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0 pointsmoonpolysoft15y ago0 comments

> Solving the problems of distributed systems is incredibly hard and not necessary if all you need is scalability and high availability.

Once one scales beyond a single node the system becomes distributed. Then by definition one must deal with distributed systems problems in order to achieve scale beyond the capabilities of a single node.

> When you decide to give up consistency, your application can no longer assume consistency ever. Giving up availability in case of network partition means few extra minutes of downtime a year.

Depends on the application. Giving up availability might mean cascading failures throughout your entire application. For instance if the datastore is unavailable for writes then any kind of queueing systems built around the DB (a common design pattern) run the risk of overflow during the downtime.

And I would make the argument that once an application scales beyond a single datacenter it cannot help but give up strict consistency under error conditions.

> I don't think he completely misunderstood distributed systems - I think he decided to completely side-step the entire field.

If he didn't misunderstand them then he is purposefully ignoring the hard problems. Which is worse?

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6 comments · 1 top-level

ora60015y ago· 5 in thread

> Once one scales beyond a single node the system becomes distributed. Then by definition one must deal with distributed systems problems in order to achieve scale beyond the capabilities of a single node

I meant that one doesn't need to solve the general problem of distributed systems. Sharding is a common way to scale avoiding most of the problems generally associated with distributed systems. Scaling within LAN is easier than across data centers. You can assume no malicious traffic between your servers and suddenly solving the byzantine generals problem is far easier.

Purposefully ignoring really hard problems can be a very good engineering practice.

benblack15y ago

I purposefully ignored the tornado and so it did not hit my datacenter, tear off a section of the roof, kill all power sources, and drench my servers. Hard problems: solved.

sophacles15y ago

Thanks! My app was in that datacenter too. I mean, it had replicated mongodb instances, and well balanced app servers, and nodes going away have no discernable affect on users. Turns out tho, that with all that distributed engineering, I didn't find out that the hosting company doesn't put your nodes in various data-centers. That tornado would have taken down my service during peak hours.

I know you will try to write that off as a "you get what you diserve" but I challenge you to go ask people if their apps would survive a tornado to the data-center. Many of them will say "sure its in the cloud!" Then drop the killer question on them... "How many different data centers are your nodes running on right now". Most will say "i dont know". Some will say "My host has many data centers" (note this doesn't answer the question). A few will actually have done the footwork.

Also, the scenario you describe is as easily mitigated with hot failovers and offsite backups. This probably qualifies as distributed engineering, but only is only the same as the above discussions in the most pedantic senses.

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ieure15y ago

Cool story, bro.

moonpolysoftOP15y ago

Sharding puts you in the same risk category as any other distributed database. Just ask the engineers and ops people at twitter, foursquare, and any number of other web companies that have dealt with sharded databases at scale. Also: sharding is eventually consistent, except you don't get any distributed counter primitives to help figure out which replica is telling the truth.

jhugg15y ago

There's a difference between what ops people see and what developers see. If the ops people have the same headache no matter what, why force the developers to think about consistency?

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