SlideShare a Scribd company logo
1 of 47
Download to read offline
Fluentd vs. Logstash
Masaki Matsushita
NTT Communications
About Me
● Masaki MATSUSHITA
● Software Engineer at
○ We are providing Internet access here!
● Github: mmasaki Twitter: @_mmasaki
● 16 Commits in Liberty
○ Trove, oslo_log, oslo_config
● CRuby Commiter
○ 100+ commits for performance improvement
2
What are Log Collectors?
● Provide pluggable and unified logging layer
Without Log Collectors With Log Collectors
Images from http://fluentd.org/ 3
Input, Filter and Output
4
Input Plugins
tail
syslog
Filter Plugins
grep
hostname
Output Plugins
InfluxDB
Elasticsearch
● They are implemented as plugins
● Can be replaced easily
Log FIles
Components
Two Popular Log Collectors
● Fluentd
○ Written in CRuby
○ Used in Kubernetes
○ Maintained by Treasure Data Inc.
● Logstash
○ Written in JRuby
○ Maintained by elastic.co
● They have similar features
● Which one is better for you? 5
Agenda
● Comparisons
○ Configuration
○ Supported Plugins
○ Performance
○ Transport Protocol
● Integrate OpenStack with Fluentd/Logstash
○ Considering High Availability 6
Configuration: Fluentd
● Every inputs are tagged
● Logs will be routed by tag
nova-api.log
(tag: openstack.nova)
cinder-api.log
(tag: openstack.cinder)
<match openstack.nova>
<match openstack.cinder>
Filter/Route
7
Fluentd Configuration: Input
<source>
@type tail
path /var/log/nova/nova-api.log
tag openstack.nova
</source>
Example of tailing nova-api log
● Every inputs will be tagged
8
Fluentd Configuration: Output
<match openstack.nova> # nova related logs
@type elasticsearch
host example.com
</match>
<match openstack.*> # all other OpenStack related logs
@type influxdb
# …
</match>
Routed by tag
(First match is priority)
Wildcards can be used
9
Fluentd Configuration: Copy
<match openstack.*>
@type copy
<store>
@type influxdb
</store>
<store>
@type elasticsearch
</store>
</match>
Copy plugin enables multiple
outputs for a tag
Copied Output
tag: openstack.*
10
Logstash Configuration
● No tags
● All inputs will be aggregated
● Logs will be scattered to outputs
nova-api.log
cinder-api.log
Filter/Aggregate
aggregated logs
11
Logstash Configuration
input {
file { path => “/var/log/nova/*.log” }
file { path => “/var/log/cinder/*.log” }
}
output {
elasticsearch { hosts => [“example.com”] }
influxdb { host => “example.com”... }
}
12
Case 1: Separated Streams
Input1
Input2
Input3
Output2
Output3
Output1
● Handle multiple streams separately
13
Case 1: Separated Streams
Fluentd: Simple matching by tag
<match input.input1>
@type output1
</match>
<match input.input2>
@type output2
</match>
<match input.input3>
@type output3
</match>
Logstash: Conditional Outputs
output {
if [type] == “input1” {
output1 {}
} else if [type] == “input2” {
output2 {}
} else if [type] == “input3” {
output3 {}
}
}
Need to split aggregated logs
14
Case 2: Aggregated Streams
Input1
Input2
Input3
Output2
Output3
Output1
● Streams will be aggregated and scattered
15
Case 2: Aggregated Streams
Fluentd: Copy plugins is needed
<match input.*>
@type copy
<store>
@type output1
</store>
<store>
@type output2
</store>
<store>
@type output3
</store>
</match>
Logstash: Quite simple
output {
output1 {}
output2 {}
output3 {}
}
16
Configuration
● Fluentd
○ Routed by simple tag matching
○ Suited to handle log streams separately
● Logstash
○ Logs are aggregated
○ Suited to handle logs in gather-scatter style
17
Plugins
● Both provide many plugins
○ Fluentd: 300+, Logstash: 200+
● Popular plugins are bundled with Logstash
○ They are maintained by the Logstash project
● Fluentd contains only minimal plugins
○ Most plugins are maintained by individuals
● Plugins can be installed easily by one command
18
Performance
● Depends on circumstances
● More than enough for OpenStack logs
○ Both can handle 10000+ logs/s
● Applying heavy filters is not a good idea
● CRuby is slow because of GVL?
○ GVL: Global VM (Interpreter) Lock
○ It’s not true for IO bound loads
19
GVL on IO bound loads
● IO operation can be performed in parallel
20
Thread 1 Thread 2
Idle :
User Space:
Kernel Space:
Actual Read/Write
Ruby Code Execution
GVL Released/
Acquired
IO operations
in parallel
Transport Protocol
● Both collectors have their own transport protocol.
○ Failure Detection and Fallback
● Logstash: Lumberjack protocol
○ Active-Standby only
● Fluentd: forward protocol
○ Active-Active (Load Balancing), Active-Standby
○ Some additional features
21
Logstash Transport: lumberjack
● Active-Standby lumberjack { #config@source
hosts => [
“primary”,
“secondary”
]
port => 1234
ssl_certificate => …
}
primary
secondary
source
secondary is used
when primary fails
Fail
Fallback
22
Fluentd Transport: forward
● Active-Active
(Load Balancing)
<match openstack.*>
type forward
<server>
host dest1
</server>
<server>
host dest2
</server>
</match>
source dest1
dest2
Equally balanced
outputs
23
Fluentd Transport: forward
● Active-Standby <match openstack.*>
type forward
<server>
host primary
</server>
<server>
host secondary
standby
</server>
</match>
primary
secondary
source
Fail
Fallback
24
Fluentd Transport: forward
● Weighted Load Balancing
<match openstack.*>
type forward
<server>
host dest1
weight 60
</server>
<server>
host dest2
weight 40
</server>
</match>
source dest1
dest2
60%
40%
25
Fluentd Transport: forward
● At-least-one Semantics
(may affect performance)
<match openstack.*>
type forward
require_ack_response
<server>
host dest
</server>
</match>
destsource
send logs
ACK
Logs are re-transmitted
until ACK is received
26
Transport Protocol
● Both can be configured as Active-Standby mode.
● Fluentd has great features:
○ Active-Active Mode (Load Balancing)
○ At-least-one Semantics
○ Weighted Load Balancing
27
Forwarders
● Fluentd/Logstash have their own “forwarders”
○ Lightweight implementation written in Golang
○ Low memory consumption
○ One binary: Less dependent and easy to install
28
Node
Tail log files
Forwarder
Log AggregatorForward/
Lumberjack
Protocol
Forwarders: Config Example
fluentd-forwarder:
[fluentd-forwarder]
to = fluent://fluentd1:24224
to = fluent://fluentd2:24224
logstash-forwarder:
"network": {
"servers": [
"logstash1:5043",
"logstash2:5043"
]
}Always send logs to both servers.
Pick one active server and send logs only to it.
Fallback to another server on failure. 29
Integration with OpenStack
● Tail log files by local Fluentd/Logstash
○ must parse many form of log files
● Rsyslog
○ installed by default in most distribution
○ can receive logs in JSON format
● Direct output from oslo_log
○ oslo_log: logging library used by components
○ Logging without any parsing 30
Log
Aggregators
OpenStack nodes
Tail Log Files
31
Tail log files
Forward Protocol
dest1
dest2
Tail Log Files
• Must handle many log files…
syslog
kern.log
apache2/access.log
apache2/error.log
keystone/keystone-all.log
keystone/keystone-manage.log
keystone/keystone.log
cinder/cinder-api.log
cinder/cinder-scheduler.log
neutron/neutron-server.log
neutron/neutron-server.log
nova/nova-api.log
nova/nova-conductor.log
nova/nova-consoleauth.log
nova/nova-manage.log
nova/nova-novncproxy.log
nova/nova-scheduler.log
mysql/error.log
mysql/mysql-slow.log
mysql.log
mysql.err
nova/nova-compute.log
nova/nova-manage.log...
32
Tail Log Files
• But you can use wildcard
Fluentd:
<source>
type tail
path /var/log/nova/*.log
tag openstack.nova
</source>
Logstash:
input {
file {
path => [“/var/log/nova/*.log”]
}
}
33
Parse Text Log
● Welcome to regular expression hell!
<source>
type tail # or syslog
path /var/log/nova/nova-api.log
format /^(?<asctime>.+) (?<process>d+) (?<loglevel>w+) (?
<objname>S+)( [(-|(?<request_id>.+?) (?<user_identity>.+))])?
((?<remote>S*) "(?<method>S+) (?<path>[^"]*) S*?" status: (?
<code>d*) len: (?<size>d*) time: (?<res_time>S)|(?<message>.
*))/
</source>
34
Log
Aggregators
OpenStack nodes
Rsyslog
35
via /dev/log
Syslog Protocol
(TCP or UDP)
rsyslog
Rsyslog: Logging.conf
● Logging Configuration in detail
● Handler: Syslog, Formatter: JSON
# /etc/{nova,cinder…}/logging.conf
[handler_syslog]
class = handlers.SysLogHandler
args = ('/dev/log', handlers.SysLogHandler.LOG_LOCAL1)
formatter = json
[formatter_json]
class = oslo_log.formatters.JSONFormatter 36
Example Output: JSONFormatter
{
"levelname": "INFO",
"funcname": "start",
"message": "Starting conductor node (version 13.0.0)",
"msg": "Starting %(topic)s node (version %(version)s)",
"asctime": "2015-09-29 18:29:57,690",
"relative_created": 2454.8499584198,
"process": 25204,
"created": 1443518997.690932,
"thread": 140119466896752,
"name": "nova.service",
"process_name": "MainProcess",
"thread_name": "GreenThread-1",
...
37
Syslog Facilities
● Assignment of local0..7 Facilities for components
● Logs are tagged as like “syslog.local0” in Fluentd
● Example:
○ local0: Keystone
○ local1: Nova
○ local2: Cinder
○ local3: Neutron
○ local4: Glance
38
Rsyslog: Config@OpenStack nodes
● Active-Standby Configuration
# /etc/rsyslog.d/rsyslog.conf
user.* @@primary:5140
$ActionExecOnlyWhenPreviousIsSuspended on
&@@secondary:5140
39
Rsyslog: Config@Aggregator
Fluentd:
<source>
type syslog
port 5140
protocol_type tcp
format json
tag syslog
</source>
Logstash:
input {
syslog {
codec => json
port => 5140
}
} Listen on both TCP and UDP
Specify TCP or UDP 40
Rsyslog: Config@Aggregator
Fluentd:
<source>
type syslog
port 5140
protocol_type tcp
format json
tag syslog
</source>
Logstash:
input {
syslog {
codec => json
port => 5140
}
}
41
Log
AggregatorsOpenStack nodes
42
via FluentHandler
Forward Protocol
Direct output from oslo_log
Local Fluentd for buffering/load balancing
(Logstash also can be used)
Direct output from oslo_log
# logging.conf:
[handler_fluent]
class = fluent.handler.FluentHandler # fluent-logger
formatter = fluent
args = (’openstack.nova', 'localhost', 24224)
[formatter_fluent]
class = fluent.handler.FluentFormatter # our Blueprint
43
Format logs as Dictionary
Our BP in oslo_log: FluentFormatter
{
"hostname":"allinone-vivid",
"extra":{"project":"unknown","version":"unknown"},
"process_name":"MainProcess",
"module":"wsgi",
"message":"(4132) wsgi starting up on http://0.0.0.0:8774/",
"filename":"wsgi.py",
"name":"nova.osapi_compute.wsgi.server",
"level":"INFO",
"traceback":null,
"funcname":"server",
"time":"2015-10-15 10:09:12,255"
}
Don’t need to parse!
44
Conclusion
● Log Handling
○ Fluentd: Logs are distinguished by tag
○ Logstash: No tags. Logs are aggregated
● Transport Protocol
○ Both supports active-standby mode
○ Fluentd supports some additional features
■ Client-side load balancing (Active-Active)
■ At-least-one semantics
■ Weighted load balancing 45
Conclusion
● Integration with OpenStack
○ Tail log files: regular expression hell
○ Rsyslog: No agents are needed
○ Direct output from oslo_log w/o any parsing
○ Review is welcome for our Blueprint
(oslo_log: fluent-formatter)
46
Thank you!
Please visit our booth!
Robot Racing over WebRTC! →

More Related Content

What's hot

Using eBPF for High-Performance Networking in Cilium
Using eBPF for High-Performance Networking in CiliumUsing eBPF for High-Performance Networking in Cilium
Using eBPF for High-Performance Networking in CiliumScyllaDB
 
Linux BPF Superpowers
Linux BPF SuperpowersLinux BPF Superpowers
Linux BPF SuperpowersBrendan Gregg
 
BGP Unnumbered で遊んでみた
BGP Unnumbered で遊んでみたBGP Unnumbered で遊んでみた
BGP Unnumbered で遊んでみたakira6592
 
MySQL_MariaDB-성능개선-202201.pptx
MySQL_MariaDB-성능개선-202201.pptxMySQL_MariaDB-성능개선-202201.pptx
MySQL_MariaDB-성능개선-202201.pptxNeoClova
 
Building a Streaming Microservice Architecture: with Apache Spark Structured ...
Building a Streaming Microservice Architecture: with Apache Spark Structured ...Building a Streaming Microservice Architecture: with Apache Spark Structured ...
Building a Streaming Microservice Architecture: with Apache Spark Structured ...Databricks
 
OpenTelemetry For Architects
OpenTelemetry For ArchitectsOpenTelemetry For Architects
OpenTelemetry For ArchitectsKevin Brockhoff
 
Performance Profiling in Rust
Performance Profiling in RustPerformance Profiling in Rust
Performance Profiling in RustInfluxData
 
BHyVeってなんや
BHyVeってなんやBHyVeってなんや
BHyVeってなんやTakuya ASADA
 
IntelON 2021 Processor Benchmarking
IntelON 2021 Processor BenchmarkingIntelON 2021 Processor Benchmarking
IntelON 2021 Processor BenchmarkingBrendan Gregg
 
Observability in Java: Getting Started with OpenTelemetry
Observability in Java: Getting Started with OpenTelemetryObservability in Java: Getting Started with OpenTelemetry
Observability in Java: Getting Started with OpenTelemetryDevOps.com
 
eBPF - Rethinking the Linux Kernel
eBPF - Rethinking the Linux KerneleBPF - Rethinking the Linux Kernel
eBPF - Rethinking the Linux KernelThomas Graf
 
Fluentdのお勧めシステム構成パターン
Fluentdのお勧めシステム構成パターンFluentdのお勧めシステム構成パターン
Fluentdのお勧めシステム構成パターンKentaro Yoshida
 
Ethernetの受信処理
Ethernetの受信処理Ethernetの受信処理
Ethernetの受信処理Takuya ASADA
 
マルチコアとネットワークスタックの高速化技法
マルチコアとネットワークスタックの高速化技法マルチコアとネットワークスタックの高速化技法
マルチコアとネットワークスタックの高速化技法Takuya ASADA
 
AvailabilityZoneとHostAggregate
AvailabilityZoneとHostAggregateAvailabilityZoneとHostAggregate
AvailabilityZoneとHostAggregateHiroki Ishikawa
 
Linux女子部 systemd徹底入門
Linux女子部 systemd徹底入門Linux女子部 systemd徹底入門
Linux女子部 systemd徹底入門Etsuji Nakai
 
Fluentd Overview, Now and Then
Fluentd Overview, Now and ThenFluentd Overview, Now and Then
Fluentd Overview, Now and ThenSATOSHI TAGOMORI
 
MySQLを割と一人で300台管理する技術
MySQLを割と一人で300台管理する技術MySQLを割と一人で300台管理する技術
MySQLを割と一人で300台管理する技術yoku0825
 
Handle Large Messages In Apache Kafka
Handle Large Messages In Apache KafkaHandle Large Messages In Apache Kafka
Handle Large Messages In Apache KafkaJiangjie Qin
 
TripleOの光と闇
TripleOの光と闇TripleOの光と闇
TripleOの光と闇Manabu Ori
 

What's hot (20)

Using eBPF for High-Performance Networking in Cilium
Using eBPF for High-Performance Networking in CiliumUsing eBPF for High-Performance Networking in Cilium
Using eBPF for High-Performance Networking in Cilium
 
Linux BPF Superpowers
Linux BPF SuperpowersLinux BPF Superpowers
Linux BPF Superpowers
 
BGP Unnumbered で遊んでみた
BGP Unnumbered で遊んでみたBGP Unnumbered で遊んでみた
BGP Unnumbered で遊んでみた
 
MySQL_MariaDB-성능개선-202201.pptx
MySQL_MariaDB-성능개선-202201.pptxMySQL_MariaDB-성능개선-202201.pptx
MySQL_MariaDB-성능개선-202201.pptx
 
Building a Streaming Microservice Architecture: with Apache Spark Structured ...
Building a Streaming Microservice Architecture: with Apache Spark Structured ...Building a Streaming Microservice Architecture: with Apache Spark Structured ...
Building a Streaming Microservice Architecture: with Apache Spark Structured ...
 
OpenTelemetry For Architects
OpenTelemetry For ArchitectsOpenTelemetry For Architects
OpenTelemetry For Architects
 
Performance Profiling in Rust
Performance Profiling in RustPerformance Profiling in Rust
Performance Profiling in Rust
 
BHyVeってなんや
BHyVeってなんやBHyVeってなんや
BHyVeってなんや
 
IntelON 2021 Processor Benchmarking
IntelON 2021 Processor BenchmarkingIntelON 2021 Processor Benchmarking
IntelON 2021 Processor Benchmarking
 
Observability in Java: Getting Started with OpenTelemetry
Observability in Java: Getting Started with OpenTelemetryObservability in Java: Getting Started with OpenTelemetry
Observability in Java: Getting Started with OpenTelemetry
 
eBPF - Rethinking the Linux Kernel
eBPF - Rethinking the Linux KerneleBPF - Rethinking the Linux Kernel
eBPF - Rethinking the Linux Kernel
 
Fluentdのお勧めシステム構成パターン
Fluentdのお勧めシステム構成パターンFluentdのお勧めシステム構成パターン
Fluentdのお勧めシステム構成パターン
 
Ethernetの受信処理
Ethernetの受信処理Ethernetの受信処理
Ethernetの受信処理
 
マルチコアとネットワークスタックの高速化技法
マルチコアとネットワークスタックの高速化技法マルチコアとネットワークスタックの高速化技法
マルチコアとネットワークスタックの高速化技法
 
AvailabilityZoneとHostAggregate
AvailabilityZoneとHostAggregateAvailabilityZoneとHostAggregate
AvailabilityZoneとHostAggregate
 
Linux女子部 systemd徹底入門
Linux女子部 systemd徹底入門Linux女子部 systemd徹底入門
Linux女子部 systemd徹底入門
 
Fluentd Overview, Now and Then
Fluentd Overview, Now and ThenFluentd Overview, Now and Then
Fluentd Overview, Now and Then
 
MySQLを割と一人で300台管理する技術
MySQLを割と一人で300台管理する技術MySQLを割と一人で300台管理する技術
MySQLを割と一人で300台管理する技術
 
Handle Large Messages In Apache Kafka
Handle Large Messages In Apache KafkaHandle Large Messages In Apache Kafka
Handle Large Messages In Apache Kafka
 
TripleOの光と闇
TripleOの光と闇TripleOの光と闇
TripleOの光と闇
 

Viewers also liked

BI, Reporting and Analytics on Apache Cassandra
BI, Reporting and Analytics on Apache CassandraBI, Reporting and Analytics on Apache Cassandra
BI, Reporting and Analytics on Apache CassandraVictor Coustenoble
 
AdStage: Monacella: An Relational Object Database using Cassandra as the Data...
AdStage: Monacella: An Relational Object Database using Cassandra as the Data...AdStage: Monacella: An Relational Object Database using Cassandra as the Data...
AdStage: Monacella: An Relational Object Database using Cassandra as the Data...DataStax Academy
 
From Zero to Hero - Centralized Logging with Logstash & Elasticsearch
From Zero to Hero - Centralized Logging with Logstash & ElasticsearchFrom Zero to Hero - Centralized Logging with Logstash & Elasticsearch
From Zero to Hero - Centralized Logging with Logstash & ElasticsearchSematext Group, Inc.
 
Integrando Redis en aplicaciones Symfony2
Integrando Redis en aplicaciones Symfony2Integrando Redis en aplicaciones Symfony2
Integrando Redis en aplicaciones Symfony2Ronny López
 
Fluentd and PHP
Fluentd and PHPFluentd and PHP
Fluentd and PHPchobi e
 
Application Logging With The ELK Stack
Application Logging With The ELK StackApplication Logging With The ELK Stack
Application Logging With The ELK Stackbenwaine
 
Fluentd and docker monitoring
Fluentd and docker monitoringFluentd and docker monitoring
Fluentd and docker monitoringVinay Krishna
 
Application Logging With Logstash
Application Logging With LogstashApplication Logging With Logstash
Application Logging With Logstashbenwaine
 
Logging with Elasticsearch, Logstash & Kibana
Logging with Elasticsearch, Logstash & KibanaLogging with Elasticsearch, Logstash & Kibana
Logging with Elasticsearch, Logstash & KibanaAmazee Labs
 

Viewers also liked (9)

BI, Reporting and Analytics on Apache Cassandra
BI, Reporting and Analytics on Apache CassandraBI, Reporting and Analytics on Apache Cassandra
BI, Reporting and Analytics on Apache Cassandra
 
AdStage: Monacella: An Relational Object Database using Cassandra as the Data...
AdStage: Monacella: An Relational Object Database using Cassandra as the Data...AdStage: Monacella: An Relational Object Database using Cassandra as the Data...
AdStage: Monacella: An Relational Object Database using Cassandra as the Data...
 
From Zero to Hero - Centralized Logging with Logstash & Elasticsearch
From Zero to Hero - Centralized Logging with Logstash & ElasticsearchFrom Zero to Hero - Centralized Logging with Logstash & Elasticsearch
From Zero to Hero - Centralized Logging with Logstash & Elasticsearch
 
Integrando Redis en aplicaciones Symfony2
Integrando Redis en aplicaciones Symfony2Integrando Redis en aplicaciones Symfony2
Integrando Redis en aplicaciones Symfony2
 
Fluentd and PHP
Fluentd and PHPFluentd and PHP
Fluentd and PHP
 
Application Logging With The ELK Stack
Application Logging With The ELK StackApplication Logging With The ELK Stack
Application Logging With The ELK Stack
 
Fluentd and docker monitoring
Fluentd and docker monitoringFluentd and docker monitoring
Fluentd and docker monitoring
 
Application Logging With Logstash
Application Logging With LogstashApplication Logging With Logstash
Application Logging With Logstash
 
Logging with Elasticsearch, Logstash & Kibana
Logging with Elasticsearch, Logstash & KibanaLogging with Elasticsearch, Logstash & Kibana
Logging with Elasticsearch, Logstash & Kibana
 

Similar to Fluentd vs Logstash - Which is better

Building a Unified Logging Layer with Fluentd, Elasticsearch and Kibana
Building a Unified Logging Layer with Fluentd, Elasticsearch and KibanaBuilding a Unified Logging Layer with Fluentd, Elasticsearch and Kibana
Building a Unified Logging Layer with Fluentd, Elasticsearch and KibanaMushfekur Rahman
 
Experiences building a distributed shared log on RADOS - Noah Watkins
Experiences building a distributed shared log on RADOS - Noah WatkinsExperiences building a distributed shared log on RADOS - Noah Watkins
Experiences building a distributed shared log on RADOS - Noah WatkinsCeph Community
 
OpenTelemetry For Operators
OpenTelemetry For OperatorsOpenTelemetry For Operators
OpenTelemetry For OperatorsKevin Brockhoff
 
Logstash: Get to know your logs
Logstash: Get to know your logsLogstash: Get to know your logs
Logstash: Get to know your logsSmartLogic
 
Mirko Damiani - An Embedded soft real time distributed system in Go
Mirko Damiani - An Embedded soft real time distributed system in GoMirko Damiani - An Embedded soft real time distributed system in Go
Mirko Damiani - An Embedded soft real time distributed system in Golinuxlab_conf
 
[scala.by] Launching new application fast
[scala.by] Launching new application fast[scala.by] Launching new application fast
[scala.by] Launching new application fastDenis Karpenko
 
My Sql Proxy
My Sql ProxyMy Sql Proxy
My Sql ProxyLiu Lizhi
 
Turbo charge your logs
Turbo charge your logsTurbo charge your logs
Turbo charge your logsJeremy Cook
 
Monitoring.pptx
Monitoring.pptxMonitoring.pptx
Monitoring.pptxShadi Akil
 
Introduction to redis - version 2
Introduction to redis - version 2Introduction to redis - version 2
Introduction to redis - version 2Dvir Volk
 
"Lightweight Virtualization with Linux Containers and Docker". Jerome Petazzo...
"Lightweight Virtualization with Linux Containers and Docker". Jerome Petazzo..."Lightweight Virtualization with Linux Containers and Docker". Jerome Petazzo...
"Lightweight Virtualization with Linux Containers and Docker". Jerome Petazzo...Yandex
 
MySQL for Oracle DBAs
MySQL for Oracle DBAsMySQL for Oracle DBAs
MySQL for Oracle DBAsFromDual GmbH
 
Modern Java Features
Modern Java Features Modern Java Features
Modern Java Features Florian Hopf
 
Building zero data loss pipelines with apache kafka
Building zero data loss pipelines with apache kafkaBuilding zero data loss pipelines with apache kafka
Building zero data loss pipelines with apache kafkaAvinash Ramineni
 
Logs @ OVHcloud
Logs @ OVHcloudLogs @ OVHcloud
Logs @ OVHcloudOVHcloud
 
BUD17-310: Introducing LLDB for linux on Arm and AArch64
BUD17-310: Introducing LLDB for linux on Arm and AArch64 BUD17-310: Introducing LLDB for linux on Arm and AArch64
BUD17-310: Introducing LLDB for linux on Arm and AArch64 Linaro
 
Apache Flink: Better, Faster & Uncut - Piotr Nowojski, data Artisans
Apache Flink: Better, Faster & Uncut - Piotr Nowojski, data ArtisansApache Flink: Better, Faster & Uncut - Piotr Nowojski, data Artisans
Apache Flink: Better, Faster & Uncut - Piotr Nowojski, data ArtisansEvention
 

Similar to Fluentd vs Logstash - Which is better (20)

Logging for Containers
Logging for ContainersLogging for Containers
Logging for Containers
 
Containers and Logging
Containers and LoggingContainers and Logging
Containers and Logging
 
Building a Unified Logging Layer with Fluentd, Elasticsearch and Kibana
Building a Unified Logging Layer with Fluentd, Elasticsearch and KibanaBuilding a Unified Logging Layer with Fluentd, Elasticsearch and Kibana
Building a Unified Logging Layer with Fluentd, Elasticsearch and Kibana
 
Experiences building a distributed shared log on RADOS - Noah Watkins
Experiences building a distributed shared log on RADOS - Noah WatkinsExperiences building a distributed shared log on RADOS - Noah Watkins
Experiences building a distributed shared log on RADOS - Noah Watkins
 
OpenTelemetry For Operators
OpenTelemetry For OperatorsOpenTelemetry For Operators
OpenTelemetry For Operators
 
Docker Logging Webinar
Docker Logging  WebinarDocker Logging  Webinar
Docker Logging Webinar
 
Logstash: Get to know your logs
Logstash: Get to know your logsLogstash: Get to know your logs
Logstash: Get to know your logs
 
Mirko Damiani - An Embedded soft real time distributed system in Go
Mirko Damiani - An Embedded soft real time distributed system in GoMirko Damiani - An Embedded soft real time distributed system in Go
Mirko Damiani - An Embedded soft real time distributed system in Go
 
[scala.by] Launching new application fast
[scala.by] Launching new application fast[scala.by] Launching new application fast
[scala.by] Launching new application fast
 
My Sql Proxy
My Sql ProxyMy Sql Proxy
My Sql Proxy
 
Turbo charge your logs
Turbo charge your logsTurbo charge your logs
Turbo charge your logs
 
Monitoring.pptx
Monitoring.pptxMonitoring.pptx
Monitoring.pptx
 
Introduction to redis - version 2
Introduction to redis - version 2Introduction to redis - version 2
Introduction to redis - version 2
 
"Lightweight Virtualization with Linux Containers and Docker". Jerome Petazzo...
"Lightweight Virtualization with Linux Containers and Docker". Jerome Petazzo..."Lightweight Virtualization with Linux Containers and Docker". Jerome Petazzo...
"Lightweight Virtualization with Linux Containers and Docker". Jerome Petazzo...
 
MySQL for Oracle DBAs
MySQL for Oracle DBAsMySQL for Oracle DBAs
MySQL for Oracle DBAs
 
Modern Java Features
Modern Java Features Modern Java Features
Modern Java Features
 
Building zero data loss pipelines with apache kafka
Building zero data loss pipelines with apache kafkaBuilding zero data loss pipelines with apache kafka
Building zero data loss pipelines with apache kafka
 
Logs @ OVHcloud
Logs @ OVHcloudLogs @ OVHcloud
Logs @ OVHcloud
 
BUD17-310: Introducing LLDB for linux on Arm and AArch64
BUD17-310: Introducing LLDB for linux on Arm and AArch64 BUD17-310: Introducing LLDB for linux on Arm and AArch64
BUD17-310: Introducing LLDB for linux on Arm and AArch64
 
Apache Flink: Better, Faster & Uncut - Piotr Nowojski, data Artisans
Apache Flink: Better, Faster & Uncut - Piotr Nowojski, data ArtisansApache Flink: Better, Faster & Uncut - Piotr Nowojski, data Artisans
Apache Flink: Better, Faster & Uncut - Piotr Nowojski, data Artisans
 

More from NTT Communications Technology Development

クラウドを最大限活用するinfrastructure as codeを考えよう
クラウドを最大限活用するinfrastructure as codeを考えようクラウドを最大限活用するinfrastructure as codeを考えよう
クラウドを最大限活用するinfrastructure as codeを考えようNTT Communications Technology Development
 
【たぶん日本初導入!】Azure Stack Hub with GPUの性能と機能紹介
【たぶん日本初導入!】Azure Stack Hub with GPUの性能と機能紹介【たぶん日本初導入!】Azure Stack Hub with GPUの性能と機能紹介
【たぶん日本初導入!】Azure Stack Hub with GPUの性能と機能紹介NTT Communications Technology Development
 
macOSの仮想化技術について ~Virtualization-rs Rust bindings for virtualization.framework ~
macOSの仮想化技術について ~Virtualization-rs Rust bindings for virtualization.framework ~macOSの仮想化技術について ~Virtualization-rs Rust bindings for virtualization.framework ~
macOSの仮想化技術について ~Virtualization-rs Rust bindings for virtualization.framework ~NTT Communications Technology Development
 
マルチクラウドでContinuous Deliveryを実現するSpinnakerについて
マルチクラウドでContinuous Deliveryを実現するSpinnakerについて マルチクラウドでContinuous Deliveryを実現するSpinnakerについて
マルチクラウドでContinuous Deliveryを実現するSpinnakerについて NTT Communications Technology Development
 
Can we boost more HPC performance? Integrate IBM POWER servers with GPUs to O...
Can we boost more HPC performance? Integrate IBM POWER servers with GPUs to O...Can we boost more HPC performance? Integrate IBM POWER servers with GPUs to O...
Can we boost more HPC performance? Integrate IBM POWER servers with GPUs to O...NTT Communications Technology Development
 
イケてない開発チームがイケてる開発を始めようとする軌跡
イケてない開発チームがイケてる開発を始めようとする軌跡イケてない開発チームがイケてる開発を始めようとする軌跡
イケてない開発チームがイケてる開発を始めようとする軌跡NTT Communications Technology Development
 

More from NTT Communications Technology Development (20)

クラウドを最大限活用するinfrastructure as codeを考えよう
クラウドを最大限活用するinfrastructure as codeを考えようクラウドを最大限活用するinfrastructure as codeを考えよう
クラウドを最大限活用するinfrastructure as codeを考えよう
 
【たぶん日本初導入!】Azure Stack Hub with GPUの性能と機能紹介
【たぶん日本初導入!】Azure Stack Hub with GPUの性能と機能紹介【たぶん日本初導入!】Azure Stack Hub with GPUの性能と機能紹介
【たぶん日本初導入!】Azure Stack Hub with GPUの性能と機能紹介
 
macOSの仮想化技術について ~Virtualization-rs Rust bindings for virtualization.framework ~
macOSの仮想化技術について ~Virtualization-rs Rust bindings for virtualization.framework ~macOSの仮想化技術について ~Virtualization-rs Rust bindings for virtualization.framework ~
macOSの仮想化技術について ~Virtualization-rs Rust bindings for virtualization.framework ~
 
マルチクラウドでContinuous Deliveryを実現するSpinnakerについて
マルチクラウドでContinuous Deliveryを実現するSpinnakerについて マルチクラウドでContinuous Deliveryを実現するSpinnakerについて
マルチクラウドでContinuous Deliveryを実現するSpinnakerについて
 
Argo CDについて
Argo CDについてArgo CDについて
Argo CDについて
 
SpinnakerとKayentaで 高速・安全なデプロイ!
SpinnakerとKayentaで 高速・安全なデプロイ!SpinnakerとKayentaで 高速・安全なデプロイ!
SpinnakerとKayentaで 高速・安全なデプロイ!
 
100Gbps OpenStack For Providing High-Performance NFV
100Gbps OpenStack For Providing High-Performance NFV100Gbps OpenStack For Providing High-Performance NFV
100Gbps OpenStack For Providing High-Performance NFV
 
Can we boost more HPC performance? Integrate IBM POWER servers with GPUs to O...
Can we boost more HPC performance? Integrate IBM POWER servers with GPUs to O...Can we boost more HPC performance? Integrate IBM POWER servers with GPUs to O...
Can we boost more HPC performance? Integrate IBM POWER servers with GPUs to O...
 
AWS re:Invent2017で見た AWSの強さとは
AWS re:Invent2017で見た AWSの強さとは AWS re:Invent2017で見た AWSの強さとは
AWS re:Invent2017で見た AWSの強さとは
 
分散トレーシング技術について(Open tracingやjaeger)
分散トレーシング技術について(Open tracingやjaeger)分散トレーシング技術について(Open tracingやjaeger)
分散トレーシング技術について(Open tracingやjaeger)
 
Mexico ops meetup発表資料 20170905
Mexico ops meetup発表資料 20170905Mexico ops meetup発表資料 20170905
Mexico ops meetup発表資料 20170905
 
NTT Tech Conference #2 - closing -
NTT Tech Conference #2 - closing -NTT Tech Conference #2 - closing -
NTT Tech Conference #2 - closing -
 
イケてない開発チームがイケてる開発を始めようとする軌跡
イケてない開発チームがイケてる開発を始めようとする軌跡イケてない開発チームがイケてる開発を始めようとする軌跡
イケてない開発チームがイケてる開発を始めようとする軌跡
 
GPU Container as a Service を実現するための最新OSS徹底比較
GPU Container as a Service を実現するための最新OSS徹底比較GPU Container as a Service を実現するための最新OSS徹底比較
GPU Container as a Service を実現するための最新OSS徹底比較
 
SpinnakerとOpenStackの構築
SpinnakerとOpenStackの構築SpinnakerとOpenStackの構築
SpinnakerとOpenStackの構築
 
Troveコミュニティ動向
Troveコミュニティ動向Troveコミュニティ動向
Troveコミュニティ動向
 
Web rtc for iot, edge computing use cases
Web rtc for iot, edge computing use casesWeb rtc for iot, edge computing use cases
Web rtc for iot, edge computing use cases
 
OpenStack Ops Mid-Cycle Meetup & Project Team Gathering出張報告
OpenStack Ops Mid-Cycle Meetup & Project Team Gathering出張報告OpenStack Ops Mid-Cycle Meetup & Project Team Gathering出張報告
OpenStack Ops Mid-Cycle Meetup & Project Team Gathering出張報告
 
NTT Tech Conference #1 Opening Keynote
NTT Tech Conference #1 Opening KeynoteNTT Tech Conference #1 Opening Keynote
NTT Tech Conference #1 Opening Keynote
 
NTT Tech Conference #1 Closing Keynote
NTT Tech Conference #1 Closing KeynoteNTT Tech Conference #1 Closing Keynote
NTT Tech Conference #1 Closing Keynote
 

Recently uploaded

Tech Tuesday - Mastering Time Management Unlock the Power of OnePlan's Timesh...
Tech Tuesday - Mastering Time Management Unlock the Power of OnePlan's Timesh...Tech Tuesday - Mastering Time Management Unlock the Power of OnePlan's Timesh...
Tech Tuesday - Mastering Time Management Unlock the Power of OnePlan's Timesh...OnePlan Solutions
 
Machine Learning Software Engineering Patterns and Their Engineering
Machine Learning Software Engineering Patterns and Their EngineeringMachine Learning Software Engineering Patterns and Their Engineering
Machine Learning Software Engineering Patterns and Their EngineeringHironori Washizaki
 
SoftTeco - Software Development Company Profile
SoftTeco - Software Development Company ProfileSoftTeco - Software Development Company Profile
SoftTeco - Software Development Company Profileakrivarotava
 
The Role of IoT and Sensor Technology in Cargo Cloud Solutions.pptx
The Role of IoT and Sensor Technology in Cargo Cloud Solutions.pptxThe Role of IoT and Sensor Technology in Cargo Cloud Solutions.pptx
The Role of IoT and Sensor Technology in Cargo Cloud Solutions.pptxRTS corp
 
Precise and Complete Requirements? An Elusive Goal
Precise and Complete Requirements? An Elusive GoalPrecise and Complete Requirements? An Elusive Goal
Precise and Complete Requirements? An Elusive GoalLionel Briand
 
OpenChain AI Study Group - Europe and Asia Recap - 2024-04-11 - Full Recording
OpenChain AI Study Group - Europe and Asia Recap - 2024-04-11 - Full RecordingOpenChain AI Study Group - Europe and Asia Recap - 2024-04-11 - Full Recording
OpenChain AI Study Group - Europe and Asia Recap - 2024-04-11 - Full RecordingShane Coughlan
 
20240415 [Container Plumbing Days] Usernetes Gen2 - Kubernetes in Rootless Do...
20240415 [Container Plumbing Days] Usernetes Gen2 - Kubernetes in Rootless Do...20240415 [Container Plumbing Days] Usernetes Gen2 - Kubernetes in Rootless Do...
20240415 [Container Plumbing Days] Usernetes Gen2 - Kubernetes in Rootless Do...Akihiro Suda
 
SensoDat: Simulation-based Sensor Dataset of Self-driving Cars
SensoDat: Simulation-based Sensor Dataset of Self-driving CarsSensoDat: Simulation-based Sensor Dataset of Self-driving Cars
SensoDat: Simulation-based Sensor Dataset of Self-driving CarsChristian Birchler
 
2024 DevNexus Patterns for Resiliency: Shuffle shards
2024 DevNexus Patterns for Resiliency: Shuffle shards2024 DevNexus Patterns for Resiliency: Shuffle shards
2024 DevNexus Patterns for Resiliency: Shuffle shardsChristopher Curtin
 
Post Quantum Cryptography – The Impact on Identity
Post Quantum Cryptography – The Impact on IdentityPost Quantum Cryptography – The Impact on Identity
Post Quantum Cryptography – The Impact on Identityteam-WIBU
 
Comparing Linux OS Image Update Models - EOSS 2024.pdf
Comparing Linux OS Image Update Models - EOSS 2024.pdfComparing Linux OS Image Update Models - EOSS 2024.pdf
Comparing Linux OS Image Update Models - EOSS 2024.pdfDrew Moseley
 
What is Advanced Excel and what are some best practices for designing and cre...
What is Advanced Excel and what are some best practices for designing and cre...What is Advanced Excel and what are some best practices for designing and cre...
What is Advanced Excel and what are some best practices for designing and cre...Technogeeks
 
Odoo 14 - eLearning Module In Odoo 14 Enterprise
Odoo 14 - eLearning Module In Odoo 14 EnterpriseOdoo 14 - eLearning Module In Odoo 14 Enterprise
Odoo 14 - eLearning Module In Odoo 14 Enterprisepreethippts
 
Introduction to Firebase Workshop Slides
Introduction to Firebase Workshop SlidesIntroduction to Firebase Workshop Slides
Introduction to Firebase Workshop Slidesvaideheekore1
 
Strategies for using alternative queries to mitigate zero results
Strategies for using alternative queries to mitigate zero resultsStrategies for using alternative queries to mitigate zero results
Strategies for using alternative queries to mitigate zero resultsJean Silva
 
Leveraging AI for Mobile App Testing on Real Devices | Applitools + Kobiton
Leveraging AI for Mobile App Testing on Real Devices | Applitools + KobitonLeveraging AI for Mobile App Testing on Real Devices | Applitools + Kobiton
Leveraging AI for Mobile App Testing on Real Devices | Applitools + KobitonApplitools
 
SpotFlow: Tracking Method Calls and States at Runtime
SpotFlow: Tracking Method Calls and States at RuntimeSpotFlow: Tracking Method Calls and States at Runtime
SpotFlow: Tracking Method Calls and States at Runtimeandrehoraa
 
Alfresco TTL#157 - Troubleshooting Made Easy: Deciphering Alfresco mTLS Confi...
Alfresco TTL#157 - Troubleshooting Made Easy: Deciphering Alfresco mTLS Confi...Alfresco TTL#157 - Troubleshooting Made Easy: Deciphering Alfresco mTLS Confi...
Alfresco TTL#157 - Troubleshooting Made Easy: Deciphering Alfresco mTLS Confi...Angel Borroy López
 
Catch the Wave: SAP Event-Driven and Data Streaming for the Intelligence Ente...
Catch the Wave: SAP Event-Driven and Data Streaming for the Intelligence Ente...Catch the Wave: SAP Event-Driven and Data Streaming for the Intelligence Ente...
Catch the Wave: SAP Event-Driven and Data Streaming for the Intelligence Ente...confluent
 
A healthy diet for your Java application Devoxx France.pdf
A healthy diet for your Java application Devoxx France.pdfA healthy diet for your Java application Devoxx France.pdf
A healthy diet for your Java application Devoxx France.pdfMarharyta Nedzelska
 

Recently uploaded (20)

Tech Tuesday - Mastering Time Management Unlock the Power of OnePlan's Timesh...
Tech Tuesday - Mastering Time Management Unlock the Power of OnePlan's Timesh...Tech Tuesday - Mastering Time Management Unlock the Power of OnePlan's Timesh...
Tech Tuesday - Mastering Time Management Unlock the Power of OnePlan's Timesh...
 
Machine Learning Software Engineering Patterns and Their Engineering
Machine Learning Software Engineering Patterns and Their EngineeringMachine Learning Software Engineering Patterns and Their Engineering
Machine Learning Software Engineering Patterns and Their Engineering
 
SoftTeco - Software Development Company Profile
SoftTeco - Software Development Company ProfileSoftTeco - Software Development Company Profile
SoftTeco - Software Development Company Profile
 
The Role of IoT and Sensor Technology in Cargo Cloud Solutions.pptx
The Role of IoT and Sensor Technology in Cargo Cloud Solutions.pptxThe Role of IoT and Sensor Technology in Cargo Cloud Solutions.pptx
The Role of IoT and Sensor Technology in Cargo Cloud Solutions.pptx
 
Precise and Complete Requirements? An Elusive Goal
Precise and Complete Requirements? An Elusive GoalPrecise and Complete Requirements? An Elusive Goal
Precise and Complete Requirements? An Elusive Goal
 
OpenChain AI Study Group - Europe and Asia Recap - 2024-04-11 - Full Recording
OpenChain AI Study Group - Europe and Asia Recap - 2024-04-11 - Full RecordingOpenChain AI Study Group - Europe and Asia Recap - 2024-04-11 - Full Recording
OpenChain AI Study Group - Europe and Asia Recap - 2024-04-11 - Full Recording
 
20240415 [Container Plumbing Days] Usernetes Gen2 - Kubernetes in Rootless Do...
20240415 [Container Plumbing Days] Usernetes Gen2 - Kubernetes in Rootless Do...20240415 [Container Plumbing Days] Usernetes Gen2 - Kubernetes in Rootless Do...
20240415 [Container Plumbing Days] Usernetes Gen2 - Kubernetes in Rootless Do...
 
SensoDat: Simulation-based Sensor Dataset of Self-driving Cars
SensoDat: Simulation-based Sensor Dataset of Self-driving CarsSensoDat: Simulation-based Sensor Dataset of Self-driving Cars
SensoDat: Simulation-based Sensor Dataset of Self-driving Cars
 
2024 DevNexus Patterns for Resiliency: Shuffle shards
2024 DevNexus Patterns for Resiliency: Shuffle shards2024 DevNexus Patterns for Resiliency: Shuffle shards
2024 DevNexus Patterns for Resiliency: Shuffle shards
 
Post Quantum Cryptography – The Impact on Identity
Post Quantum Cryptography – The Impact on IdentityPost Quantum Cryptography – The Impact on Identity
Post Quantum Cryptography – The Impact on Identity
 
Comparing Linux OS Image Update Models - EOSS 2024.pdf
Comparing Linux OS Image Update Models - EOSS 2024.pdfComparing Linux OS Image Update Models - EOSS 2024.pdf
Comparing Linux OS Image Update Models - EOSS 2024.pdf
 
What is Advanced Excel and what are some best practices for designing and cre...
What is Advanced Excel and what are some best practices for designing and cre...What is Advanced Excel and what are some best practices for designing and cre...
What is Advanced Excel and what are some best practices for designing and cre...
 
Odoo 14 - eLearning Module In Odoo 14 Enterprise
Odoo 14 - eLearning Module In Odoo 14 EnterpriseOdoo 14 - eLearning Module In Odoo 14 Enterprise
Odoo 14 - eLearning Module In Odoo 14 Enterprise
 
Introduction to Firebase Workshop Slides
Introduction to Firebase Workshop SlidesIntroduction to Firebase Workshop Slides
Introduction to Firebase Workshop Slides
 
Strategies for using alternative queries to mitigate zero results
Strategies for using alternative queries to mitigate zero resultsStrategies for using alternative queries to mitigate zero results
Strategies for using alternative queries to mitigate zero results
 
Leveraging AI for Mobile App Testing on Real Devices | Applitools + Kobiton
Leveraging AI for Mobile App Testing on Real Devices | Applitools + KobitonLeveraging AI for Mobile App Testing on Real Devices | Applitools + Kobiton
Leveraging AI for Mobile App Testing on Real Devices | Applitools + Kobiton
 
SpotFlow: Tracking Method Calls and States at Runtime
SpotFlow: Tracking Method Calls and States at RuntimeSpotFlow: Tracking Method Calls and States at Runtime
SpotFlow: Tracking Method Calls and States at Runtime
 
Alfresco TTL#157 - Troubleshooting Made Easy: Deciphering Alfresco mTLS Confi...
Alfresco TTL#157 - Troubleshooting Made Easy: Deciphering Alfresco mTLS Confi...Alfresco TTL#157 - Troubleshooting Made Easy: Deciphering Alfresco mTLS Confi...
Alfresco TTL#157 - Troubleshooting Made Easy: Deciphering Alfresco mTLS Confi...
 
Catch the Wave: SAP Event-Driven and Data Streaming for the Intelligence Ente...
Catch the Wave: SAP Event-Driven and Data Streaming for the Intelligence Ente...Catch the Wave: SAP Event-Driven and Data Streaming for the Intelligence Ente...
Catch the Wave: SAP Event-Driven and Data Streaming for the Intelligence Ente...
 
A healthy diet for your Java application Devoxx France.pdf
A healthy diet for your Java application Devoxx France.pdfA healthy diet for your Java application Devoxx France.pdf
A healthy diet for your Java application Devoxx France.pdf
 

Fluentd vs Logstash - Which is better

  • 1. Fluentd vs. Logstash Masaki Matsushita NTT Communications
  • 2. About Me ● Masaki MATSUSHITA ● Software Engineer at ○ We are providing Internet access here! ● Github: mmasaki Twitter: @_mmasaki ● 16 Commits in Liberty ○ Trove, oslo_log, oslo_config ● CRuby Commiter ○ 100+ commits for performance improvement 2
  • 3. What are Log Collectors? ● Provide pluggable and unified logging layer Without Log Collectors With Log Collectors Images from http://fluentd.org/ 3
  • 4. Input, Filter and Output 4 Input Plugins tail syslog Filter Plugins grep hostname Output Plugins InfluxDB Elasticsearch ● They are implemented as plugins ● Can be replaced easily Log FIles Components
  • 5. Two Popular Log Collectors ● Fluentd ○ Written in CRuby ○ Used in Kubernetes ○ Maintained by Treasure Data Inc. ● Logstash ○ Written in JRuby ○ Maintained by elastic.co ● They have similar features ● Which one is better for you? 5
  • 6. Agenda ● Comparisons ○ Configuration ○ Supported Plugins ○ Performance ○ Transport Protocol ● Integrate OpenStack with Fluentd/Logstash ○ Considering High Availability 6
  • 7. Configuration: Fluentd ● Every inputs are tagged ● Logs will be routed by tag nova-api.log (tag: openstack.nova) cinder-api.log (tag: openstack.cinder) <match openstack.nova> <match openstack.cinder> Filter/Route 7
  • 8. Fluentd Configuration: Input <source> @type tail path /var/log/nova/nova-api.log tag openstack.nova </source> Example of tailing nova-api log ● Every inputs will be tagged 8
  • 9. Fluentd Configuration: Output <match openstack.nova> # nova related logs @type elasticsearch host example.com </match> <match openstack.*> # all other OpenStack related logs @type influxdb # … </match> Routed by tag (First match is priority) Wildcards can be used 9
  • 10. Fluentd Configuration: Copy <match openstack.*> @type copy <store> @type influxdb </store> <store> @type elasticsearch </store> </match> Copy plugin enables multiple outputs for a tag Copied Output tag: openstack.* 10
  • 11. Logstash Configuration ● No tags ● All inputs will be aggregated ● Logs will be scattered to outputs nova-api.log cinder-api.log Filter/Aggregate aggregated logs 11
  • 12. Logstash Configuration input { file { path => “/var/log/nova/*.log” } file { path => “/var/log/cinder/*.log” } } output { elasticsearch { hosts => [“example.com”] } influxdb { host => “example.com”... } } 12
  • 13. Case 1: Separated Streams Input1 Input2 Input3 Output2 Output3 Output1 ● Handle multiple streams separately 13
  • 14. Case 1: Separated Streams Fluentd: Simple matching by tag <match input.input1> @type output1 </match> <match input.input2> @type output2 </match> <match input.input3> @type output3 </match> Logstash: Conditional Outputs output { if [type] == “input1” { output1 {} } else if [type] == “input2” { output2 {} } else if [type] == “input3” { output3 {} } } Need to split aggregated logs 14
  • 15. Case 2: Aggregated Streams Input1 Input2 Input3 Output2 Output3 Output1 ● Streams will be aggregated and scattered 15
  • 16. Case 2: Aggregated Streams Fluentd: Copy plugins is needed <match input.*> @type copy <store> @type output1 </store> <store> @type output2 </store> <store> @type output3 </store> </match> Logstash: Quite simple output { output1 {} output2 {} output3 {} } 16
  • 17. Configuration ● Fluentd ○ Routed by simple tag matching ○ Suited to handle log streams separately ● Logstash ○ Logs are aggregated ○ Suited to handle logs in gather-scatter style 17
  • 18. Plugins ● Both provide many plugins ○ Fluentd: 300+, Logstash: 200+ ● Popular plugins are bundled with Logstash ○ They are maintained by the Logstash project ● Fluentd contains only minimal plugins ○ Most plugins are maintained by individuals ● Plugins can be installed easily by one command 18
  • 19. Performance ● Depends on circumstances ● More than enough for OpenStack logs ○ Both can handle 10000+ logs/s ● Applying heavy filters is not a good idea ● CRuby is slow because of GVL? ○ GVL: Global VM (Interpreter) Lock ○ It’s not true for IO bound loads 19
  • 20. GVL on IO bound loads ● IO operation can be performed in parallel 20 Thread 1 Thread 2 Idle : User Space: Kernel Space: Actual Read/Write Ruby Code Execution GVL Released/ Acquired IO operations in parallel
  • 21. Transport Protocol ● Both collectors have their own transport protocol. ○ Failure Detection and Fallback ● Logstash: Lumberjack protocol ○ Active-Standby only ● Fluentd: forward protocol ○ Active-Active (Load Balancing), Active-Standby ○ Some additional features 21
  • 22. Logstash Transport: lumberjack ● Active-Standby lumberjack { #config@source hosts => [ “primary”, “secondary” ] port => 1234 ssl_certificate => … } primary secondary source secondary is used when primary fails Fail Fallback 22
  • 23. Fluentd Transport: forward ● Active-Active (Load Balancing) <match openstack.*> type forward <server> host dest1 </server> <server> host dest2 </server> </match> source dest1 dest2 Equally balanced outputs 23
  • 24. Fluentd Transport: forward ● Active-Standby <match openstack.*> type forward <server> host primary </server> <server> host secondary standby </server> </match> primary secondary source Fail Fallback 24
  • 25. Fluentd Transport: forward ● Weighted Load Balancing <match openstack.*> type forward <server> host dest1 weight 60 </server> <server> host dest2 weight 40 </server> </match> source dest1 dest2 60% 40% 25
  • 26. Fluentd Transport: forward ● At-least-one Semantics (may affect performance) <match openstack.*> type forward require_ack_response <server> host dest </server> </match> destsource send logs ACK Logs are re-transmitted until ACK is received 26
  • 27. Transport Protocol ● Both can be configured as Active-Standby mode. ● Fluentd has great features: ○ Active-Active Mode (Load Balancing) ○ At-least-one Semantics ○ Weighted Load Balancing 27
  • 28. Forwarders ● Fluentd/Logstash have their own “forwarders” ○ Lightweight implementation written in Golang ○ Low memory consumption ○ One binary: Less dependent and easy to install 28 Node Tail log files Forwarder Log AggregatorForward/ Lumberjack Protocol
  • 29. Forwarders: Config Example fluentd-forwarder: [fluentd-forwarder] to = fluent://fluentd1:24224 to = fluent://fluentd2:24224 logstash-forwarder: "network": { "servers": [ "logstash1:5043", "logstash2:5043" ] }Always send logs to both servers. Pick one active server and send logs only to it. Fallback to another server on failure. 29
  • 30. Integration with OpenStack ● Tail log files by local Fluentd/Logstash ○ must parse many form of log files ● Rsyslog ○ installed by default in most distribution ○ can receive logs in JSON format ● Direct output from oslo_log ○ oslo_log: logging library used by components ○ Logging without any parsing 30
  • 31. Log Aggregators OpenStack nodes Tail Log Files 31 Tail log files Forward Protocol dest1 dest2
  • 32. Tail Log Files • Must handle many log files… syslog kern.log apache2/access.log apache2/error.log keystone/keystone-all.log keystone/keystone-manage.log keystone/keystone.log cinder/cinder-api.log cinder/cinder-scheduler.log neutron/neutron-server.log neutron/neutron-server.log nova/nova-api.log nova/nova-conductor.log nova/nova-consoleauth.log nova/nova-manage.log nova/nova-novncproxy.log nova/nova-scheduler.log mysql/error.log mysql/mysql-slow.log mysql.log mysql.err nova/nova-compute.log nova/nova-manage.log... 32
  • 33. Tail Log Files • But you can use wildcard Fluentd: <source> type tail path /var/log/nova/*.log tag openstack.nova </source> Logstash: input { file { path => [“/var/log/nova/*.log”] } } 33
  • 34. Parse Text Log ● Welcome to regular expression hell! <source> type tail # or syslog path /var/log/nova/nova-api.log format /^(?<asctime>.+) (?<process>d+) (?<loglevel>w+) (? <objname>S+)( [(-|(?<request_id>.+?) (?<user_identity>.+))])? ((?<remote>S*) "(?<method>S+) (?<path>[^"]*) S*?" status: (? <code>d*) len: (?<size>d*) time: (?<res_time>S)|(?<message>. *))/ </source> 34
  • 36. Rsyslog: Logging.conf ● Logging Configuration in detail ● Handler: Syslog, Formatter: JSON # /etc/{nova,cinder…}/logging.conf [handler_syslog] class = handlers.SysLogHandler args = ('/dev/log', handlers.SysLogHandler.LOG_LOCAL1) formatter = json [formatter_json] class = oslo_log.formatters.JSONFormatter 36
  • 37. Example Output: JSONFormatter { "levelname": "INFO", "funcname": "start", "message": "Starting conductor node (version 13.0.0)", "msg": "Starting %(topic)s node (version %(version)s)", "asctime": "2015-09-29 18:29:57,690", "relative_created": 2454.8499584198, "process": 25204, "created": 1443518997.690932, "thread": 140119466896752, "name": "nova.service", "process_name": "MainProcess", "thread_name": "GreenThread-1", ... 37
  • 38. Syslog Facilities ● Assignment of local0..7 Facilities for components ● Logs are tagged as like “syslog.local0” in Fluentd ● Example: ○ local0: Keystone ○ local1: Nova ○ local2: Cinder ○ local3: Neutron ○ local4: Glance 38
  • 39. Rsyslog: Config@OpenStack nodes ● Active-Standby Configuration # /etc/rsyslog.d/rsyslog.conf user.* @@primary:5140 $ActionExecOnlyWhenPreviousIsSuspended on &@@secondary:5140 39
  • 40. Rsyslog: Config@Aggregator Fluentd: <source> type syslog port 5140 protocol_type tcp format json tag syslog </source> Logstash: input { syslog { codec => json port => 5140 } } Listen on both TCP and UDP Specify TCP or UDP 40
  • 41. Rsyslog: Config@Aggregator Fluentd: <source> type syslog port 5140 protocol_type tcp format json tag syslog </source> Logstash: input { syslog { codec => json port => 5140 } } 41
  • 42. Log AggregatorsOpenStack nodes 42 via FluentHandler Forward Protocol Direct output from oslo_log Local Fluentd for buffering/load balancing (Logstash also can be used)
  • 43. Direct output from oslo_log # logging.conf: [handler_fluent] class = fluent.handler.FluentHandler # fluent-logger formatter = fluent args = (’openstack.nova', 'localhost', 24224) [formatter_fluent] class = fluent.handler.FluentFormatter # our Blueprint 43 Format logs as Dictionary
  • 44. Our BP in oslo_log: FluentFormatter { "hostname":"allinone-vivid", "extra":{"project":"unknown","version":"unknown"}, "process_name":"MainProcess", "module":"wsgi", "message":"(4132) wsgi starting up on http://0.0.0.0:8774/", "filename":"wsgi.py", "name":"nova.osapi_compute.wsgi.server", "level":"INFO", "traceback":null, "funcname":"server", "time":"2015-10-15 10:09:12,255" } Don’t need to parse! 44
  • 45. Conclusion ● Log Handling ○ Fluentd: Logs are distinguished by tag ○ Logstash: No tags. Logs are aggregated ● Transport Protocol ○ Both supports active-standby mode ○ Fluentd supports some additional features ■ Client-side load balancing (Active-Active) ■ At-least-one semantics ■ Weighted load balancing 45
  • 46. Conclusion ● Integration with OpenStack ○ Tail log files: regular expression hell ○ Rsyslog: No agents are needed ○ Direct output from oslo_log w/o any parsing ○ Review is welcome for our Blueprint (oslo_log: fluent-formatter) 46
  • 47. Thank you! Please visit our booth! Robot Racing over WebRTC! →