Amazon Explains S3 Outage: Gossip Kills

Amazon has released a rather comprehensive write-up on their post-mortem analysis of why Amazon S3 went down last week. The S3 servers use a gossiping protocol to determine system states, including what servers are available and the status of the nodes across the network.

A single bit corrupted in several of these gossips such that they were still intelligible but reflecting inaccurate data about the system state. These propagated through the network (much like a virus, really) and caused most of the servers to spend most of their time gossiping or failing to complete the gossip; if the gossip doesn’t complete, the server can’t/won’t send its data.

While Amazon MD5 checksums data in containers to ensure its integrity as its being transmitted, they weren’t doing this on their gossips. They’ve since established several new practices to attempt to ensure that a problem like this won’t cause a failure across the entire system, including better failure handling with gossips and faster restoration when nodes do go down.

They end their missive simply enough, owning up in a way I give them credit for:

Though we’re proud of our operational performance in operating Amazon S3 for almost 2.5 years, we know that any downtime is unacceptable and we won’t be satisfied until performance is statistically indistinguishable from perfect.

“Statistically indistinguishable from perfect” is a rather poetic phrase, and I’d like to think we strive for that over at Synapse Studios. But my stats-masters programmer would just mock me.

Read their full statement here.

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GigaOM Talks to Amazon’s Jeff Bezos about Amazon Web Services

GigaOM caught up with Amazon’s Jeff Bezos at the D6 conference this week. A lot of people on Wall Street have been struggling to make the somewhat obvious connection between Amazon as a retailer and Amazon as a web service provider. The background to the concept is really pretty self-evident: Amazon needed to develop amazing tools for their own internal scalability and data management needs and in doing so, determined they could scale those tools, make them available to developers for their own applications and commoditize the marketplace. So they did, as we describe after the jump.

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