor Everything - Christoph Wurm, Elastic - DevOpsDays Te...Using Elastic to Monitor Everything - Christoph Wurm, Elastic - DevOpsDays Te...
Using Elastic to Monitor Everything - Christoph Wurm, Elastic - DevOpsDays Te...
 
Effective Searching by Dominik Kornas
Effective Searching by Dominik KornasEffective Searching by Dominik Kornas
Effective Searching by Dominik Kornas
 
Don't change the partition count for kafka topics!
Don't change the partition count for kafka topics!Don't change the partition count for kafka topics!
Don't change the partition count for kafka topics!
 
Clickhouse at Cloudflare. By Marek Vavrusa
Clickhouse at Cloudflare. By Marek VavrusaClickhouse at Cloudflare. By Marek Vavrusa
Clickhouse at Cloudflare. By Marek Vavrusa
 
213 event processingtalk-deviewkorea.key
213 event processingtalk-deviewkorea.key213 event processingtalk-deviewkorea.key
213 event processingtalk-deviewkorea.key
 
Fluentd and Docker - running fluentd within a docker container
Fluentd and Docker - running fluentd within a docker containerFluentd and Docker - running fluentd within a docker container
Fluentd and Docker - running fluentd within a docker container
 
Elk
Elk Elk
Elk
 
Lessons Learned While Scaling Elasticsearch at Vinted
Lessons Learned While Scaling Elasticsearch at VintedLessons Learned While Scaling Elasticsearch at Vinted
Lessons Learned While Scaling Elasticsearch at Vinted
 
Efficient Scalable Search in a Multi-Tenant Environment: Presented by Harry H...
Efficient Scalable Search in a Multi-Tenant Environment: Presented by Harry H...Efficient Scalable Search in a Multi-Tenant Environment: Presented by Harry H...
Efficient Scalable Search in a Multi-Tenant Environment: Presented by Harry H...
 
Elasticsearch Distributed search & analytics on BigData made easy
Elasticsearch Distributed search & analytics on BigData made easyElasticsearch Distributed search & analytics on BigData made easy
Elasticsearch Distributed search & analytics on BigData made easy
 

Similar to The World's Fastest Time Series DBMS for IoT and Big Data

InfiniFlux Feature perf comp_v1
InfiniFlux Feature perf comp_v1InfiniFlux Feature perf comp_v1
InfiniFlux Feature perf comp_v1InfiniFlux
 
MySQL 5.6 - Operations and Diagnostics Improvements
MySQL 5.6 - Operations and Diagnostics ImprovementsMySQL 5.6 - Operations and Diagnostics Improvements
MySQL 5.6 - Operations and Diagnostics ImprovementsMorgan Tocker
 
REST Easy with Django-Rest-Framework
REST Easy with Django-Rest-FrameworkREST Easy with Django-Rest-Framework
REST Easy with Django-Rest-FrameworkMarcel Chastain
 
Why Wordnik went non-relational
Why Wordnik went non-relationalWhy Wordnik went non-relational
Why Wordnik went non-relationalTony Tam
 
MySQL Manchester TT - 5.7 Whats new
MySQL Manchester TT - 5.7 Whats newMySQL Manchester TT - 5.7 Whats new
MySQL Manchester TT - 5.7 Whats newMark Swarbrick
 
Collabnix Online Webinar: Integrated Log Analytics & Monitoring using Docker ...
Collabnix Online Webinar: Integrated Log Analytics & Monitoring using Docker ...Collabnix Online Webinar: Integrated Log Analytics & Monitoring using Docker ...
Collabnix Online Webinar: Integrated Log Analytics & Monitoring using Docker ...Ajeet Singh Raina
 
InfiniFlux Time Series DBMS FAQ
InfiniFlux Time Series DBMS FAQInfiniFlux Time Series DBMS FAQ
InfiniFlux Time Series DBMS FAQInfiniFlux
 
What’s new in WSO2 Enterprise Integrator 6.6
What’s new in WSO2 Enterprise Integrator 6.6What’s new in WSO2 Enterprise Integrator 6.6
What’s new in WSO2 Enterprise Integrator 6.6WSO2
 
Final Presentation IRT - Jingxuan Wei V1.2
Final Presentation  IRT - Jingxuan Wei V1.2Final Presentation  IRT - Jingxuan Wei V1.2
Final Presentation IRT - Jingxuan Wei V1.2JINGXUAN WEI
 
2015 03-16-elk at-bsides
2015 03-16-elk at-bsides2015 03-16-elk at-bsides
2015 03-16-elk at-bsidesJeremy Cohoe
 
The Apache Spark config behind the indsutry's first 100TB Spark SQL benchmark
The Apache Spark config behind the indsutry's first 100TB Spark SQL benchmarkThe Apache Spark config behind the indsutry's first 100TB Spark SQL benchmark
The Apache Spark config behind the indsutry's first 100TB Spark SQL benchmarkLenovo Data Center
 
DIY Netflow Data Analytic with ELK Stack by CL Lee
DIY Netflow Data Analytic with ELK Stack by CL LeeDIY Netflow Data Analytic with ELK Stack by CL Lee
DIY Netflow Data Analytic with ELK Stack by CL LeeMyNOG
 
The Impact of Columnar File Formats on SQL-on-Hadoop Engine Performance: A St...
The Impact of Columnar File Formats on SQL-on-Hadoop Engine Performance: A St...The Impact of Columnar File Formats on SQL-on-Hadoop Engine Performance: A St...
The Impact of Columnar File Formats on SQL-on-Hadoop Engine Performance: A St...t_ivanov
 
Innovations of .NET and Azure (Recaps of Build 2017 selected sessions)
Innovations of .NET and Azure (Recaps of Build 2017 selected sessions)Innovations of .NET and Azure (Recaps of Build 2017 selected sessions)
Innovations of .NET and Azure (Recaps of Build 2017 selected sessions)Jeff Chu
 
Neo4j Vision and Roadmap
Neo4j Vision and Roadmap Neo4j Vision and Roadmap
Neo4j Vision and Roadmap Neo4j
 
Centralized Logging System Using ELK Stack
Centralized Logging System Using ELK StackCentralized Logging System Using ELK Stack
Centralized Logging System Using ELK StackRohit Sharma
 
The Adventure: BlackRay as a Storage Engine
The Adventure: BlackRay as a Storage EngineThe Adventure: BlackRay as a Storage Engine
The Adventure: BlackRay as a Storage Enginefschupp
 

Similar to The World's Fastest Time Series DBMS for IoT and Big Data (20)

InfiniFlux Feature perf comp_v1
InfiniFlux Feature perf comp_v1InfiniFlux Feature perf comp_v1
InfiniFlux Feature perf comp_v1
 
MySQL 5.6 - Operations and Diagnostics Improvements
MySQL 5.6 - Operations and Diagnostics ImprovementsMySQL 5.6 - Operations and Diagnostics Improvements
MySQL 5.6 - Operations and Diagnostics Improvements
 
REST Easy with Django-Rest-Framework
REST Easy with Django-Rest-FrameworkREST Easy with Django-Rest-Framework
REST Easy with Django-Rest-Framework
 
Why Wordnik went non-relational
Why Wordnik went non-relationalWhy Wordnik went non-relational
Why Wordnik went non-relational
 
MySQL Manchester TT - 5.7 Whats new
MySQL Manchester TT - 5.7 Whats newMySQL Manchester TT - 5.7 Whats new
MySQL Manchester TT - 5.7 Whats new
 
Collabnix Online Webinar: Integrated Log Analytics & Monitoring using Docker ...
Collabnix Online Webinar: Integrated Log Analytics & Monitoring using Docker ...Collabnix Online Webinar: Integrated Log Analytics & Monitoring using Docker ...
Collabnix Online Webinar: Integrated Log Analytics & Monitoring using Docker ...
 
InfiniFlux Time Series DBMS FAQ
InfiniFlux Time Series DBMS FAQInfiniFlux Time Series DBMS FAQ
InfiniFlux Time Series DBMS FAQ
 
What’s new in WSO2 Enterprise Integrator 6.6
What’s new in WSO2 Enterprise Integrator 6.6What’s new in WSO2 Enterprise Integrator 6.6
What’s new in WSO2 Enterprise Integrator 6.6
 
Final Presentation IRT - Jingxuan Wei V1.2
Final Presentation  IRT - Jingxuan Wei V1.2Final Presentation  IRT - Jingxuan Wei V1.2
Final Presentation IRT - Jingxuan Wei V1.2
 
2015 03-16-elk at-bsides
2015 03-16-elk at-bsides2015 03-16-elk at-bsides
2015 03-16-elk at-bsides
 
The Apache Spark config behind the indsutry's first 100TB Spark SQL benchmark
The Apache Spark config behind the indsutry's first 100TB Spark SQL benchmarkThe Apache Spark config behind the indsutry's first 100TB Spark SQL benchmark
The Apache Spark config behind the indsutry's first 100TB Spark SQL benchmark
 
DIY Netflow Data Analytic with ELK Stack by CL Lee
DIY Netflow Data Analytic with ELK Stack by CL LeeDIY Netflow Data Analytic with ELK Stack by CL Lee
DIY Netflow Data Analytic with ELK Stack by CL Lee
 
The Impact of Columnar File Formats on SQL-on-Hadoop Engine Performance: A St...
The Impact of Columnar File Formats on SQL-on-Hadoop Engine Performance: A St...The Impact of Columnar File Formats on SQL-on-Hadoop Engine Performance: A St...
The Impact of Columnar File Formats on SQL-on-Hadoop Engine Performance: A St...
 
Innovations of .NET and Azure (Recaps of Build 2017 selected sessions)
Innovations of .NET and Azure (Recaps of Build 2017 selected sessions)Innovations of .NET and Azure (Recaps of Build 2017 selected sessions)
Innovations of .NET and Azure (Recaps of Build 2017 selected sessions)
 
Using the Splunk Java SDK
Using the Splunk Java SDKUsing the Splunk Java SDK
Using the Splunk Java SDK
 
Neo4j Vision and Roadmap
Neo4j Vision and Roadmap Neo4j Vision and Roadmap
Neo4j Vision and Roadmap
 
Splunk Developer Platform
Splunk Developer PlatformSplunk Developer Platform
Splunk Developer Platform
 
Oracle
OracleOracle
Oracle
 
Centralized Logging System Using ELK Stack
Centralized Logging System Using ELK StackCentralized Logging System Using ELK Stack
Centralized Logging System Using ELK Stack
 
The Adventure: BlackRay as a Storage Engine
The Adventure: BlackRay as a Storage EngineThe Adventure: BlackRay as a Storage Engine
The Adventure: BlackRay as a Storage Engine
 

More from InfiniFlux

InfiniFlux IP Address Type
InfiniFlux IP Address TypeInfiniFlux IP Address Type
InfiniFlux IP Address TypeInfiniFlux
 
InfiniFlux duration
InfiniFlux durationInfiniFlux duration
InfiniFlux durationInfiniFlux
 
InfiniFlux Minmax Cache
InfiniFlux Minmax CacheInfiniFlux Minmax Cache
InfiniFlux Minmax CacheInfiniFlux
 
InfiniFlux performance
InfiniFlux performanceInfiniFlux performance
InfiniFlux performanceInfiniFlux
 
InfiniFlux vs_RDBMS
InfiniFlux vs_RDBMSInfiniFlux vs_RDBMS
InfiniFlux vs_RDBMSInfiniFlux
 
InfiniFlux Backup
InfiniFlux BackupInfiniFlux Backup
InfiniFlux BackupInfiniFlux
 
InfiniFlux collector
InfiniFlux collectorInfiniFlux collector
InfiniFlux collectorInfiniFlux
 

More from InfiniFlux (7)

InfiniFlux IP Address Type
InfiniFlux IP Address TypeInfiniFlux IP Address Type
InfiniFlux IP Address Type
 
InfiniFlux duration
InfiniFlux durationInfiniFlux duration
InfiniFlux duration
 
InfiniFlux Minmax Cache
InfiniFlux Minmax CacheInfiniFlux Minmax Cache
InfiniFlux Minmax Cache
 
InfiniFlux performance
InfiniFlux performanceInfiniFlux performance
InfiniFlux performance
 
InfiniFlux vs_RDBMS
InfiniFlux vs_RDBMSInfiniFlux vs_RDBMS
InfiniFlux vs_RDBMS
 
InfiniFlux Backup
InfiniFlux BackupInfiniFlux Backup
InfiniFlux Backup
 
InfiniFlux collector
InfiniFlux collectorInfiniFlux collector
InfiniFlux collector
 

Recently uploaded

Steps To Getting Up And Running Quickly With MyTimeClock Employee Scheduling ...
Steps To Getting Up And Running Quickly With MyTimeClock Employee Scheduling ...Steps To Getting Up And Running Quickly With MyTimeClock Employee Scheduling ...
Steps To Getting Up And Running Quickly With MyTimeClock Employee Scheduling ...MyIntelliSource, Inc.
 
Short Story: Unveiling the Reasoning Abilities of Large Language Models by Ke...
Short Story: Unveiling the Reasoning Abilities of Large Language Models by Ke...Short Story: Unveiling the Reasoning Abilities of Large Language Models by Ke...
Short Story: Unveiling the Reasoning Abilities of Large Language Models by Ke...kellynguyen01
 
What is Fashion PLM and Why Do You Need It
What is Fashion PLM and Why Do You Need ItWhat is Fashion PLM and Why Do You Need It
What is Fashion PLM and Why Do You Need ItWave PLM
 
What is Binary Language? Computer Number Systems
What is Binary Language?  Computer Number SystemsWhat is Binary Language?  Computer Number Systems
What is Binary Language? Computer Number SystemsJheuzeDellosa
 
The Evolution of Karaoke From Analog to App.pdf
The Evolution of Karaoke From Analog to App.pdfThe Evolution of Karaoke From Analog to App.pdf
The Evolution of Karaoke From Analog to App.pdfPower Karaoke
 
The Essentials of Digital Experience Monitoring_ A Comprehensive Guide.pdf
The Essentials of Digital Experience Monitoring_ A Comprehensive Guide.pdfThe Essentials of Digital Experience Monitoring_ A Comprehensive Guide.pdf
The Essentials of Digital Experience Monitoring_ A Comprehensive Guide.pdfkalichargn70th171
 
BATTLEFIELD ORM: TIPS, TACTICS AND STRATEGIES FOR CONQUERING YOUR DATABASE
BATTLEFIELD ORM: TIPS, TACTICS AND STRATEGIES FOR CONQUERING YOUR DATABASEBATTLEFIELD ORM: TIPS, TACTICS AND STRATEGIES FOR CONQUERING YOUR DATABASE
BATTLEFIELD ORM: TIPS, TACTICS AND STRATEGIES FOR CONQUERING YOUR DATABASEOrtus Solutions, Corp
 
Asset Management Software - Infographic
Asset Management Software - InfographicAsset Management Software - Infographic
Asset Management Software - InfographicHr365.us smith
 
buds n tech IT solutions
buds n  tech IT                solutionsbuds n  tech IT                solutions
buds n tech IT solutionsmonugehlot87
 
KnowAPIs-UnknownPerf-jaxMainz-2024 (1).pptx
KnowAPIs-UnknownPerf-jaxMainz-2024 (1).pptxKnowAPIs-UnknownPerf-jaxMainz-2024 (1).pptx
KnowAPIs-UnknownPerf-jaxMainz-2024 (1).pptxTier1 app
 
Cloud Management Software Platforms: OpenStack
Cloud Management Software Platforms: OpenStackCloud Management Software Platforms: OpenStack
Cloud Management Software Platforms: OpenStackVICTOR MAESTRE RAMIREZ
 
(Genuine) Escort Service Lucknow | Starting ₹,5K To @25k with A/C 🧑🏽‍❤️‍🧑🏻 89...
(Genuine) Escort Service Lucknow | Starting ₹,5K To @25k with A/C 🧑🏽‍❤️‍🧑🏻 89...(Genuine) Escort Service Lucknow | Starting ₹,5K To @25k with A/C 🧑🏽‍❤️‍🧑🏻 89...
(Genuine) Escort Service Lucknow | Starting ₹,5K To @25k with A/C 🧑🏽‍❤️‍🧑🏻 89...gurkirankumar98700
 
EY_Graph Database Powered Sustainability
EY_Graph Database Powered SustainabilityEY_Graph Database Powered Sustainability
EY_Graph Database Powered SustainabilityNeo4j
 
ODSC - Batch to Stream workshop - integration of Apache Spark, Cassandra, Pos...
ODSC - Batch to Stream workshop - integration of Apache Spark, Cassandra, Pos...ODSC - Batch to Stream workshop - integration of Apache Spark, Cassandra, Pos...
ODSC - Batch to Stream workshop - integration of Apache Spark, Cassandra, Pos...Christina Lin
 
Salesforce Certified Field Service Consultant
Salesforce Certified Field Service ConsultantSalesforce Certified Field Service Consultant
Salesforce Certified Field Service ConsultantAxelRicardoTrocheRiq
 
why an Opensea Clone Script might be your perfect match.pdf
why an Opensea Clone Script might be your perfect match.pdfwhy an Opensea Clone Script might be your perfect match.pdf
why an Opensea Clone Script might be your perfect match.pdfjoe51371421
 
Professional Resume Template for Software Developers
Professional Resume Template for Software DevelopersProfessional Resume Template for Software Developers
Professional Resume Template for Software DevelopersVinodh Ram
 
Building a General PDE Solving Framework with Symbolic-Numeric Scientific Mac...
Building a General PDE Solving Framework with Symbolic-Numeric Scientific Mac...Building a General PDE Solving Framework with Symbolic-Numeric Scientific Mac...
Building a General PDE Solving Framework with Symbolic-Numeric Scientific Mac...stazi3110
 
办理学位证(UQ文凭证书)昆士兰大学毕业证成绩单原版一模一样
办理学位证(UQ文凭证书)昆士兰大学毕业证成绩单原版一模一样办理学位证(UQ文凭证书)昆士兰大学毕业证成绩单原版一模一样
办理学位证(UQ文凭证书)昆士兰大学毕业证成绩单原版一模一样umasea
 

Recently uploaded (20)

Call Girls In Mukherjee Nagar 📱 9999965857 🤩 Delhi 🫦 HOT AND SEXY VVIP 🍎 SE...
Call Girls In Mukherjee Nagar 📱  9999965857  🤩 Delhi 🫦 HOT AND SEXY VVIP 🍎 SE...Call Girls In Mukherjee Nagar 📱  9999965857  🤩 Delhi 🫦 HOT AND SEXY VVIP 🍎 SE...
Call Girls In Mukherjee Nagar 📱 9999965857 🤩 Delhi 🫦 HOT AND SEXY VVIP 🍎 SE...
 
Steps To Getting Up And Running Quickly With MyTimeClock Employee Scheduling ...
Steps To Getting Up And Running Quickly With MyTimeClock Employee Scheduling ...Steps To Getting Up And Running Quickly With MyTimeClock Employee Scheduling ...
Steps To Getting Up And Running Quickly With MyTimeClock Employee Scheduling ...
 
Short Story: Unveiling the Reasoning Abilities of Large Language Models by Ke...
Short Story: Unveiling the Reasoning Abilities of Large Language Models by Ke...Short Story: Unveiling the Reasoning Abilities of Large Language Models by Ke...
Short Story: Unveiling the Reasoning Abilities of Large Language Models by Ke...
 
What is Fashion PLM and Why Do You Need It
What is Fashion PLM and Why Do You Need ItWhat is Fashion PLM and Why Do You Need It
What is Fashion PLM and Why Do You Need It
 
What is Binary Language? Computer Number Systems
What is Binary Language?  Computer Number SystemsWhat is Binary Language?  Computer Number Systems
What is Binary Language? Computer Number Systems
 
The Evolution of Karaoke From Analog to App.pdf
The Evolution of Karaoke From Analog to App.pdfThe Evolution of Karaoke From Analog to App.pdf
The Evolution of Karaoke From Analog to App.pdf
 
The Essentials of Digital Experience Monitoring_ A Comprehensive Guide.pdf
The Essentials of Digital Experience Monitoring_ A Comprehensive Guide.pdfThe Essentials of Digital Experience Monitoring_ A Comprehensive Guide.pdf
The Essentials of Digital Experience Monitoring_ A Comprehensive Guide.pdf
 
BATTLEFIELD ORM: TIPS, TACTICS AND STRATEGIES FOR CONQUERING YOUR DATABASE
BATTLEFIELD ORM: TIPS, TACTICS AND STRATEGIES FOR CONQUERING YOUR DATABASEBATTLEFIELD ORM: TIPS, TACTICS AND STRATEGIES FOR CONQUERING YOUR DATABASE
BATTLEFIELD ORM: TIPS, TACTICS AND STRATEGIES FOR CONQUERING YOUR DATABASE
 
Asset Management Software - Infographic
Asset Management Software - InfographicAsset Management Software - Infographic
Asset Management Software - Infographic
 
buds n tech IT solutions
buds n  tech IT                solutionsbuds n  tech IT                solutions
buds n tech IT solutions
 
KnowAPIs-UnknownPerf-jaxMainz-2024 (1).pptx
KnowAPIs-UnknownPerf-jaxMainz-2024 (1).pptxKnowAPIs-UnknownPerf-jaxMainz-2024 (1).pptx
KnowAPIs-UnknownPerf-jaxMainz-2024 (1).pptx
 
Cloud Management Software Platforms: OpenStack
Cloud Management Software Platforms: OpenStackCloud Management Software Platforms: OpenStack
Cloud Management Software Platforms: OpenStack
 
(Genuine) Escort Service Lucknow | Starting ₹,5K To @25k with A/C 🧑🏽‍❤️‍🧑🏻 89...
(Genuine) Escort Service Lucknow | Starting ₹,5K To @25k with A/C 🧑🏽‍❤️‍🧑🏻 89...(Genuine) Escort Service Lucknow | Starting ₹,5K To @25k with A/C 🧑🏽‍❤️‍🧑🏻 89...
(Genuine) Escort Service Lucknow | Starting ₹,5K To @25k with A/C 🧑🏽‍❤️‍🧑🏻 89...
 
EY_Graph Database Powered Sustainability
EY_Graph Database Powered SustainabilityEY_Graph Database Powered Sustainability
EY_Graph Database Powered Sustainability
 
ODSC - Batch to Stream workshop - integration of Apache Spark, Cassandra, Pos...
ODSC - Batch to Stream workshop - integration of Apache Spark, Cassandra, Pos...ODSC - Batch to Stream workshop - integration of Apache Spark, Cassandra, Pos...
ODSC - Batch to Stream workshop - integration of Apache Spark, Cassandra, Pos...
 
Salesforce Certified Field Service Consultant
Salesforce Certified Field Service ConsultantSalesforce Certified Field Service Consultant
Salesforce Certified Field Service Consultant
 
why an Opensea Clone Script might be your perfect match.pdf
why an Opensea Clone Script might be your perfect match.pdfwhy an Opensea Clone Script might be your perfect match.pdf
why an Opensea Clone Script might be your perfect match.pdf
 
Professional Resume Template for Software Developers
Professional Resume Template for Software DevelopersProfessional Resume Template for Software Developers
Professional Resume Template for Software Developers
 
Building a General PDE Solving Framework with Symbolic-Numeric Scientific Mac...
Building a General PDE Solving Framework with Symbolic-Numeric Scientific Mac...Building a General PDE Solving Framework with Symbolic-Numeric Scientific Mac...
Building a General PDE Solving Framework with Symbolic-Numeric Scientific Mac...
 
办理学位证(UQ文凭证书)昆士兰大学毕业证成绩单原版一模一样
办理学位证(UQ文凭证书)昆士兰大学毕业证成绩单原版一模一样办理学位证(UQ文凭证书)昆士兰大学毕业证成绩单原版一模一样
办理学位证(UQ文凭证书)昆士兰大学毕业证成绩单原版一模一样
 

The World's Fastest Time Series DBMS for IoT and Big Data

  • 1. Performance and Feature Comparison on InfiniFlux, MySQL, MongoDB, Splunk, and Elasticsearch www.infiniflux.com
  • 2. Feature - Query Language 2 Product Query Language InfiniFlux Easy to process statistics as it supports standard SQL MySQL Easy to process statistics as it supports standard SQL Elasticsearch Require to enter conditional clauses of SQL queries in the REST API and JSON format MongoDB Difficult to use it since it requires to enter conditional clauses in the “Find” method Splunk Easy to process statistics as it supports search processing language (SPL) Conclusion Easy and efficient to process unstructured big data with Splunk or Elasticsearch, and structured big data with Infiniflux which supports SQL. For statistical processing, difficult to use Elasticsearch and MongoDB due to complicated query.
  • 3. Feature - Time Series Query 3 Product Time Series Query InfiniFlux Use “DURATION” clause and hidden column, “_ARRIVAL_TIME”. Able to process high-speed query as input data are partitioned based on input time. MySQL Not supported Elasticsearch Not supported MongoDB Not supported Splunk Possible to use “DURATION” clause for searching For time series query, InfiniFlux and Splunk are easy and efficient to use. In terms of search performance, InfiniFlux is the best for searching time series data. Conclusion
  • 4. Feature - Full Text Search 4 Product Full Text Search InfiniFlux Able to search by using “SEARCH” operator. Able to search data at a high-speed by using the inverted index. MySQL Use “LIKE” operator. It may not able to use index based on search patterns, and as a result, slow down in search speed. Elasticsearch Supported MongoDB Supported Splunk Supported InfiniFlux is the only available DBMS for full text search at a high-speed against structured data. Conclusion
  • 5. Feature - Extended Data Type 5 Product Extended Data Type InfiniFlux Support IPv6 and IPv4 types, and operators (contains and contained) MySQL Not supported Elastic search Not supported MongoDB Not supported Splunk Support functions such as “cidrmatch” Conclusion InfiniFlux is the only available DBMS for supporting network data type and related functions.
  • 6. 6 Field Time of log creation Source IP Source port Destination IP Destination port Protocol type Log text Status code Data size Field Name arrivaltime srcip srcport dstip dstport protocol eventlog eventcode eventsize Field Type datetime ipv4 integer ipv4 integer short varchar (1024) short long Evaluate data input and analyze performance of each product by 100,000,000 records (13GB) on basic hardware environment Hardware Specifications - CentOS 6.6 - Intel(R) Core(TM) i7-4790 CPU @ 3.60GHz (4 core) - 32GB Memory - SATA DISK Test Targets - InfiniFlux 2.0 - MySQL 5.2 - Splunk 6.2.3 - Elasticsearch 1.5.3 - MongoDB 3.0.3 [DATA] Performance
  • 7. 7 4334 13848 698 1624 389 0 2000 4000 6000 8000 10000 12000 14000 16000 Elasticsearch MySQL Splunk MongoDB INFINIFLUX DATA LOADING TIME (sec) Performance Conclusion InfiniFlux ables to load 100,000,000 data records only within 389 seconds, the fastest time ever among the competitors.
  • 8. 8 3 1 85 208 4 0 50 100 150 200 250 Elasticsearch MySQL Splunk MongoDB INFINIFLUX COMPLEX SEARCH (sec) Performance Conclusion InfiniFlux only takes 4 seconds to perform composite operations. MySQL takes a second to search, which is the fastest, however, it takes a long time to load data.
  • 9. 9 4337 13849 783 1832 393 Elasticsearch MySQL Splunk MongoDB INFINIFLUX OVERALL RESULT (sec) Performance Conclusion Overall, InfiniFlux ables to conduct 100,000,000 records of data input and composite operations only within 393 seconds.
  • 10. 10 Performance 20.4 17.52 21.6 42.11 4.1 0 5 10 15 20 25 30 35 40 45 Elasticsearch MySQL Splunk MongoDB INFINIFLUX STORAGE SIZE (GB) Conclusion InfiniFlux can compress 13GB of data including indices into 4.1GB showing compression rate of 68.5%. Source size
  • 11. 11 Performance InfiniFlux MongoDB Splunk MySQL Elasticsearch Duration (sec) 389 (00:06:29) 1624 (00:10:16) 698 (00:11:38) 13848 (03:50:48) 4334 (01:12:14) Inserted csv size (GB) 13G Data size (GB) 4.1G 42.1157G 8.6G 17.52G 20.4G Compression ratio (%) 76.92% Decompressed (223.97%) 33.95% Decompressed (130.77%) Decompressed (156.92%) Memory usage (%) 29.22 73.75 40.78 87.59 89.82 Memory usage (GB) 9.0756 22.9073 12.667 27.20 27.8965 Data search Search Text (2.6M) 2s 213s (00:03:33) 424s (00:07:04) 31s 2s IP search (2.66M) 1s 212s (00:03:32) 40s 1s 3s Time search <1s 211s (00:03:31) 8s 1s 2s Statistic Sum 25s 217s (00:03:37) 435s (00:07:15) 35s 1s Average 25s 219s (00:03:39) 436s (00:07:16) 46s 4s Count 17s 218s (00:03:38) 382s (00:06:22) 45s 3s Complex query 4s 208s 85s 1s 3s OVERALL RESULT 393s 1832s 783s 13849s 4337s *For more information on the results, please visit: http://www.infiniflux.com/performance
  • 12. The World's Fastest Time Series DBMS for IoT and Big Data www.infiniflux.com info@infiniflux.com InfiniFlux