PostgreSQL Connection Pooling: Part 1 – Pros & Cons

A long time ago, in a galaxy far far away, ‘threads’ were a programming novelty rarely used and seldom trusted. In that environment, the first PostgreSQL developers decided forking a process for each connection to the database is the safest choice. It would be a shame if your database crashed, after all.

Since then, a lot of water has flown under that bridge, but the PostgreSQL community has stuck by their original decision. It is difficult to fault their argument – as it’s absolutely true that:

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MySQL High Availability Framework Explained – Part III: Failover Scenarios

In this three-part blog series, we introduced a High Availability (HA) Framework for MySQL hosting in Part I, and discussed the details of MySQL semisynchronous replication in Part II. Now in Part III, we review how the framework handles some of the important MySQL failure scenarios and recovers to ensure high availability.

MySQL Failover Scenarios

Scenario 1 – Master MySQL Goes Down

• The Corosync and Pacemaker framework detects that the master MySQL is no longer available. Pacemaker demotes the master resource and tries to recover with a restart of the MySQL service, if possible.
• At this point, due to the semisynchronous nature of the replication, all transactions committed on the master have been received by at least one of the slaves.
• Pacemaker waits until all the received transactions are applied on the slaves and lets the slaves report their promotion scores. The score calculation is done in such a way that the score is ‘0’ if a slave is completely in sync with the master, and is a negative number otherwise.
• Pacemaker picks the slave that has reported the 0 score and promotes that slave which now assumes the role of master MySQL on which writes are allowed.
• After slave promotion, the Resource Agent triggers a DNS rerouting module. The module updates the proxy DNS entry with the IP address of the new master, thus, facilitating all application writes to be redirected to the new master.
• Pacemaker also sets up the available slaves to start replicating from this new master.

Thus, whenever a master MySQL goes down (whether due to a MySQL crash, OS crash, system reboot, etc.), our HA framework detects it and promotes a suitable slave to take over the role of the master. This ensures that the system continues to be available to the applications.

Scenario 2 – Slave MySQL Goes Down

• The Corosync and Pacemaker framework detects that the slave MySQL is no longer available.
• Pacemaker tries to recover the resource by trying to restart MySQL on the node. If it comes up, it is added back to the current master as a slave and replication continues.
• If recovery fails, Pacemaker reports that resource as down – based on which alerts or notifications can be generated. If necessary, the ScaleGrid support team will handle the recovery of this node.
• In this case, there is no impact on the availability of MySQL services.

Scenario 3 – Network Partition – Network Connectivity Breaks Down Between Master and Slave Nodes

This is a classical problem in any distributed system where each node thinks the other nodes are down, while in reality, only the network communication between the nodes is broken. This scenario is more commonly known as split-brain scenario, and if not handled properly, can lead to more than one node claiming to be a master MySQL which in turn leads to data inconsistencies and corruption.

Let’s use an example to review how our framework deals with split-brain scenarios in the cluster. We assume that due to network issues, the cluster has partitioned into two groups – master in one group and 2 slaves in the other group, and we will denote this as [(M), (S1,S2)].

• Corosync detects that the master node is not able to communicate with the slave nodes, and the slave nodes can communicate with each other, but not with the master.
• The master node will not be able to commit any transactions as the semisynchronous replication expects acknowledgement from at least one of the slaves before the master can commit. At the same time, Pacemaker shuts down MySQL on the master node due to lack of quorum based on the Pacemaker setting ‘no-quorum-policy = stop’. Quorum here means a majority of the nodes, or two out of three in a 3-node cluster setup. Since there is only one master node running in this partition of the cluster, the no-quorum-policy setting is triggered leading to the shutdown of the MySQL master.
• Now, Pacemaker on the partition [(S1), (S2)] detects that there is no master available in the cluster and initiates a promotion process. Assuming that S1 is up to date with the master (as guaranteed by semisynchronous replication), it is then promoted as the new master.
• Application traffic will be redirected to this new master MySQL node and the slave S2 will start replicating from the new master.

Thus, we see that the MySQL HA framework handles split-brain scenarios effectively, ensuring both data consistency and availability in the event the network connectivity breaks between master and slave nodes.

This concludes our 3-part blog series on the MySQL High Availability (HA) framework using semisynchronous replication and the Corosync plus Pacemaker stack. At ScaleGrid, we offer highly available hosting for MySQL on AWS and MySQL on Azure that is implemented based on the concepts explained in this blog series. Please visit the ScaleGrid Console for a free trial of our solutions.

Did the Microsoft Stack Kill MySpace?

Robert Scoble wrote a fascinating case study, MySpace’s death spiral: insiders say it’s due to bets on Los Angeles and Microsoft, where he reports MySpace insiders blame the Microsoft stack on why they lost the great social network race to Facebook.  

Does anyone know if this is true? What's the real story?

I was wondering because it doesn't seem to track with the MySpace Architecture post that I did in 2009, where they seem happy with their choices and had stats to back up their improvements. Why this matters is it's a fascinating model for startups to learn from. What does it really take to succeed? Is it the people or the stack? Is it the organization or the technology? Is it the process or the competition? Is the quality of the site or the love of the users? So much to consider and learn from.

Some conjectures from the article:

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When things aren't scalable

OK, I know this site is for scalable web site design. But as there aren't any sites I can find for graceful failure under "slashdotted" like pressure I'll ask here. Does anyone have a sensible way, once you have a "web application" that either won't scale, or can't scale, that you can give some users a good consistent experience and bounce other users to a busy site page. I have seen sites do this to varying degrees, some of which work better than others, but no explanations beyond simply bouncing requests to a "we're busy page server" when you have more than a given number of connections. This is obviously useless as a web page likely requires multiple connection (ignoring keep-alive, pipelining etc) multiple connection to completely render properly. The normal problem is users getting a page and not the "furniture" for that page like images or css. Other problems are having to wait ages to get the busy page or the site being slow even if you do "get in". And some site let a user "in" and then as they browse around they get bounced out suddenly to the busy page. Obviously not being the developer for sites I deal with (I am an infrastructure bod) I can't solve the problem where it should have been pre-emptively solved. That is to say I can't write the code to be scalable or re-write the code to do some simple session filtering or the like (and not being a developer I get dirty looks when I point developers at information like your site ... I can hear them thinking "how dare you suggest I don't know how to code a web site you lowly infrastructure cretin"). Before developer on-line lynch me I should point out that sometimes the cause of not being able to scale a site is that I can't get in new hardware quick enough, but then who knows when you will get slashdotted right ?. So my question applies even when a developer of genius level brilliance has built a unsurpasibly scalable web site for me to run the infrastructure for. My best guess so far is using something like HAProxy to load balance sessions, and then use it's more advanced total session count, and cookie issuing abilities to track users and bounce some at a given "heavy load" point. This isn't ideal as the heavy load point would have to be based on connection counts not server load or server response times, but it's the best I can come up with so far. Also, having mentioned brilliant developers writing great sites not always making my question redundant, could I ask, do people normally think about coping with overload when designing scalable solution - surely they should but I don't see much talk about it. Couldn't a simple Java filter or the equivalent for other things be built into applications ? It'd be nice to have a site that not only scales, but "is nice" when waiting for the infrastructure it runs on to be scaled, which could be several days when you have to purchase new hardware.

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Friendster Lost Lead Because of a Failure to Scale

Hey, this scaling stuff might just be important. Jim Scheinman, former Bebo and Friendster exec, puts the blame squarely on Friendster's inability to scale as why they lost the social networking race: VB: Can you tell me a bit about what you learned in your time at Friendster? JS: For me, it basically came down to failed execution on the technology side — we had millions of Friendster members begging us to get the site working faster so they could log in and spend hours social networking with their friends. I remember coming in to the office for months reading thousands of customer service emails telling us that if we didn’t get our site working better soon, they’d be ‘forced to join’ a new social networking site that had just launched called MySpace…the rest is history. To be fair to Friendster’s technology team at the time, they were on the forefront of many new scaling and database issues that web sites simply hadn’t had to deal with prior to Friendster. As is often the case, the early pioneer made critical mistakes that enabled later entrants to the market, MySpace, Facebook & Bebo to learn and excel. As a postscript to the story, it’s interesting to note that Kent Lindstrom (CEO of Friendster) and the rest of the team have done an outstanding job righting that ship. Hopefully with all the quality information out now on the intertubes visionaries can concentrate on making good stuff instead of always fighting the plumbing. When you think about, is there any industry or group that gives so much value away for free as the software community? I don't think so. We are an amazingly giving group and the world has benefited greatly from that impulse. A thought for Thanksgiving.

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