SlideShare a Scribd company logo
1 of 11
Cassandra Metrics
By: Chris Lohfink
Blackbird
About Me
• Engineer at Blackbird
• Worked with C* since 0.8 (3 years)
• 7 years as a Java/Python developer
• Interests
o Data Science
o Hobbyist Electronics
o Development
Blackbird
About Cassandra
• Fault tolerant to a fault
o easy to ignore until it gets bad
• Like all other systems:
o If not many events no one pays attention to it
o If theres a lot of events need to keep eye on it
o When things happen need information to quickly diagnose
Basically...
Blackbird
Blackbird
Lots of Metrics
A lot of data but with no context or
understanding doesn’t have that
much use
… but you have lots of pretty
graphs
Blackbird
Disclaimer
This not all of the important metrics, in fact it is missing many critical ones
• Heap
• OS metrics
• Latencies
• Log messages
Blackbird
An Example for a little background
Threads
ReadStage
x32
ClientRequest
RequestResponse
231-1 231-1 Threads
ReadRepairStage
Threads
231-1
Messaging
Service
Blackbird
Cassandra Key Metrics
● Cassandra internal messaging based on SEDA with many asynchronous
elements
● Its easy to overrun the processing capabilities of a stage that is not in the
requests feedback loop (i.e. ReadRepairStage)
Blackbird
Access the metrics
● nodetool tpstats
Pool Name Active Pending Completed Blocked All time blocked
ReadStage 0 0 113702 0 0
RequestResponseStage 0 0 0 0 0
MutationStage 0 0 164503 0 0
...
InternalResponseStage 0 0 0 0 0
HintedHandoff 0 0 0 0 0
Message type Dropped
RANGE_SLICE 0
READ_REPAIR 0
...
REQUEST_RESPONSE 0
COUNTER_MUTATION 0
● JMX
org.apache.cassandra.request:type=*
and
org.apache.cassandra.internal:type=*
● Metrics Reporter
MBean Attribute tpstats name Description
ActiveCount Active Number of tasks pulled off the queue with
a Thread currently processing.
PendingTasks Pending Number of tasks in queue waiting for a
thread
CompletedTasks Completed Number of tasks completed
CurrentlyBlockedTasks Blocked When a pool reaches its core pool size
(configurable or set per stage, more
below) it will begin queuing until the max
size is reached. When this is reached it
will block until there is room in the queue.
TotalBlockedTasks All time blocked Total number of tasks that have been
blocked
Blackbird
Examples
• Read/Mutation Stage
o Too many reads/writes, disk failure, poor tuning
• ReplicateOnWrite (CounterMutationStage in 2.1+)
o High throughput of counter increments
• FlushWriter
o writes over running disk capabilities, poor tuning
o large collections
• GossipStage
o vnodes + many servers (pre 2.0.3)
Blackbird
Questions
?

More Related Content

What's hot

Prometheus for Monitoring Metrics (Fermilab 2018)
Prometheus for Monitoring Metrics (Fermilab 2018)Prometheus for Monitoring Metrics (Fermilab 2018)
Prometheus for Monitoring Metrics (Fermilab 2018)Brian Brazil
 
Teoria efectului defectului hardware: GoogleFS
Teoria efectului defectului hardware: GoogleFSTeoria efectului defectului hardware: GoogleFS
Teoria efectului defectului hardware: GoogleFSAsociatia ProLinux
 
Anatomy of an action
Anatomy of an actionAnatomy of an action
Anatomy of an actionGordon Chung
 
Concurrency Control in Distributed Database.
Concurrency Control in Distributed Database.Concurrency Control in Distributed Database.
Concurrency Control in Distributed Database.Meghaj Mallick
 
OpenTSDB: HBaseCon2017
OpenTSDB: HBaseCon2017OpenTSDB: HBaseCon2017
OpenTSDB: HBaseCon2017HBaseCon
 
Evolving Prometheus for the Cloud Native World (FOSDEM 2018)
Evolving Prometheus for the Cloud Native World (FOSDEM 2018)Evolving Prometheus for the Cloud Native World (FOSDEM 2018)
Evolving Prometheus for the Cloud Native World (FOSDEM 2018)Brian Brazil
 
Leveraging chaos mesh in Astra Serverless testing
Leveraging chaos mesh in Astra Serverless testingLeveraging chaos mesh in Astra Serverless testing
Leveraging chaos mesh in Astra Serverless testingPierre Laporte
 
An Introduction to Prometheus (GrafanaCon 2016)
An Introduction to Prometheus (GrafanaCon 2016)An Introduction to Prometheus (GrafanaCon 2016)
An Introduction to Prometheus (GrafanaCon 2016)Brian Brazil
 
Getting to Know the Cassandra Codebase
Getting to Know the Cassandra CodebaseGetting to Know the Cassandra Codebase
Getting to Know the Cassandra Codebasegdusbabek
 
Prometheus: From Berlin to Bonanza (Keynote CloudNativeCon+Kubecon Europe 2017)
Prometheus:  From Berlin to Bonanza (Keynote CloudNativeCon+Kubecon Europe 2017)Prometheus:  From Berlin to Bonanza (Keynote CloudNativeCon+Kubecon Europe 2017)
Prometheus: From Berlin to Bonanza (Keynote CloudNativeCon+Kubecon Europe 2017)Brian Brazil
 
Anatomy of a Prometheus Client Library (PromCon 2018)
Anatomy of a Prometheus Client Library (PromCon 2018)Anatomy of a Prometheus Client Library (PromCon 2018)
Anatomy of a Prometheus Client Library (PromCon 2018)Brian Brazil
 
Scala like distributed collections - dumping time-series data with apache spark
Scala like distributed collections - dumping time-series data with apache sparkScala like distributed collections - dumping time-series data with apache spark
Scala like distributed collections - dumping time-series data with apache sparkDemi Ben-Ari
 
Cassandra Codebase 2011
Cassandra Codebase 2011Cassandra Codebase 2011
Cassandra Codebase 2011gdusbabek
 
HDFS client write/read implementation details
HDFS client write/read implementation detailsHDFS client write/read implementation details
HDFS client write/read implementation detailswchevreuil
 
Monitoring NGINX (plus): key metrics and how-to
Monitoring NGINX (plus): key metrics and how-toMonitoring NGINX (plus): key metrics and how-to
Monitoring NGINX (plus): key metrics and how-toDatadog
 
Tempesta FW - Framework и Firewall для WAF и DDoS mitigation, Александр Крижа...
Tempesta FW - Framework и Firewall для WAF и DDoS mitigation, Александр Крижа...Tempesta FW - Framework и Firewall для WAF и DDoS mitigation, Александр Крижа...
Tempesta FW - Framework и Firewall для WAF и DDoS mitigation, Александр Крижа...Ontico
 
Insight DE project
Insight DE projectInsight DE project
Insight DE projectKat Chuang
 
Counting with Prometheus (CloudNativeCon+Kubecon Europe 2017)
Counting with Prometheus (CloudNativeCon+Kubecon Europe 2017)Counting with Prometheus (CloudNativeCon+Kubecon Europe 2017)
Counting with Prometheus (CloudNativeCon+Kubecon Europe 2017)Brian Brazil
 
An Introduction to Prometheus
An Introduction to PrometheusAn Introduction to Prometheus
An Introduction to PrometheusEvgeny Shmarnev
 
Prometheus for Monitoring Metrics (Percona Live Europe 2017)
Prometheus for Monitoring Metrics (Percona Live Europe 2017)Prometheus for Monitoring Metrics (Percona Live Europe 2017)
Prometheus for Monitoring Metrics (Percona Live Europe 2017)Brian Brazil
 

What's hot (20)

Prometheus for Monitoring Metrics (Fermilab 2018)
Prometheus for Monitoring Metrics (Fermilab 2018)Prometheus for Monitoring Metrics (Fermilab 2018)
Prometheus for Monitoring Metrics (Fermilab 2018)
 
Teoria efectului defectului hardware: GoogleFS
Teoria efectului defectului hardware: GoogleFSTeoria efectului defectului hardware: GoogleFS
Teoria efectului defectului hardware: GoogleFS
 
Anatomy of an action
Anatomy of an actionAnatomy of an action
Anatomy of an action
 
Concurrency Control in Distributed Database.
Concurrency Control in Distributed Database.Concurrency Control in Distributed Database.
Concurrency Control in Distributed Database.
 
OpenTSDB: HBaseCon2017
OpenTSDB: HBaseCon2017OpenTSDB: HBaseCon2017
OpenTSDB: HBaseCon2017
 
Evolving Prometheus for the Cloud Native World (FOSDEM 2018)
Evolving Prometheus for the Cloud Native World (FOSDEM 2018)Evolving Prometheus for the Cloud Native World (FOSDEM 2018)
Evolving Prometheus for the Cloud Native World (FOSDEM 2018)
 
Leveraging chaos mesh in Astra Serverless testing
Leveraging chaos mesh in Astra Serverless testingLeveraging chaos mesh in Astra Serverless testing
Leveraging chaos mesh in Astra Serverless testing
 
An Introduction to Prometheus (GrafanaCon 2016)
An Introduction to Prometheus (GrafanaCon 2016)An Introduction to Prometheus (GrafanaCon 2016)
An Introduction to Prometheus (GrafanaCon 2016)
 
Getting to Know the Cassandra Codebase
Getting to Know the Cassandra CodebaseGetting to Know the Cassandra Codebase
Getting to Know the Cassandra Codebase
 
Prometheus: From Berlin to Bonanza (Keynote CloudNativeCon+Kubecon Europe 2017)
Prometheus:  From Berlin to Bonanza (Keynote CloudNativeCon+Kubecon Europe 2017)Prometheus:  From Berlin to Bonanza (Keynote CloudNativeCon+Kubecon Europe 2017)
Prometheus: From Berlin to Bonanza (Keynote CloudNativeCon+Kubecon Europe 2017)
 
Anatomy of a Prometheus Client Library (PromCon 2018)
Anatomy of a Prometheus Client Library (PromCon 2018)Anatomy of a Prometheus Client Library (PromCon 2018)
Anatomy of a Prometheus Client Library (PromCon 2018)
 
Scala like distributed collections - dumping time-series data with apache spark
Scala like distributed collections - dumping time-series data with apache sparkScala like distributed collections - dumping time-series data with apache spark
Scala like distributed collections - dumping time-series data with apache spark
 
Cassandra Codebase 2011
Cassandra Codebase 2011Cassandra Codebase 2011
Cassandra Codebase 2011
 
HDFS client write/read implementation details
HDFS client write/read implementation detailsHDFS client write/read implementation details
HDFS client write/read implementation details
 
Monitoring NGINX (plus): key metrics and how-to
Monitoring NGINX (plus): key metrics and how-toMonitoring NGINX (plus): key metrics and how-to
Monitoring NGINX (plus): key metrics and how-to
 
Tempesta FW - Framework и Firewall для WAF и DDoS mitigation, Александр Крижа...
Tempesta FW - Framework и Firewall для WAF и DDoS mitigation, Александр Крижа...Tempesta FW - Framework и Firewall для WAF и DDoS mitigation, Александр Крижа...
Tempesta FW - Framework и Firewall для WAF и DDoS mitigation, Александр Крижа...
 
Insight DE project
Insight DE projectInsight DE project
Insight DE project
 
Counting with Prometheus (CloudNativeCon+Kubecon Europe 2017)
Counting with Prometheus (CloudNativeCon+Kubecon Europe 2017)Counting with Prometheus (CloudNativeCon+Kubecon Europe 2017)
Counting with Prometheus (CloudNativeCon+Kubecon Europe 2017)
 
An Introduction to Prometheus
An Introduction to PrometheusAn Introduction to Prometheus
An Introduction to Prometheus
 
Prometheus for Monitoring Metrics (Percona Live Europe 2017)
Prometheus for Monitoring Metrics (Percona Live Europe 2017)Prometheus for Monitoring Metrics (Percona Live Europe 2017)
Prometheus for Monitoring Metrics (Percona Live Europe 2017)
 

Similar to Metrics lightning talk

Cassandra Summit 2014: Monitor Everything!
Cassandra Summit 2014: Monitor Everything!Cassandra Summit 2014: Monitor Everything!
Cassandra Summit 2014: Monitor Everything!DataStax Academy
 
Deployment Preparedness
Deployment Preparedness Deployment Preparedness
Deployment Preparedness MongoDB
 
S3, Cassandra or Outer Space? Dumping Time Series Data using Spark - Demi Ben...
S3, Cassandra or Outer Space? Dumping Time Series Data using Spark - Demi Ben...S3, Cassandra or Outer Space? Dumping Time Series Data using Spark - Demi Ben...
S3, Cassandra or Outer Space? Dumping Time Series Data using Spark - Demi Ben...Codemotion Tel Aviv
 
Presto At Treasure Data
Presto At Treasure DataPresto At Treasure Data
Presto At Treasure DataTaro L. Saito
 
Cassandra To Infinity And Beyond
Cassandra To Infinity And BeyondCassandra To Infinity And Beyond
Cassandra To Infinity And BeyondRomain Hardouin
 
S3, Cassandra or Outer Space? Dumping Time Series Data using Spark - Demi Be...
S3, Cassandra or Outer Space? Dumping Time Series Data using Spark  - Demi Be...S3, Cassandra or Outer Space? Dumping Time Series Data using Spark  - Demi Be...
S3, Cassandra or Outer Space? Dumping Time Series Data using Spark - Demi Be...Codemotion
 
Tutorial: Network State Awareness Troubleshooting
Tutorial: Network State Awareness TroubleshootingTutorial: Network State Awareness Troubleshooting
Tutorial: Network State Awareness TroubleshootingAPNIC
 
Myths of Big Partitions (Robert Stupp, DataStax) | Cassandra Summit 2016
Myths of Big Partitions (Robert Stupp, DataStax) | Cassandra Summit 2016Myths of Big Partitions (Robert Stupp, DataStax) | Cassandra Summit 2016
Myths of Big Partitions (Robert Stupp, DataStax) | Cassandra Summit 2016DataStax
 
Neo4j after 1 year in production
Neo4j after 1 year in productionNeo4j after 1 year in production
Neo4j after 1 year in productionAndrew Nikishaev
 
Managing your Black Friday Logs - Antonio Bonuccelli - Codemotion Rome 2018
Managing your Black Friday Logs - Antonio Bonuccelli - Codemotion Rome 2018Managing your Black Friday Logs - Antonio Bonuccelli - Codemotion Rome 2018
Managing your Black Friday Logs - Antonio Bonuccelli - Codemotion Rome 2018Codemotion
 
Apache Tajo on Swift: Bringing SQL to the OpenStack World
Apache Tajo on Swift: Bringing SQL to the OpenStack WorldApache Tajo on Swift: Bringing SQL to the OpenStack World
Apache Tajo on Swift: Bringing SQL to the OpenStack WorldJihoon Son
 
Building Big Data Streaming Architectures
Building Big Data Streaming ArchitecturesBuilding Big Data Streaming Architectures
Building Big Data Streaming ArchitecturesDavid Martínez Rego
 
(WEB401) Optimizing Your Web Server on AWS | AWS re:Invent 2014
(WEB401) Optimizing Your Web Server on AWS | AWS re:Invent 2014(WEB401) Optimizing Your Web Server on AWS | AWS re:Invent 2014
(WEB401) Optimizing Your Web Server on AWS | AWS re:Invent 2014Amazon Web Services
 
How to Make Norikra Perfect
How to Make Norikra PerfectHow to Make Norikra Perfect
How to Make Norikra PerfectSATOSHI TAGOMORI
 
Kubernetes @ Squarespace (SRE Portland Meetup October 2017)
Kubernetes @ Squarespace (SRE Portland Meetup October 2017)Kubernetes @ Squarespace (SRE Portland Meetup October 2017)
Kubernetes @ Squarespace (SRE Portland Meetup October 2017)Kevin Lynch
 
Performance and predictability (1)
Performance and predictability (1)Performance and predictability (1)
Performance and predictability (1)RichardWarburton
 
Performance and Predictability - Richard Warburton
Performance and Predictability - Richard WarburtonPerformance and Predictability - Richard Warburton
Performance and Predictability - Richard WarburtonJAXLondon2014
 
Kafka Connect: Operational Lessons Learned from the Trenches (Elizabeth Benne...
Kafka Connect: Operational Lessons Learned from the Trenches (Elizabeth Benne...Kafka Connect: Operational Lessons Learned from the Trenches (Elizabeth Benne...
Kafka Connect: Operational Lessons Learned from the Trenches (Elizabeth Benne...confluent
 

Similar to Metrics lightning talk (20)

Cassandra Summit 2014: Monitor Everything!
Cassandra Summit 2014: Monitor Everything!Cassandra Summit 2014: Monitor Everything!
Cassandra Summit 2014: Monitor Everything!
 
Cassandra Metrics
Cassandra MetricsCassandra Metrics
Cassandra Metrics
 
Deployment Preparedness
Deployment Preparedness Deployment Preparedness
Deployment Preparedness
 
S3, Cassandra or Outer Space? Dumping Time Series Data using Spark - Demi Ben...
S3, Cassandra or Outer Space? Dumping Time Series Data using Spark - Demi Ben...S3, Cassandra or Outer Space? Dumping Time Series Data using Spark - Demi Ben...
S3, Cassandra or Outer Space? Dumping Time Series Data using Spark - Demi Ben...
 
Presto At Treasure Data
Presto At Treasure DataPresto At Treasure Data
Presto At Treasure Data
 
Cassandra To Infinity And Beyond
Cassandra To Infinity And BeyondCassandra To Infinity And Beyond
Cassandra To Infinity And Beyond
 
S3, Cassandra or Outer Space? Dumping Time Series Data using Spark - Demi Be...
S3, Cassandra or Outer Space? Dumping Time Series Data using Spark  - Demi Be...S3, Cassandra or Outer Space? Dumping Time Series Data using Spark  - Demi Be...
S3, Cassandra or Outer Space? Dumping Time Series Data using Spark - Demi Be...
 
Tutorial: Network State Awareness Troubleshooting
Tutorial: Network State Awareness TroubleshootingTutorial: Network State Awareness Troubleshooting
Tutorial: Network State Awareness Troubleshooting
 
Myths of Big Partitions (Robert Stupp, DataStax) | Cassandra Summit 2016
Myths of Big Partitions (Robert Stupp, DataStax) | Cassandra Summit 2016Myths of Big Partitions (Robert Stupp, DataStax) | Cassandra Summit 2016
Myths of Big Partitions (Robert Stupp, DataStax) | Cassandra Summit 2016
 
Neo4j after 1 year in production
Neo4j after 1 year in productionNeo4j after 1 year in production
Neo4j after 1 year in production
 
Managing your Black Friday Logs - Antonio Bonuccelli - Codemotion Rome 2018
Managing your Black Friday Logs - Antonio Bonuccelli - Codemotion Rome 2018Managing your Black Friday Logs - Antonio Bonuccelli - Codemotion Rome 2018
Managing your Black Friday Logs - Antonio Bonuccelli - Codemotion Rome 2018
 
Apache Tajo on Swift: Bringing SQL to the OpenStack World
Apache Tajo on Swift: Bringing SQL to the OpenStack WorldApache Tajo on Swift: Bringing SQL to the OpenStack World
Apache Tajo on Swift: Bringing SQL to the OpenStack World
 
Building Big Data Streaming Architectures
Building Big Data Streaming ArchitecturesBuilding Big Data Streaming Architectures
Building Big Data Streaming Architectures
 
(WEB401) Optimizing Your Web Server on AWS | AWS re:Invent 2014
(WEB401) Optimizing Your Web Server on AWS | AWS re:Invent 2014(WEB401) Optimizing Your Web Server on AWS | AWS re:Invent 2014
(WEB401) Optimizing Your Web Server on AWS | AWS re:Invent 2014
 
cb streams - gavin pickin
cb streams - gavin pickincb streams - gavin pickin
cb streams - gavin pickin
 
How to Make Norikra Perfect
How to Make Norikra PerfectHow to Make Norikra Perfect
How to Make Norikra Perfect
 
Kubernetes @ Squarespace (SRE Portland Meetup October 2017)
Kubernetes @ Squarespace (SRE Portland Meetup October 2017)Kubernetes @ Squarespace (SRE Portland Meetup October 2017)
Kubernetes @ Squarespace (SRE Portland Meetup October 2017)
 
Performance and predictability (1)
Performance and predictability (1)Performance and predictability (1)
Performance and predictability (1)
 
Performance and Predictability - Richard Warburton
Performance and Predictability - Richard WarburtonPerformance and Predictability - Richard Warburton
Performance and Predictability - Richard Warburton
 
Kafka Connect: Operational Lessons Learned from the Trenches (Elizabeth Benne...
Kafka Connect: Operational Lessons Learned from the Trenches (Elizabeth Benne...Kafka Connect: Operational Lessons Learned from the Trenches (Elizabeth Benne...
Kafka Connect: Operational Lessons Learned from the Trenches (Elizabeth Benne...
 

Recently uploaded

Vector Search -An Introduction in Oracle Database 23ai.pptx
Vector Search -An Introduction in Oracle Database 23ai.pptxVector Search -An Introduction in Oracle Database 23ai.pptx
Vector Search -An Introduction in Oracle Database 23ai.pptxRemote DBA Services
 
MS Copilot expands with MS Graph connectors
MS Copilot expands with MS Graph connectorsMS Copilot expands with MS Graph connectors
MS Copilot expands with MS Graph connectorsNanddeep Nachan
 
Corporate and higher education May webinar.pptx
Corporate and higher education May webinar.pptxCorporate and higher education May webinar.pptx
Corporate and higher education May webinar.pptxRustici Software
 
Apidays New York 2024 - Passkeys: Developing APIs to enable passwordless auth...
Apidays New York 2024 - Passkeys: Developing APIs to enable passwordless auth...Apidays New York 2024 - Passkeys: Developing APIs to enable passwordless auth...
Apidays New York 2024 - Passkeys: Developing APIs to enable passwordless auth...apidays
 
Polkadot JAM Slides - Token2049 - By Dr. Gavin Wood
Polkadot JAM Slides - Token2049 - By Dr. Gavin WoodPolkadot JAM Slides - Token2049 - By Dr. Gavin Wood
Polkadot JAM Slides - Token2049 - By Dr. Gavin WoodJuan lago vázquez
 
EMPOWERMENT TECHNOLOGY GRADE 11 QUARTER 2 REVIEWER
EMPOWERMENT TECHNOLOGY GRADE 11 QUARTER 2 REVIEWEREMPOWERMENT TECHNOLOGY GRADE 11 QUARTER 2 REVIEWER
EMPOWERMENT TECHNOLOGY GRADE 11 QUARTER 2 REVIEWERMadyBayot
 
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers:  A Deep Dive into Serverless Spatial Data and FMECloud Frontiers:  A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FMESafe Software
 
Artificial Intelligence Chap.5 : Uncertainty
Artificial Intelligence Chap.5 : UncertaintyArtificial Intelligence Chap.5 : Uncertainty
Artificial Intelligence Chap.5 : UncertaintyKhushali Kathiriya
 
DEV meet-up UiPath Document Understanding May 7 2024 Amsterdam
DEV meet-up UiPath Document Understanding May 7 2024 AmsterdamDEV meet-up UiPath Document Understanding May 7 2024 Amsterdam
DEV meet-up UiPath Document Understanding May 7 2024 AmsterdamUiPathCommunity
 
How to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected WorkerHow to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected WorkerThousandEyes
 
ICT role in 21st century education and its challenges
ICT role in 21st century education and its challengesICT role in 21st century education and its challenges
ICT role in 21st century education and its challengesrafiqahmad00786416
 
presentation ICT roal in 21st century education
presentation ICT roal in 21st century educationpresentation ICT roal in 21st century education
presentation ICT roal in 21st century educationjfdjdjcjdnsjd
 
Architecting Cloud Native Applications
Architecting Cloud Native ApplicationsArchitecting Cloud Native Applications
Architecting Cloud Native ApplicationsWSO2
 
Exploring Multimodal Embeddings with Milvus
Exploring Multimodal Embeddings with MilvusExploring Multimodal Embeddings with Milvus
Exploring Multimodal Embeddings with MilvusZilliz
 
Connector Corner: Accelerate revenue generation using UiPath API-centric busi...
Connector Corner: Accelerate revenue generation using UiPath API-centric busi...Connector Corner: Accelerate revenue generation using UiPath API-centric busi...
Connector Corner: Accelerate revenue generation using UiPath API-centric busi...DianaGray10
 
Repurposing LNG terminals for Hydrogen Ammonia: Feasibility and Cost Saving
Repurposing LNG terminals for Hydrogen Ammonia: Feasibility and Cost SavingRepurposing LNG terminals for Hydrogen Ammonia: Feasibility and Cost Saving
Repurposing LNG terminals for Hydrogen Ammonia: Feasibility and Cost SavingEdi Saputra
 
Apidays New York 2024 - APIs in 2030: The Risk of Technological Sleepwalk by ...
Apidays New York 2024 - APIs in 2030: The Risk of Technological Sleepwalk by ...Apidays New York 2024 - APIs in 2030: The Risk of Technological Sleepwalk by ...
Apidays New York 2024 - APIs in 2030: The Risk of Technological Sleepwalk by ...apidays
 
DBX First Quarter 2024 Investor Presentation
DBX First Quarter 2024 Investor PresentationDBX First Quarter 2024 Investor Presentation
DBX First Quarter 2024 Investor PresentationDropbox
 

Recently uploaded (20)

Vector Search -An Introduction in Oracle Database 23ai.pptx
Vector Search -An Introduction in Oracle Database 23ai.pptxVector Search -An Introduction in Oracle Database 23ai.pptx
Vector Search -An Introduction in Oracle Database 23ai.pptx
 
MS Copilot expands with MS Graph connectors
MS Copilot expands with MS Graph connectorsMS Copilot expands with MS Graph connectors
MS Copilot expands with MS Graph connectors
 
Corporate and higher education May webinar.pptx
Corporate and higher education May webinar.pptxCorporate and higher education May webinar.pptx
Corporate and higher education May webinar.pptx
 
Apidays New York 2024 - Passkeys: Developing APIs to enable passwordless auth...
Apidays New York 2024 - Passkeys: Developing APIs to enable passwordless auth...Apidays New York 2024 - Passkeys: Developing APIs to enable passwordless auth...
Apidays New York 2024 - Passkeys: Developing APIs to enable passwordless auth...
 
Polkadot JAM Slides - Token2049 - By Dr. Gavin Wood
Polkadot JAM Slides - Token2049 - By Dr. Gavin WoodPolkadot JAM Slides - Token2049 - By Dr. Gavin Wood
Polkadot JAM Slides - Token2049 - By Dr. Gavin Wood
 
+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...
+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...
+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...
 
EMPOWERMENT TECHNOLOGY GRADE 11 QUARTER 2 REVIEWER
EMPOWERMENT TECHNOLOGY GRADE 11 QUARTER 2 REVIEWEREMPOWERMENT TECHNOLOGY GRADE 11 QUARTER 2 REVIEWER
EMPOWERMENT TECHNOLOGY GRADE 11 QUARTER 2 REVIEWER
 
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers:  A Deep Dive into Serverless Spatial Data and FMECloud Frontiers:  A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
 
Artificial Intelligence Chap.5 : Uncertainty
Artificial Intelligence Chap.5 : UncertaintyArtificial Intelligence Chap.5 : Uncertainty
Artificial Intelligence Chap.5 : Uncertainty
 
DEV meet-up UiPath Document Understanding May 7 2024 Amsterdam
DEV meet-up UiPath Document Understanding May 7 2024 AmsterdamDEV meet-up UiPath Document Understanding May 7 2024 Amsterdam
DEV meet-up UiPath Document Understanding May 7 2024 Amsterdam
 
How to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected WorkerHow to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected Worker
 
Understanding the FAA Part 107 License ..
Understanding the FAA Part 107 License ..Understanding the FAA Part 107 License ..
Understanding the FAA Part 107 License ..
 
ICT role in 21st century education and its challenges
ICT role in 21st century education and its challengesICT role in 21st century education and its challenges
ICT role in 21st century education and its challenges
 
presentation ICT roal in 21st century education
presentation ICT roal in 21st century educationpresentation ICT roal in 21st century education
presentation ICT roal in 21st century education
 
Architecting Cloud Native Applications
Architecting Cloud Native ApplicationsArchitecting Cloud Native Applications
Architecting Cloud Native Applications
 
Exploring Multimodal Embeddings with Milvus
Exploring Multimodal Embeddings with MilvusExploring Multimodal Embeddings with Milvus
Exploring Multimodal Embeddings with Milvus
 
Connector Corner: Accelerate revenue generation using UiPath API-centric busi...
Connector Corner: Accelerate revenue generation using UiPath API-centric busi...Connector Corner: Accelerate revenue generation using UiPath API-centric busi...
Connector Corner: Accelerate revenue generation using UiPath API-centric busi...
 
Repurposing LNG terminals for Hydrogen Ammonia: Feasibility and Cost Saving
Repurposing LNG terminals for Hydrogen Ammonia: Feasibility and Cost SavingRepurposing LNG terminals for Hydrogen Ammonia: Feasibility and Cost Saving
Repurposing LNG terminals for Hydrogen Ammonia: Feasibility and Cost Saving
 
Apidays New York 2024 - APIs in 2030: The Risk of Technological Sleepwalk by ...
Apidays New York 2024 - APIs in 2030: The Risk of Technological Sleepwalk by ...Apidays New York 2024 - APIs in 2030: The Risk of Technological Sleepwalk by ...
Apidays New York 2024 - APIs in 2030: The Risk of Technological Sleepwalk by ...
 
DBX First Quarter 2024 Investor Presentation
DBX First Quarter 2024 Investor PresentationDBX First Quarter 2024 Investor Presentation
DBX First Quarter 2024 Investor Presentation
 

Metrics lightning talk

  • 2. Blackbird About Me • Engineer at Blackbird • Worked with C* since 0.8 (3 years) • 7 years as a Java/Python developer • Interests o Data Science o Hobbyist Electronics o Development
  • 3. Blackbird About Cassandra • Fault tolerant to a fault o easy to ignore until it gets bad • Like all other systems: o If not many events no one pays attention to it o If theres a lot of events need to keep eye on it o When things happen need information to quickly diagnose Basically...
  • 5. Blackbird Lots of Metrics A lot of data but with no context or understanding doesn’t have that much use … but you have lots of pretty graphs
  • 6. Blackbird Disclaimer This not all of the important metrics, in fact it is missing many critical ones • Heap • OS metrics • Latencies • Log messages
  • 7. Blackbird An Example for a little background Threads ReadStage x32 ClientRequest RequestResponse 231-1 231-1 Threads ReadRepairStage Threads 231-1 Messaging Service
  • 8. Blackbird Cassandra Key Metrics ● Cassandra internal messaging based on SEDA with many asynchronous elements ● Its easy to overrun the processing capabilities of a stage that is not in the requests feedback loop (i.e. ReadRepairStage)
  • 9. Blackbird Access the metrics ● nodetool tpstats Pool Name Active Pending Completed Blocked All time blocked ReadStage 0 0 113702 0 0 RequestResponseStage 0 0 0 0 0 MutationStage 0 0 164503 0 0 ... InternalResponseStage 0 0 0 0 0 HintedHandoff 0 0 0 0 0 Message type Dropped RANGE_SLICE 0 READ_REPAIR 0 ... REQUEST_RESPONSE 0 COUNTER_MUTATION 0 ● JMX org.apache.cassandra.request:type=* and org.apache.cassandra.internal:type=* ● Metrics Reporter MBean Attribute tpstats name Description ActiveCount Active Number of tasks pulled off the queue with a Thread currently processing. PendingTasks Pending Number of tasks in queue waiting for a thread CompletedTasks Completed Number of tasks completed CurrentlyBlockedTasks Blocked When a pool reaches its core pool size (configurable or set per stage, more below) it will begin queuing until the max size is reached. When this is reached it will block until there is room in the queue. TotalBlockedTasks All time blocked Total number of tasks that have been blocked
  • 10. Blackbird Examples • Read/Mutation Stage o Too many reads/writes, disk failure, poor tuning • ReplicateOnWrite (CounterMutationStage in 2.1+) o High throughput of counter increments • FlushWriter o writes over running disk capabilities, poor tuning o large collections • GossipStage o vnodes + many servers (pre 2.0.3)