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Pluggable server to Stream messages / events to queues like Kafka and other systems
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README.md

Sparkngin

Pronounced Spark Engine

Nginx + ZeroMQ = Awesome!

Sparkngin is a high-performance persistent message stream engine built on Nginx. Sparkngin can function as logging, event or message streaming solution. When used with Neverwinter Data Platform Sparkngin can stream data to data repositories like Hive, Hbase, Storm and HDFS.

The Problem

The core problem is how to stream data from rpc calls to HTTP/REST or ZeroMQ (an endpoint) and send (messages, events, logs) to an end system like (Kafka, Kinesis, Storm, HDFS ...). We want this to be horizonatally scalable HA and high-performance way that allows for reliable delivery of messages.

Features

Out of the box includes:

  • Data, Log, Event, Message - Ingestion/Streaming
  • Heart Beat
  • Log cleanup
  • Persistent kafka client Realtime streaming logs to Kafka
  • Connection retries when it looses connection to log destination
  • Log persistence if the log producer connection is down
  • Monitoring with Ganglia
  • Heart Alerting with Nagios

Use-Cases

  • Distributed log ingestion to HDFS
  • Distributed event collection
  • Event Aggregation
  • Scalable data ingestion

Table of Contents

  • TODO

Install/Developer Setup

Community

Contributing

See the NeverwinterDP Guide to Contributing

Potential Implementation Strategies

There is a question of how to implement quaranteed delivery of logs to kafka.

  • Implement Avro Protocol?
  • nginx write to ipc pipe -> secondary application that logs to disk and kafka
  • nginx write to zmq -> secondary application that logs to disk and kafka
  • nginx direct kafka driver that also spools to disk

Example Flow

Application sending messages -> Sparkngin [ Nginx -> Zeromq (Publisher) -> Zeromq (Subscriber) -> Kafka (Client call "Producer") ] -> Kafka -> (Client called consumer) -> Some Application

Roadmap

Feature Todo

  • Architecture Proposal
  • Sparkngin -> Zeromq (raw)
  • Sparkngin -> Zeromq (NW protocol V1 - see below)
  • Zeromq -> Kafka
  • Zeromq -> Flume
  • Zeromq -> Syslog
  • Ganglia Integration
  • Nagios Integration
  • Sparkngin Client (raw)
  • Sparkngin Client (NW protocol V1)
  • Heartbeat Agent
  • Unix Man page
  • Guide
  • Build System - cmake or autotools
  • Untar and Deploy - Work out of the box
  • CentOS Package
  • CentOS Repo Setup and Deploy of CentOS Package
  • RHEL Package
  • RHEL Repo Setup and Deploy of CentOS Package
  • Mac DMG
  • ZeroConf system
  • HA logger
  • Log stash integration
  • Elastic search integration
  • Sparkngin/Ambari Deployment
  • Sparkngin/Ambari Monitoring/Ganglia
  • Sparkngin/Ambari Notification/Nagios
  • Event Schema Registration - json, avro, thrift protobuffs
  • Support Json Envelope (NW protocol V2)

NW Protocol V1

Purpose is to provide standard event data that is used to allow for systematic monitoring, analytics, retries and timebased partition notifications (Aka send a message when all data from hour 1 is sent)

  • timestamp
  • ip of referrer
  • topic
  • env
  • version
  • submitted timestamp

Model Project to Eval

Community Threads

Resources to Learn Development Nginx

Example Nginx Modules

Contributors

FAQ

  • Why Sparkngin?

Sparkngin is mean to solve the short coming of realtime event streaming using restful endpoint. Utilizing the logging and other connections in nginx is hard to configure and has limitations.

  • Why trust Sparkngin?

Sparkngin is built on top of two main projects Nginx which is the worlds second most popular web server and Zeromq a high performance networking library. Both provide a very solid core to realtime event streaming. If you have questions about why nginx, click the link. Some people who use it are Facebook, PInterest, Airbnb, Netflix, Hulu and Wordpress among others. Here is a summary of some nginx benefits and features.

  • What is the difference between timestamp and submitted timestamp

The concept is the timestamp is the system timestamp of that machine, which the submitted timestamp is a optional timestamp you might submit in the request. So if for example you have data that you want to submit an hour later, you might want to organize it around a submitted timestamp rather than by the system recorded timestamp of the user.

Design

Architecture

Whole system is consit of two parts:

  1. Nginx module: Core
  2. Adaptors: They can be used to link Sparkngin Nginx module with 3rdparty system, e.g. Kafka, Flume....

Sparkngin Nginx module

  • Init zeromq publisher mode in nginx init master callback
  • Create a shared memory buffer in init master callback, the buffer will be used to cache log data
  • Publish Nginx http log activity data via zeromq
  • If there are not any subscriber, log data will be stored into buffer. The buffer is orgnized as a loop linked list. Oldest log will be overwritten when the buffer is full.

Configuration of Sparkngin Nginx module

  • listen port
  • cache buffer size
  • gzip mode on/off
  • output format: json / plain text / binary
  • output fields customization

HTTP API

  • /log: log event The interface could be used to added log data into log stream.

e.g. /log?level=info&type=http&stimestamp=134545634345&ver=1.0&topic=test&env=testdata&ip=1.1.1.1

  • /status: some statistic results of running which is formated in json
  • /imok: get heart beat signal

Log Field

  • ip
  • timestamp
  • submitted timestamp
  • type
  • level
  • topic
  • env
  • version
  • referrer
  • user agent
  • data which is parsed from cookie

Configuration directives

sparkngin_listen

  • syntax: sparkngin_listen port
  • default: 7000
  • context: http

Set listen port of zeromq publisher.

sparkngin_buf_size

  • syntax: sparkngin_buf_size size
  • default: 4M
  • context: http

Set log data cache buffer size.

sparkngin_gzip

  • syntax: sparkngin_gzip on/off
  • default: off
  • context: http

Set gzip switch on/off.

sparkngin_format

  • syntax: sparkngin_format (json|plain) ['delimiter']
  • default: plain ' '
  • context: http

Set output format:

  • json - log data will be exported in json format
  • plain - log data will be exported in plain text format, each field is sperated by delimiter.

sparkngin_root_loc

  • syntax: sparkngin_root_loc
  • default: ``
  • context: location

Set sparkngin root location.

e.g.

With below configuration, we can access /sn/imok, /sn/stat, /sn/log.

location /sn { sparkngin_root_loc ; }

sparkngin_fields

  • syntax: sparkngin_fields fields list
  • default: %version% %ip% %time_stamp% %level% %topic% %user-agent% %referrer% %cookie%
  • context: http

Set output fields. Below are available fields:

  • version
  • ip
  • time_stamp
  • submitted_timestamp
  • level - info, warnning, error...
  • topic
  • user-agent
  • referrer
  • cookie - whole cookie data
  • cookie_[cooklie_key] - e.g. %cookie_user_id%, will parse 'user_id' value from cookie.
  • env

Log Config Spec

Sample Nginx configuration

worker_processes  2;

events {
    worker_connections  1024;
}

http {
    sparkngin_listen 7000;
    sparkngin_gzip on;
    sparkngin_format json;
    sparkngin_fields %version% %ip% %time_stamp% %level% %topic% %user-agent% %referrer% %cookie%;

    include       mime.types;
    default_type  application/octet-stream;

    sendfile        on;

    keepalive_timeout  65;

    server {
        listen       80;
        server_name  localhost;

        location / {
            root   html;
            index  index.html index.htm;
        }

        location /sparkngin {
            sparkngin_root_loc ;
        }
    }
}

Keep your fork updated

Github Fork a Repo Help

  • Add the remote, call it "upstream":
git remote add upstream git@github.com:DemandCube/Sparkngin.git
  • Fetch all the branches of that remote into remote-tracking branches,
  • such as upstream/master:
git fetch upstream
  • Make sure that you're on your master branch:
git checkout master
  • Merge upstream changes to your master branch
git merge upstream/master
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