I think ... - database

HA(High-Availability) Setup for InfluxDB

2018-01-18T00:00:00+06:00

NOTE Since I have written this article, all the components used in this below architecture have gone through many updates and releases. While the general premise involving influxdb-relay and the multiplexing might still hold, please sync up with the latest release docs before jumping into some serious system design.

Currently, from version 0.9, you cannot create an InfluxDB cluster from the open-sourced free edition. Only commercially available InfluxDB Enterprise can do that for now. That stirred up the early-adopter enthusiast users, especially for their usage in professional setups. They complained that InfluxData, the company behind InfluxDB, is trying to milk the OSS solution for profit.

I can’t blame the InfluxData guys much, as they got to pay their bills too. So far, we — the users of open-source systems — couldn’t show much promise about the financial realities of the projects. Continuing development of OSS products, by only depending on donations, patrons, or enterprise sponsorship, is far too rare and unpredictable, even for the projects that many successful organizations heavily rely on.

Anyways, InfluxDB then promised and later introduced Influx Relay as a complimentary consolation for missing HA parts of InfluxDB. You can get the details here and here about that.

Premise¶

For my needs, I have to try to create a reliable HA(High-Availability) setup from available free options, hence InfluxDB and the relay. It’s quite a bit far from an InfluxDB-cluster in terms of robustness or ease of setup, but it’s got the job done, at least for me.

I needed a setup to receive system-stats from at least 500+ instances and to store them for a while, but without breaking the bank in bills from AWS. Meaning, I could ask for and could use only couple of instances for my solution.

Here were my trade-offs.

Not too many instances for this purpose. Neither, any of the heavyweight lifters e.g. AWS’ m3-xlarge etc. To use only what’s necessary.
To satisfy the budget, hence avoiding pay-per-use solutions as far as it is possible.
Solutions must not be crazy complex, so that handover to the DevOps team be smooth.
Reading the data would be too rarely w.r.t. writing. The related Grafana dashboards will be only used to investigate issues by a handful of people.

Overall Design¶

Write¶

From a birds’ eye view, I decided to use two server instances to run parallelly, hosting InfluxDB on them independently and then sending the same data over to them for storing. This scheme mostly looks like RAID-1 systems.

That brings up a couple of challenges.

None of the agents I used on the sender side could multiplex output. That means, they were able to send data to a single destination, not multiple. On the Windows front, I’ve used Telegraf which is able randomly to switch between pre-listed destinations, but NOT multiple at-once.
In the case of Linux hosts, I used Netdata which is excellent in its own right, but unable to send stats to multiple destinations.
Here comes Influx-relay. It can receive time-series data-stream from hosts on a TCP or UDP port, buffer for a while, and then re-send those received and buffered data to multiple receive ends which can either be an InfluxDB instance or another listening Influx-relay instances.
This chaining can broaden the relaying scheme even further. However, for my purpose, this relay-chaining was not necessary. Rather, from the relay, I am sending data to the separate InfluxDB instances, running on two separate instances.
Now that I partially multiplexed the output, my hosts (senders) still are able to send to one destination. So, I need a proxy as well as a load-balancer. For a while, I was torn between NGINX and HAProxy. Both were new to me.

However, for a couple of reasons, I went for HAProxy. Firstly, I don’t need HTTP session management. Secondly, as I wanted to keep my UDP for later, HAProxy was perfectly capable of that.
NGINX has the support recently, but the maturity was a concern. Also, configuring NGINX seems a little intimidating (which I know might not be so true). Last but not least, and for what it’s worth, out-of-the-box, HAProxy’s stat page carries much more in-depth information than that of free-version of NGINX.
Upon receiving the stats stream, HAProxy was supposed to send that to different Influx-relays in a load-balanced fashion.

So, here’s my rough plan.

collector-agent → HAProxy → (50/50 load-balanced) → Influx-relay → (multiplexed) → 2 InfluxDB instances

Now, each one of the received data is to go to both of the InfluxDB instances, or at least to one in case of failure (or, overload per se) of any the relays or Influx instances. Also, I have chosen to keep Influx-relays deployed as Dockerized and kept HAProxy and InfluxDB instances running as native services. Of course, you can Dockerize HAProxy and InfluxDB, too.

Read¶

As I’ve already noted in the section that reading the data, meaning to fetch data to visualize on Grafana end, will happen rarely and sporadically; only to investigate alarms or any other client-side performance issues.

So, the read requests, reaching the HAProxy end, needed not much routing, other than directly to InfluxDB itself. Still, to better distribute the load I decided to load-balance it 50/50 basis.

Ports¶

As all the READ requests are routed through HAProxy running on each of the instances, to the external world only HAProxy’s port should be opened for this purpose.
On the other hand, for WRITE requests, InfluxDBs are receiving data from relays, one of its own instance and another one on other instance, so InfluxDB should listen on its own port for WRITE requests only. But, this must be accessible only from own VPS zone, but not open to the outside world.
In case of HAProxy as well as InfluxDB, you can use the default ports, obviously, which is 8086 & 8088 respectively. Or, you can choose to go for other ports (security through obfuscation). Your call. In this writing, I’ll go with the defaults.

Authentication, SSL¶

You can configure SSL with your own server certificates through the HAProxy configs. You can even go for SSL from the relays to InfluxDB writes. If your sender hosts are connecting to your HAProxy through public internet, you should at least go for password-based authentication, better to utilize SSL. However, for brevity’s sake, I’ll skip them in this post.

**Note: * Please bear in mind, this is an “in-progress” post; prematurely published to force me to work on it. I have the plan to add all the necessary configurations & commands, that I used, here.

Generate ER diagram from a SQL-based database

2014-12-18T00:00:00+06:00

When you are “study”-ing (for whatever reason) someone else’s database, and the database has more than 20 tables, you might be in trouble to understand what’s going where.

Now, imagine a database with 300+ tables. It’s like spaghetti, just not as enjoyable.

I faced a similar challenge recently with a database of 250+ tables. Yes, I felt like in a deep sh*t. And, I started looking for tools that can describe the tables, or at least a decent ER diagram. If anything more, better. And, preferably free.

Then, I found SchemaSpy, originally authored by John Currier. It generates a complete in-depth HTML-based description (of course, including clickable ER-diagram) of the database, which you can then browse with your browser. This post is about its primary usage.

The output will be kind of like this:

It is based on Java, but it can work its magic on most of the major database products. However, it would require an appropriate JDBC connector for that database.

Quote from the author:

SchemaSpy uses JDBC’s database metadata extraction services to gather the majority of its information but has to make vendor-specific SQL queries to gather some information such as the SQL associated with a view and the details of check constraints.

In this post, as an example, I have shown how to use it with PostgreSQL. But, it’s not the only one that’s supported. You can use it with any proper RDBMS system as long as it has a JDBC-connector. Now, to use it, you need to get these stuffs.

First of all, your system should have Java runtime properly installed. Download from here.
SchemaSpy, which is a .jar file. Get it here. At the time of writing, it was version 5.0.0.
JDBC connector to PostgreSQL. Make sure to match your PostgreSQL version. You can download it from here.
You can check your PostgreSQL version by executing: SELECT version(); query on psql prompt.
Also, SchemaSpy depends on GarphViz to generate the ER diagrams, so you need to be installed it on your system. Get it from here.(http://www.graphviz.org/Download..php)
And, of course, make sure the target database instance is running & serving the database you are trying to visualize.

Quoting from the author:

SchemaSpy uses the dot executable from Graphviz to generate graphical representations of the table/view relationships. The visual representation of the connections is a fundamental feature of the tool.

Graphviz is not required to view the output generated by SchemaSpy, but the dot program should be in your PATH (not CLASSPATH) when running SchemaSpy, or none of the entity-relationship diagrams will be generated. Or, maybe use the -gv option).

I kept the .jar files (both the JDBC-connector and the SchemaSpy) in my home folder for convenience.
Now, in my case, my OS is Linux, and the database is hosted locally; hence address is 127.0.0.1, running PostgreSQL-9.3 at port 5432. So, I run the command like this:

 $ java -jar ./schemaSpy_5.0.0.jar -t pgsql -host 127.0.0.1:5432 -db your_database_name \
                                   -u your_DB_user_name -p your_password -s public \
                                   -dp ./postgresql-9.3-1102.jdbc3.jar \
                                   -o output_folder

It may take a little while, depending on the size of the schema of the database.
After that, you will find the output folder/directory named output_folder.
You’ll see some output when the magic is going on, similar to this below.

Using database properties: [./schemaSpy_5.0.0.jar]/net/sourceforge/schemaspy/dbTypes/pgsql.properties
Gathering schema details....................(6sec)
Writing/graphing summary....................(2sec)
Writing/diagramming detail..................(31sec)
Wrote relationship details of 113 tables/views to directory 'output' in 41 seconds.

View the results by opening output_folder/index.html

Now, all the generated files are in the output_folder. Start your journey by starting from the index.html in the output folder. Open it by using any browser.

Good luck. :)

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