I'm glossing over their documentation here :
http://www.rubydoc.info/github/github/statsd-ruby/Statsd
And there's methods for recording data, but I can't seem to find anything about retrieving recorded data. I'm adopting a projecting with an existing statsd addition. It's host is likely a defunct URL. Perhaps, is the host where those stats are recorded?
The statsd server implementations that Mircea links just take care of receiving, aggregating metrics and publishing them to a backend service. Etsy's statsd definition (bold is mine):
A network daemon that runs on the Node.js platform and listens for
statistics, like counters and timers, sent over UDP or TCP and sends
aggregates to one or more pluggable backend services (e.g.,
Graphite).
To retrieve the recorded data you have to query the backend. Check the list of available backends. The most common one is Graphite.
See also this question: How does StatsD store its data?
There are 2 parts to statsd: a client and a server.
What you're looking at is the client part. You will not see functionality related to retrieving the data as it's not there - it normally is on the server side.
Here is a list of statsd server implementations:
http://www.joemiller.me/2011/09/21/list-of-statsd-server-implementations/
Research and pick one that fits your needs.
Statsd originally started at etsy: https://github.com/etsy/statsd/wiki
Related
I've used influxDB for a while now, but never had to continuously stream data from it. A simple GET /query was sufficient. But now I need a way to stream data to the frontend to draw pretty graphs and such.
So far we've been running GET /query periodically from the frontend, but this is highly inefficient. I would much rather get keep the connection open and receive the data when it's written to the DB. Searching the interwebs there doesn't seem to be support neither for websockets, nor for HTTP/2 in influxDB right now.
So, question to others, who possibly hit this issue - how did you solve this?
InfluxDB v1.x supports subscriptions. As data is written to InfluxDB, writes are duplicated to subscriber endpoints via HTTP, HTTPS, or UDP in line protocol.
https://docs.influxdata.com/influxdb/v1.7/administration/subscription-management/
I am implementing a robotic system based on ROS. I have different nodes which send data multiple times per second. However, I don't need that. I want to send the robot state only when it is at a new location. What technique of ROS do you suggest to use?
Dependent on your requirements, you can either use the ROS Services or the Parameter Server.
ROS Service: The publish / subscribe model is a very flexible
communication paradigm, but its many-to-many one-way transport is not
appropriate for RPC request / reply interactions, which are often
required in a distributed system. Request / reply is done via a
Service, which is defined by a pair of messages: one for the request
and one for the reply.
Parameter Server: A parameter server is a shared, multi-variate dictionary that is accessible via network APIs. Nodes use this server
to store and retrieve parameters at runtime. As it is not designed for
high-performance, it is best used for static, non-binary data such as
configuration parameters.
I have an application that I wish to monitor graphically.
I am using this StatsD client. I am using Graphite as the backend. I have a question about the basic workflow:
We use the StatsD client in order to include metrics within our application. These metrics are then sent in the form of UDP packets (usually). Graphite (specifically Carbon within Graphite) captures these packets and stores them in the Whisper database as time-series data.
What exactly then, is the role of the StatsD daemon? I have written a working application using only the StatsD client and Graphite. Where am I missing the usage of StatsD daemon?
Had the same question, so I'm going to answer it here even thogh the post is 7 months old.
From what I could gather (as explained here), a StatsD Deamon is synonymous to a StatsD Server. In your case, it's Carbon/Graphite or maybe a StatsD specific component within your Graphite Stack.
In my company, for instance, we use the StatsD Beats Daemon within the ELK-Stack.
I'm a bit confused about Kafka architecture. We would like to capture Twitter Streaming API. We came across this https://github.com/NFLabs/kafka-twitter/blob/master/src/main/java/com/nflabs/peloton2/kafka/producer/TwitterProducer.java Twitter Producer.
What I'm thinking about is how to design the system so it's fault tolerant.
If the producer goes down, does it mean we lose some of the data? How to prevent this from happening?
If the producer you linked to stops running, new data from the Twitter API will not make its way into Kafka. I'm not sure how the Twitter Streaming API works, but it may be possible to get historic data, allowing you to fetch all data back to the point when the producer failed.
Another option is to use Kafka Connect, which is a distributed, fault tolerant service for connecting data sources and sinks to Kafka. Connect exposes a higher-level API and uses the out-of-the-box producer/consumer API behind the scenes. The documentation explains Connect very thoroughly, so give that a read and go from there.
Just asking one silly question, hope someone can answer this.
I'm bit confused regarding MQTT broker. Basically, the confusion is, there are so many things being used for data storing, transfer and processing (like Flume, HDInsight, Spark etc). So, when and why I need to use one MQTT broker?
If I would like to use Windows 10 IoT application with HiveMQ, from where can I get the details? how to use it? How I get benefit out of this MQTT broker? Can I not send data from my IoT application directly using Azure or HDFS? So, how MQTT broker fits into it or helping me to achieve something?
I'm new to all these and tried to find some tutorials, however, I'm not getting anything proper. Please explain it in more details or give some tutorials for this?
MQTT is a client-server protocol for pub-sub based transport that has a comparatively small overhead, and thus applicable to mobile and IoT applications (unlike Flume, etc.). The MQTT broker is basically a server that handles messaging to/from MQTT clients and among them. The functionality pretty much stops at the transport layer, even though various MQTT add-ons exist.
If you are looking to implement a solution that would reliably transfer data from your IoT devices to the back-end system for processing, I would suggest you take a look into Kaa open-source IoT platform. It goes much further than MQTT by providing not only the transport layer, suitable for low-power IoT devices, but also a solid chunk of the application level logic (including the object bindings for your application-level data structures, temporary data persistence, etc.).
Here is a link to a webinar that explains how to build a scalable IoT analytics system with Kaa and Spark in less than an hour.
This is an architectural choice. IoT applications are possible without MQTT but there are some advantages when using MQTT. If you are completely new to MQTT, take a look at this in-depth MQTT series: http://forkbomb-blog.de/2015/all-you-need-to-know-about-mqtt
Basically the main architectural advantage is publish / subscribe designed for low-latency, high throughput (mobile) communication with minimal protocol overhead (which is important if bandwidth is at a premium). You can completely decouple consumers and producers.
HDFS is the (distributed) Hadoop file system and is the foundation for Map / Reduce processing. It is not comparable to a MQTT broker. The MQTT broker could write to the HDFS, though (in case of HiveMQ with a custom plugin).
Basically MQTT is a protocol while the products you are mentioning are, well, products which solve completely different problems:
Flume is basically used for log aggregation at scale. You won't use MQTT for that, at least there is not too much advantage because this is typically done in backend applications.
Spark and Hadoop shine at Big Data crunching. They are a framework and not a ready to use solution. They are not really comparable to MQTT. Often MQTT brokers like HiveMQ are used in conjunction with these, Spark / Hadoop for data processing and HiveMQ for communication.
I hope this helps you getting started. Best would be to read about typical use cases of all these technologies, this is a bit too broad for a single SO answer.
MQTT is a data transport, so the usual thing I have to compare it with is HTTP. HTTP has two important characteristics, a) It goes from one point to another, b) It is request/response, so only one end can start a data transfer. MQTT connects many end points to many end points, and either end can start a data transfer. So, if you have just one device and only one service or person that will ever access it, and only by polling, then HTTP is great. MQTT means many devices can post data to many services or people, AND the other way around. Your question assumes that your data is always going to land up in some sort of data store, but many interactions are about events and responding to them immediately, like ringing a doorbell, or lowering the landing gear. In these cases you will often want to both record the data, and have an immediate action occur, like your phone making a doorbell noise.
Finally, you send data to MQTT semantically, rather than by IP address.
This means that your services subscribes to /mikeshouse/doorbell rather than polling 192.168.22.4, which is a huge gain once you have a number of devices.