Full Width [alt+shift+f] Shortcuts [alt+shift+k]
Sign Up [alt+shift+s] Log In [alt+shift+l]

Improve your reading experience

Logged in users get linked directly to articles resulting in a better reading experience. Please login for free, it takes less than 1 minute.

More from Jaz's Blog

When Imperfect Systems are Good, Actually: Bluesky's Lossy Timelines

Often when designing systems, we aim for perfection in things like consistency of data, availability, latency, and more. The hardest part of system design is that it’s difficult (if not impossible) to design systems that have perfect consistency, perfect availability, incredibly low latency, and incredibly high throughput, all at the same time. Instead, when we approach system design, it’s best to treat each of these properties as points on different axes that we balance to find the “right fit” for the application we’re supporting. I recently made some major tradeoffs in the design of Bluesky’s Following Feed/Timeline to improve the performance of writes at the cost of consistency in a way that doesn’t negatively affect users but reduced P99s by over 96%. Timeline Fanout When you make a post on Bluesky, your post is indexed by our systems and persisted to a database where we can fetch it to hydrate and serve in API responses. Additionally, a reference to your post is “fanned out” to your followers so they can see it in their Timelines. This process involves looking up all of your followers, then inserting a new row into each of their Timeline tables in reverse chronological order with a reference to your post. When a user loads their Timeline, we fetch a page of post references and then hydrate the posts/actors concurrently to quickly build an API response and let them see the latest content from people they follow. The Timelines table is sharded by user. This means each user gets their own Timeline partition, randomly distributed among shards of our horizontally scalable database (ScyllaDB), replicated across multiple shards for high availability. Timelines are regularly trimmed when written to, keeping them near a target length and dropping older post references to conserve space. Hot Shards in Your Area Bluesky currently has around 32 Million Users and our Timelines database is broken into hundreds of shards. To support millions of partitions on such a small number of shards, each user’s Timeline partition is colocated with tens of thousands of other users’ Timelines. Under normal circumstances with all users behaving well, this doesn’t present a problem as the work of an individual Timeline is small enough that a shard can handle the work of tens of thousands of them without being heavily taxed. Unfortunately, with a large number of users, some of them will do abnormal things like… well… following hundreds of thousands of other users. Generally, this can be dealt with via policy and moderation to prevent abusive users from causing outsized load on systems, but these processes take time and can be imperfect. When a user follows hundreds of thousands of others, their Timeline becomes hyperactive with writes and trimming occurring at massively elevated rates. This load slows down the individual operations to the user’s Timeline, which is fine for the bad behaving user, but causes problems to the tens of thousands of other users sharing a shard with them. We typically call this situation a “Hot Shard”: where some resident of a shard has “hot” data that is being written to or read from at much higher rates than others. Since the data on the shard is only replicated a few times, we can’t effectively leverage the horizontal scale of our database to process all this additional work. Instead, the “Hot Shard” ends up spending so much time doing work for a single partition that operations to the colocated partitions slow down as well. Stacking Latencies Returning to our Fanout process, let’s consider the case of Fanout for a user followed by 2,000,000 other users. Under normal circumstances, writing to a single Timeline takes an average of ~600 microseconds. If we sequentially write to the Timelines of our user’s followers, we’ll be sitting around for 20 minutes at best to Fanout this post. If instead we concurrently Fanout to 1,000 Timelines at once, we can complete this Fanout job in ~1.2 seconds. That sounds great, except it oversimplifies an important property of systems: tail latencies. The average latency of a write is ~600 microseconds, but some writes take much less time and some take much more. In fact, the P99 latency of writes to the Timelines cluster can be as high as 15 milliseconds! What does this mean for our Fanout? Well, if we concurrently write to 1,000 Timelines at once, statistically we’ll see 10 writes as slow as or slower than 15 milliseconds. In the case of timelines, each “page” of followers is 10,000 users large and each “page” must be fanned out before we fetch the next page. This means that our slowest writes will hold up the fetching and Fanout of the next page. How does this affect our expected Fanout time? Each “page” will have ~100 writes as slow as or slower than the P99 latency. If we get unlucky, they could all stack up on a single routine and end up slowing down a single page of Fanout to 1.5 seconds. In the worst case, for our 2,000,000 Follower celebrity, their post Fanout could end up taking as long as 5 minutes! That’s not even considering P99.9 and P99.99 latencies which could end up being >1 second, which could leave us waiting tens of minutes for our Fanout job. Now imagine how bad this would be for a user with 20,000,000+ Followers! So, how do we fix the problem? By embracing imperfection, of course! Lossy Timelines Imagine a user who follows hundreds of thousands of others. Their Timeline is being written to hundreds of times a second, moving so fast it would be humanly impossible to keep up with the entirety of their Timeline even if it was their full-time job. For a given user, there’s a threshold beyond which it is unreasonable for them to be able to keep up with their Timeline. Beyond this point, they likely consume content through various other feeds and do not primarily use their Following Feed. Additionally, beyond this point, it is reasonable for us to not necessarily have a perfect chronology of everything posted by the many thousands of users they follow, but provide enough content that the Timeline always has something new. Note in this case I’m using the term “reasonable” to loosely convey that as a social media service, there must be a limit to the amount of work we are expected to do for a single user. What if we introduce a mechanism to reduce the correctness of a Timeline such that there is a limit to the amount of work a single Timeline can place on a DB shard. We can assert a reasonable limit for the number of follows a user should have to have a healthy and active Timeline, then increase the “lossiness” of their Timeline the further past that limit they go. A loss_factor can be defined as min(reasonable_limit/num_follows, 1) and can be used to probabilistically drop writes to a Timeline to prevent hot shards. Just before writing a page in Fanout, we can generate a random float between 0 and 1, then compare it to the loss_factor of each user in the page. If the user’s loss_factor is smaller than the generated float, we filter the user out of the page and don’t write to their Timeline. Now, users all have the same number of “follows worth” of Fanout. For example with a reasonable_limit of 2,000, a user who follows 4,000 others will have a loss_factor of 0.5 meaning half the writes to their Timeline will get dropped. For a user following 8,000 others, their loss factor of 0.25 will drop 75% of writes to their Timeline. Thus, each user has a effective ceiling on the amount of Fanout work done for their Timeline. By specifying the limits of reasonable user behavior and embracing imperfection for users who go beyond it, we can continue to provide service that meets the expectations of users without sacrificing scalability of the system. Aside on Caching We write to Timelines at a rate of more than one million times a second during the busy parts of the day. Looking up the number of follows of a given user before fanning out to them would require more than one million additional reads per second to our primary database cluster. This additional load would not be well received by our database and the additional cost wouldn’t be worth the payoff for faster Timeline Fanout. Instead, we implemented an approach that caches high-follow accounts in a Redis sorted set, then each instance of our Fanout service loads an updated version of the set into memory every 30 seconds. This allows us to perform lookups of follow counts for high-follow accounts millions of times per second per Fanount service instance. By caching values which don’t need to be perfect to function correctly in this case, we can once again embrace imperfection in the system to improve performance and scalability without compromising the function of the service. Results We implemented Lossy Timelines a few weeks ago on our production systems and saw a dramatic reduction in hot shards on the Timelines database clusters. In fact, there now appear to be no hot shards in the cluster at all, and the P99 of a page of Fanout work has been reduced by over 90%. Additionally, with the reduction in write P99s, the P99 duration for a full post Fanout has been reduced by over 96%. Jobs that used to take 5-10 minutes for large accounts now take <10 seconds. Knowing where it’s okay to be imperfect lets you trade consistency for other desirable aspects of your systems and scale ever higher. There are plenty of other places for improvement in our Timelines architecture, but this step was a big one towards improving throughput and scalability of Bluesky’s Timelines. If you’re interested in these sorts of problems and would like to help us build the core data services that power Bluesky, check out this job listing. If you’re interested in other open positions at Bluesky, you can find them here.

2 months ago 21 votes
Jetstream: Shrinking the AT Proto Firehose by >99%

Bluesky recently saw a massive spike in activity in response to Brazil’s ban of Twitter. As a result, the AT Proto event firehose provided by Bluesky’s Relay at bsky.network has increased in volume by a huge amount. The average event rate during this surge increased by ~1,300%. Before this new surge in activity, the firehose would produce around 24 GB/day of traffic. After the surge, this volume jumped to over 232 GB/day! Keeping up with the full, verified firehose quickly became less practical on cheap cloud infrastructure with metered bandwidth. To help reduce the burden of operating bots, feed generators, labelers, and other non-verifying AT Proto services, I built Jetstream as an alternative, lightweight, filterable JSON firehose for AT Proto. How the Firehose Works The AT Proto firehose is a mechanism used to keep verified, fully synced copies of the repos of all users. Since repos are represented as Merkle Search Trees, each firehose event contains an update to the user’s MST which includes all the changed blocks (nodes in the path from the root to the modified leaf). The root of this path is signed by the repo owner, and a consumer can keep their copy of the repo’s MST up-to-date by applying the diff in the event. For a more in-depth explanation of how Merkle Trees are constructed, check out this explainer. Practically, this means that for every small JSON record added to a repo, we also send along some number of MST blocks (which are content-addressed hashes and thus very information-dense) that are mostly useful for consumers attempting to keep a fully synced, verified copy of the repo. You can think of this as the difference between cloning a git repo v.s. just grabbing the latest version of the files without the .git folder. In this case, the firehose effectively streams the diffs for the repository with commits, signatures, and metadata, which is inherently heavier than a point-in-time checkout of the repo. Because firehose events with repo updates are signed by the repo owner, they allow a consumer to process events from any operator without having to trust the messenger. This is the “Authenticated” part of the Authenticated Transfer (AT) Protocol and is crucial to the correct functioning of the network. That being said, of the hundreds of consumers of Bluesky’s production Relay, >90% of them are building feeds, bots, and other tools that don’t keep full copies of the entire network and don’t verify MST operations at all. For these consumers, all they actually process is the JSON records created, updated, and deleted in each event. If consumers already trust the provider to do validation on their end, they could get by with a much more lightweight data stream. How Jetstream Works Jetstream is a streaming service that consumes an AT Proto com.atproto.sync.subscribeRepos stream and converts it into lightweight, friendly JSON. If you want to try it out yourself, you can connect to my public Jetstream instance and view all posts on Bluesky in realtime: $ websocat "wss://jetstream2.us-east.bsky.network/subscribe?wantedCollections=app.bsky.feed.post" Note: the above instance is operated by Bluesky PBC and is free to use, more instances are listed in the official repo Readme Jetstream converts the CBOR-encoded MST blocks produced by the AT Proto firehose and translates them into JSON objects that are easier to interface with using standard tooling available in programming languages. Since Repo MSTs only contain records in their leaf nodes, this means Jetstream can drop all of the blocks in an event except for those of the leaf nodes, typically leaving only one block per event. In reality, this means that Jetstream’s JSON firehose is nearly 1/10 the size of the full protocol firehose for the same events, but lacks the verifiability and signatures included in the protocol-level firehose. Jetstream events end up looking something like: { "did": "did:plc:eygmaihciaxprqvxpfvl6flk", "time_us": 1725911162329308, "type": "com", "commit": { "rev": "3l3qo2vutsw2b", "type": "c", "collection": "app.bsky.feed.like", "rkey": "3l3qo2vuowo2b", "record": { "$type": "app.bsky.feed.like", "createdAt": "2024-09-09T19:46:02.102Z", "subject": { "cid": "bafyreidc6sydkkbchcyg62v77wbhzvb2mvytlmsychqgwf2xojjtirmzj4", "uri": "at://did:plc:wa7b35aakoll7hugkrjtf3xf/app.bsky.feed.post/3l3pte3p2e325" } }, "cid": "bafyreidwaivazkwu67xztlmuobx35hs2lnfh3kolmgfmucldvhd3sgzcqi" } } Each event lets you know the DID of the repo it applies to, when it was seen by Jetstream (a time-based cursor), and up to one updated repo record as serialized JSON. Check out this 10 second CPU profile of Jetstream serving 200k evt/sec to a local consumer: By dropping the MST and verification overhead by consuming from relay we trust, we’ve reduced the size of a firehose of all events on the network from 232 GB/day to ~41GB/day, but we can do better. Jetstream and zstd I recently read a great engineering blog from Discord about their use of zstd to compress websocket traffic to/from their Gateway service and client applications. Since Jetstream emits marshalled JSON through the websocket for developer-friendliness, I figured it might be a neat idea to see if we could get further bandwidth reduction by employing zstd to compress events we send to consumers. zstd has two basic operating modes, “simple” mode and “streaming” mode. Streaming Compression At first glance, streaming mode seems like it’d be a great fit. We’ve got a websocket connection with a consumer and streaming mode allows the compression to get more efficient over the lifetime of the connection. I went and implemented a streaming compression version of Jetstream where a consumer can request compression when connecting and will get zstd compressed JSON sent as binary messages over the socket instead of plaintext. Unfortunately, this had a massive impact on Jetstream’s server-side CPU utilization. We were effectively compressing every message once per consumer as part of their streaming session. This was not a scalable approach to offering compression on Jetstream. Additionally, Jetstream stores a buffer of the past 24 hours (configurable) of events on disk in PebbleDB to allow consumers to replay events before getting transitioned into live-tailing mode. Jetstream stores serialized JSON in the DB, so playback is just shuffling the bytes into the websocket without having to round-trip the data into a Go struct. When we layer in streaming compression, playback becomes significantly more expensive because we have to compress outgoing events on-the-fly for a consumer that’s catching up. In real numbers, this increased CPU usage of Jetstream by 23% while lowering the throughput of playback from ~200k evt/sec to ~28k evt/sec for a single local consumer. When in streaming mode, we can’t leverage the bytes we compress for one consumer and reuse them for another consumer because zstd’s streaming context window may not be in sync between the two consumers. They haven’t received exactly the same data in the session so the clients on the other end don’t have their state machines in the same state. Since streaming mode’s primary advantage is giving us eventually better efficiency as the encoder learns about the data, what if we just taught the encoder about the data at the start and compress each message statelessly? Dictionary Mode zstd offers a mechanism for initializing an encoder/decoder with pre-optimized settings by providing a dictionary trained on a sample of the data you’ll be encoding/decoding. Using this dictionary, zstd essentially uses it’s smallest encoded representations for the most frequently seen patterns in the sample data. In our case, where we’re compressing serialized JSON with a common event shape and lots of common property names, training a dictionary on a large number of real events should allow us to represent the common elements among messages in the smallest number of bytes. For take two of Jetstream with zstd, let’s to use a single encoder for the whole service that utilizes a custom dictionary trained on 100,000 real events. We can use this encoder to compress every event as we see it, before persisting and emitting it to consumers. Now we end up with two copies of every event, one that’s just serialized JSON, and one that’s statelessly compressed to zstd using our dictionary. Any consumers that want compression can have a copy of the dictionary on their end to initialize a decoder, then when we broadcast the shared compressed event, all consumers can read it without any state or context issues. This requires the consumers and server to have a pre-shared dictionary, which is a major drawback of this implementation but good enough for our purposes. That leaves the problem of event playback for compression-enabled clients. An easy solution here is to just store the compressed events as well! Since we’re only sticking the JSON records into our PebbleDB, the actual size of the 24 hour playback window is <8GB with sstable compression. If we store a copy of the JSON serialized event and a copy of the zstd compressed event, this will, at most, double our storage requirements. Then during playback, if the consumer requests compression, we can just shuffle bytes out of the compressed version of the DB into their socket instead of having to move it through a zstd encoder. Savings Running with a custom dictionary, I was able to get the average Jetstream event down from 482 bytes to just 211 bytes (~0.44 compression ratio). Jetstream allows us to live tail all posts on Bluesky as they’re posted for as little as ~850 MB/day, and we could keep up with all events moving through the firehose during the Brazil Twitter Exodus weekend for 18GB/day (down from 232GB/day). With this scheme, Jetstream is required to compress each event only once before persisting it to disk and emitting it to connected consumers. The CPU impact of these changes is significant in proportion to Jetstream’s incredibly light load but it’s a flat cost we pay once no matter how many consumers we have. (CPU profile from a 30 second pprof sample with 12 consumers live-tailing Jetstream) Additionally, with Jetstream’s shared buffer broadcast architecture, we keep memory allocations incredibly low and the cost per consumer on CPU and RAM is trivial. In the allocation profile below, more than 80% of the allocations are used to consume the full protocol firehose. The total resident memory of Jetstream sits below 16MB, 25% of which is actually consumed by the new zstd dictionary. To bring it all home, here’s a screenshot from the dashboard of my public Jetstream instance serving 12 consumers all with various filters and compression settings, running on a $5/mo OVH VPS. At our new baseline firehose activity, a consumer of the protocol-level firehose would require downloading ~3.16TB/mo to keep up. A Jetstream consumer getting all created, updated, and deleted records without compression enabled would require downloading ~400GB/mo to keep up. A Jetstream consumer that only cares about posts and has zstd compression enabled can get by on as little as ~25.5GB/mo, <99% of the full weight firehose. Feel free to join the conversation about Jetstream and zstd on Bluesky.

7 months ago 12 votes
How HLS Works

Over the past few weeks, I’ve been building out server-side short video support for Bluesky. The major aim of this feature is to support short (90 second max) video streaming at a quality that doesn’t cost an arm and a leg for us to provide for free. In order to stay within these constraints, we’re considering making use of a video CDN that can bear the brunt of the bandwidth required to support Video-on-Demand streaming. While the CDN is a pretty fully-featured product, we want to avoid too much vendor lock-in and provide some enhancements to our streaming platform that requires extending their offering and getting creative with video streaming protocols. Some of the things we’d like to be able to do that don’t work out-of-the-box are: Track view counts, viewer sessions, and duration viewed to provide better feedback for video performance. Provide dynamic closed-caption support with the flexibility to automate them in the future. Store a transcoded version of source files somewhere durable to provide a “source of truth” for videos when needed. Append a “trailer” to the end of video streams for some branding in a TikTok-esque 3-second snippet. In this post I’ll be focusing on the HLS-related features above, namely view/duration accounting, closed captions, and trailers. HLS is Just a Bunch of Text files HTTP Live Streaming (HLS) is a standard established by Apple in 2009 that allows for adaptive-bitrate live and Video-on-Demand (VOD) streaming. For the purposes of this blog post, I’ll restrict my explanations to how HLS VOD streaming works. A player that implements the HLS protocol is capable of dynamically adjusting the quality of a streamed video based on network conditions. Additionally, a server that implements the HLS protocol should provide one or more variants of a media stream which accommodate varying network qualities to allow for graceful degradation of stream quality without stopping playback. HLS implements this by producing a series of plaintext (.m3u8) “playlist” files that tell the player what bitrates and resolutions the server provides so that the player can decide which variant it should stream. HLS differentiates between two kinds of “playlist” files: Master Playlists, and Media Playlists. Master Playlists A Master Playlist is the first file fetched by your video player. It contains a series of variants which point to child Media Playlists. It also describes the approximate bitrate of the variant sources and the codecs and resolutions used by those sources. $ curl https://my.video.host.com/video_15/playlist.m3u8 #EXTM3U #EXT-X-VERSION:3 #EXT-X-STREAM-INF:PROGRAM-ID=0,BANDWIDTH=688540,CODECS="avc1.64001e,mp4a.40.2",RESOLUTION=640x360 360p/video.m3u8 #EXT-X-STREAM-INF:PROGRAM-ID=0,BANDWIDTH=1921217,CODECS="avc1.64001f,mp4a.40.2",RESOLUTION=1280x720 720p/video.m3u8 In the above file, the key things to notice are the RESOLUTION parameters and the {res}/video.m3u8 links. Your media player will generally start with the lowest resolution version before jumping up to higher resolutions once the network speed between you and the server is dialed in. The links in this file are pointers to Media Playlists, generally as relative paths from the Master Playlist such that, if we wanted to grab the 720p Media Playlist, we’d navigate to: https://my.video.host.com/video_15/720p/video.m3u8. A Master Playlist can also contain multi-track audio directives and directives for closed-captions but for now let’s move onto the Media Playlist. Media Playlists A Media Playlist is yet another plaintext file that provides your video player with two key bits of data: a list of media Segments (encoded as .ts video files) and headers for each Segment that tell the player the runtime of the media. $ curl https://my.video.host.com/video_15/720p/video.m3u8 #EXTM3U #EXT-X-VERSION:3 #EXT-X-PLAYLIST-TYPE:VOD #EXT-X-MEDIA-SEQUENCE:0 #EXT-X-TARGETDURATION:4 #EXTINF:4.000, video0.ts #EXTINF:4.000, video1.ts #EXTINF:4.000, video2.ts #EXTINF:4.000, video3.ts #EXTINF:4.000, video4.ts #EXTINF:2.800, video5.ts This Media Playlist describes a video that’s 22.8 seconds long (5 x 4-second Segments + 1 x 2.8-second Segment). The playlist describes a VOD piece of media, meaning we know this playlist contains the entirety of the media the player needs. The TARGETDURATION tells us the maximum length of each Segment so the player knows how many Segments to buffer ahead of time. During live streaming, that also lets the player know how frequently to refresh the playlist file to discover new Segments. Finally the EXTINF headers for each Segment indicate the duration of the following .ts Segment file and the relative paths of the video#.ts tell the player where to load the actual media files from. Where’s the Actual Media? At this point, the video player has loaded two .m3u8 playlist files and got lots of metadata about how to play the video but it hasn’t actually loaded any media files. The .ts files referenced in the Media Playlist are where the real media is, so if we wanted to control the playlists but let the CDN handle serving actual media, we can just redirect those video#.ts requests to our CDN. .ts files are Transport Stream MPEG-2 encoded short media files that can contain video or audio and video. Tracking Views To track views of our HLS streams, we can leverage the fact that every video player must first load the Master Playlist. When a user requests the Master Playlist, we can modify the results dynamically to provide a SessionID to each response and allow us to track the user session without cookies or headers: #EXTM3U #EXT-X-VERSION:3 #EXT-X-STREAM-INF:PROGRAM-ID=0,BANDWIDTH=688540,CODECS="avc1.64001e,mp4a.40.2",RESOLUTION=640x360 360p/video.m3u8?session_id=12345 #EXT-X-STREAM-INF:PROGRAM-ID=0,BANDWIDTH=1921217,CODECS="avc1.64001f,mp4a.40.2",RESOLUTION=1280x720 720p/video.m3u8?session_id=12345 Now when their video player fetches the Media Playlists, it’ll include a query-string that we can use to identify the streaming session, ensuring we don’t double-count views on the video and can track which Segments of video were loaded in the session. #EXTM3U #EXT-X-VERSION:3 #EXT-X-PLAYLIST-TYPE:VOD #EXT-X-MEDIA-SEQUENCE:0 #EXT-X-TARGETDURATION:4 #EXTINF:4.000, video0.ts?session_id=12345&duration=4 #EXTINF:4.000, video1.ts?session_id=12345&duration=4 #EXTINF:4.000, video2.ts?session_id=12345&duration=4 #EXTINF:4.000, video3.ts?session_id=12345&duration=4 #EXTINF:4.000, video4.ts?session_id=12345&duration=4 #EXTINF:2.800, video5.ts?session_id=12345&duration=2.8 Finally when the video player fetches the media Segment files, we can measure the Segment view before we redirect to our CDN with a 302, allowing us to know the amount of video-seconds loaded in the session and which Segments were loaded. This method has limitations, namely that a media player loading a segment doesn’t necessarily mean it showed that segment to the viewer, but it’s the best we can do without an instrumented media player. Adding Subtitles Subtitles are included in the Master Playlist as a variant and then are referenced in each of the video variants to let the player know where to load subs from. #EXTM3U #EXT-X-VERSION:3 #EXT-X-MEDIA:TYPE=SUBTITLES,GROUP-ID="subs",NAME="en_subtitle",DEFAULT=NO,AUTOSELECT=yes,LANGUAGE="en",FORCED="no",CHARACTERISTICS="public.accessibility.transcribes-spoken-dialog",URI="subtitles/en.m3u8" #EXT-X-STREAM-INF:PROGRAM-ID=0,BANDWIDTH=688540,CODECS="avc1.64001e,mp4a.40.2",RESOLUTION=640x360,SUBTITLES="subs" 360p/video.m3u8 #EXT-X-STREAM-INF:PROGRAM-ID=0,BANDWIDTH=1921217,CODECS="avc1.64001f,mp4a.40.2",RESOLUTION=1280x720,SUBTITLES="subs" 720p/video.m3u8 Just like with the video Media Playlists, we need a Media Playlist file for the subtitle track as well so that the player knows where to load the source files from and what duration of the stream they cover. $ curl https://my.video.host.com/video_15/subtitles/en.m3u8 #EXTM3U #EXT-X-VERSION:3 #EXT-X-MEDIA-SEQUENCE:0 #EXT-X-TARGETDURATION:22.8 #EXTINF:22.800, en.vtt In this case, since we’re only serving a short video, we can just provide a single Segment that points at a WebVTT subtitle file encompassing the entire duration of the video. If you crack open the en.vtt file you’ll see something like: $ curl https://my.video.host.com/video_15/subtitles/en.vtt WEBVTT 00:00.000 --> 00:02.000 According to all known laws of aviation, 00:02.000 --> 00:04.000 there is no way a bee should be able to fly. 00:04.000 --> 00:06.000 Its wings are too small to get its fat little body off the ground. ... The media player is capable of reading WebVTT and presenting the subtitles at the right time to the viewer. For longer videos you may want to break up your VTT files into more Segments and update the subtitle Media Playlist accordingly. To provide multiple languages and versions of subtitles, just add more EXT-X-MEDIA:TYPE=SUBTITLES lines to the Master Playlist and tweak the NAME, LANGUAGE (if different), and URI of the additional subtitle variant definitions. #EXT-X-MEDIA:TYPE=SUBTITLES,GROUP-ID="subs",NAME="en_subtitle",DEFAULT=NO,AUTOSELECT=yes,LANGUAGE="en",FORCED="no",CHARACTERISTICS="public.accessibility.transcribes-spoken-dialog",URI="subtitles/en.m3u8" #EXT-X-MEDIA:TYPE=SUBTITLES,GROUP-ID="subs",NAME="fr_subtitle",DEFAULT=NO,AUTOSELECT=yes,LANGUAGE="fr",FORCED="no",CHARACTERISTICS="public.accessibility.transcribes-spoken-dialog",URI="subtitles/fr.m3u8" #EXT-X-MEDIA:TYPE=SUBTITLES,GROUP-ID="subs",NAME="ja_subtitle",DEFAULT=NO,AUTOSELECT=yes,LANGUAGE="ja",FORCED="no",CHARACTERISTICS="public.accessibility.transcribes-spoken-dialog",URI="subtitles/ja.m3u8" Appending a Trailer For branding purposes (and in other applications, for advertising purposes), it can be helpful to insert Segments of video into a playlist to change the content of the video without requiring the content be appended to and re-encoded with the source file. Thankfully, HLS allows us to easily insert Segments into the Media Playlist using this one neat trick: #EXTM3U #EXT-X-VERSION:3 #EXT-X-PLAYLIST-TYPE:VOD #EXT-X-MEDIA-SEQUENCE:0 #EXT-X-TARGETDURATION:4 #EXTINF:4.000, video0.ts #EXTINF:4.000, video1.ts #EXTINF:4.000, video2.ts #EXTINF:4.000, video3.ts #EXTINF:4.000, video4.ts #EXTINF:2.800, video5.ts #EXT-X-DISCONTINUITY #EXTINF:3.337, trailer0.ts #EXTINF:1.201, trailer1.ts #EXTINF:1.301, trailer2.ts #EXT-X-ENDLIST In this Media Playlist we use HLS’s EXT-X-DISCONTINUITY header to let the video player know that the following Segments may be in a different bitrate, resolution, and aspect-ratio than the preceding content. Once we’ve provided the discontinuity header, we can add more Segments just like normal that point at a different media source broken up into .ts files. Remember, HLS allows us to use relative or absolute paths here, so we could provide a full URL for these trailer#.ts files, or virtually route them so they can retain the path context of the currently viewed video. Note that we don’t need to provide the discontinuity header here, and we could also name the trailer files something like video{6-8}.ts if we wanted to, but for clarity and proper player behavior, it’s best to use the discontinuity header if your trailer content doesn’t match the bitrate and resolution of the other video Segments. When the video player goes to play this media, it will continue from video5.ts to trailer0.ts without missing a beat, making it appear as if the trailer is part of the original video. This approach allows us to dynamically change the contents of the trailer for all videos, heavily cache the trailer .ts Segment files for performance, and avoid having to encode the trailer onto the end of every video source file. Conclusion At the end of the day, we’ve now got a video streaming service capable of tracking views and watch session durations, dynamic closed caption support, and branded trailers to help grow the platform. HLS is not a terribly complex protocol. The vast majority of it is human-readable plaintext files and is easy to inspect in the wild to how it’s used in production. When I started this project, I knew next to nothing about the protocol but was able to download some .m3u8 files and get digging to discover how the protocol worked, then build my own implementation of a HLS server to accommodate the video streaming needs of Bluesky. To learn more about HLS, you can check out the official RFC here which describes all the features discussed above and more. I hope this post encourages you to go explore other protocols you use every day by poking at them in the wild, downloading the files your browser interprets for you, and figuring out how simple some of these apparently “complex” systems are. If you’re interested in solving problems like these, take a look at our open Job Recs. If you have any questions about HLS, Bluesky, or other distributed, @scale social media infrastructure, you can find me on Bluesky here and you can discuss this post here.

9 months ago 11 votes
An entire Social Network in 1.6GB (GraphD Part 2)

In Part 1 of this series, we tried to answer the question “who do you follow who also follows user B” in Bluesky, a social network with millions of users and hundreds of millions of follow relationships. At the conclusion of the post, we’d developed an in-memory graph store for the network that uses HashMaps and HashSets to keep track of the followers of every user and the set of users they follow, allowing bidirectional lookups, intersections, unions, and other set operations for combining social graph data. I received some helpful feedback after that post where several people pointed me towards Roaring Bitmaps as a potential improvement on my implementation. They were right, Roaring Bitmaps would be an excellent fit for my Graph service, GraphD, and could also provide me with a much needed way to quickly persist and load the Graph data to and from disk on startup, hopefully reducing the startup time of the service. What are Bitmaps? If you just want to dive into the Roaring Bitmap spec, you can read the paper here, but it might be easier to first talk about bitmaps in general. You can think of a bitmap as a vector of one-bit values (like booleans) that let you encode a set of integer values. For instance, say we have 10,000 users on our website and want to keep track of which users have validated their email addresses. We could do this by creating a list of the uint32 user IDs of each user, in which case if all 10,000 users have validated their emails we’re storing 10k * 32 bits = 40KB. Or, we could create a vector of single-bit values that’s 10,000 bits long (10k / 8 = 1.25KB), then if a user has confirmed their email we can set the value at the index of their UID to 1. If we want to create a list of all the UIDs of validated accounts, we can walk the vector and record the index of each non-zero bit. If we want to check if user n has validated their email, we can do a O(1) lookup in the bitmap by loading the bit at index n and checking if it’s set. When Bitmaps get Big and Sparse Now when talking about our social network problem, we’re dealing with a few more than 10,000 UIDs. We need to keep track of 5.5M users and whether or not the user follows or is followed by any of the other 5.5M users in the network. To keep a bitmap of “People who follow User A”, we’re going to need 5.5M bits which would require (5.5M / 8) ~687KB of space. If we wanted to keep bitmaps of “People who follow User A” and “People who User A follows”, we’d need ~1.37MB of space per user using a simple bitmap, meaning we’d need 5,500,000 * 1.37MB = ~7.5 Terabytes of space! Clearly this isn’t an improvement of our strategy from Part 1, so how can we make this more efficient? One strategy for compressing the bitmap is to take consecutive runs of 0’s or 1’s (i.e. 00001110000001) in the bitmap and turn them into a number. For instance if we had an account that followed only the last 100 accounts in our social network, the first 5,499,900 indices in our bitmap would be 0’s and so we could represent the bitmap by saying: 5,499,900 0's, then 100 1's which you notice I’ve written here in a lot fewer than 687KB and a computer could encode using two uint32 values plus two bits (one indicator bit for the state of each run) for a total of 66 bits. This strategy is called Run Length Encoding (RLE) and works pretty well but has a few drawbacks: mainly if your data is randomly and heavily populated, you may not have many consecutive runs (imagine a bitset where every odd bit is set and every even bit is unset). Also lookups and evaluation of the bitset requires walking the whole bitset to figure out where the index you care about lives in the compressed format. Thankfully there’s a more clever way to compress bitmaps using a strategy called Roaring Bitmaps. A brief description of the storage strategy for Roaring Bitmaps from the official paper is as follows: We partition the range of 32-bit indexes ([0, n)) into chunks of 2^16 integers sharing the same 16 most significant digits. We use specialized containers to store their 16 least significant bits. When a chunk contains no more than 4096 integers, we use a sorted array of packed 16-bit integers. When there are more than 4096 integers, we use a 2^16-bit bitmap. Thus, we have two types of containers: an array container for sparse chunks and a bitmap container for dense chunks. The 4096 threshold insures that at the level of the containers, each integer uses no more than 16 bits. These bitmaps are designed to support both densely and sparsely distributed data and can provide high performance binary set operations (and/or/etc.) operating on the containers within two or more bitsets in parallel. For more info on how Roaring Bitmaps work and some neat diagrams, check out this excellent primer on Roaring Bitmaps by Vikram Oberoi. So, how does this help us build a better graph? GraphD, Revisited with Roaring Bitmaps Let’s get back to our GraphD Service, this time in Go instead of Rust. For each user we can keep track of a struct with two bitmaps: type FollowMap struct { followingBM *roaring.Bitmap followingLk sync.RWMutex followersBM *roaring.Bitmap followersLk sync.RWMutex } Our FollowMap gives us a Roaring Bitmap for both the set of users we follow, and the set of users who follow us. Adding a Follow to the graph just requires we set the right bits in both user’s respective maps: // Note I've removed locking code and error checks for brevity func (g *Graph) addFollow(actorUID, targetUID uint32) { actorMap, _ := g.g.Load(actorUID) actorMap.followingBM.Add(targetUID) targetMap, _ := g.g.Load(targetUID) targetMap.followersBM.Add(actorUID) } Even better if we want to compute the intersections of two sets (i.e. the people User A follows who also follow User B) we can do so in parallel: // Note I've removed locking code and error checks for brevity func (g *Graph) IntersectFollowingAndFollowers(actorUID, targetUID uint32) ([]uint32, error) { actorMap, ok := g.g.Load(actorUID) targetMap, ok := g.g.Load(targetUID) intersectMap := roaring.ParAnd(4, actorMap.followingBM, targetMap.followersBM) return intersectMap.ToArray(), nil } Storing the entire graph as Roaring Bitmaps in-memory costs us around 6.5GB of RAM and allows us to perform set intersections between moderately large sets (with hundreds of thousands of set bits) in under 500 microseconds while serving over 70k req/sec! And the best part of all? We can use Roaring’s serialization format to write these bitmaps to disk or transfer them over the network. Storing 164M Follows in 1.6GB In the original version of GraphD, on startup the service would read a CSV file with an adjacency list of the (ActorDID, TargetDID) pairs of all follows on the network. This required creating a CSV dump of the follows table, pausing writes to the follows table, then bringing up the service and waiting 5 minutes for it to read the CSV file, intern the DIDs as uint32 UIDs, and construct the in-memory graph. This process is slow, pauses writes for 5 minutes, and every time our service restarts we have to do it all over again! With Roaring Bitmaps, we’re now given an easy way to effectively serialize a version of the in-memory graph that is many times smaller than the adjacency list CSV and many times faster to load. We can serialize the entire graph into a SQLite DB on the local machine where each row in a table contains: (uid, DID, followers_bitmap, following_bitmap) Loading the entire graph from this SQLite DB can be done in around ~20 seconds: // Note I've removed locking code and error checks for brevity rows, err := g.db.Query(`SELECT uid, did, following, followers FROM actors;`) for rows.Next() { var uid uint32 var did string var followingBytes []byte var followersBytes []byte rows.Scan(&uid, &did, &followingBytes, &followersBytes) followingBM := roaring.NewBitmap() followingBM.FromBuffer(followingBytes) followersBM := roaring.NewBitmap() followersBM.FromBuffer(followersBytes) followMap := &FollowMap{ followingBM: followingBM, followersBM: followersBM, followingLk: sync.RWMutex{}, followersLk: sync.RWMutex{}, } g.g.Store(uid, followMap) g.setUID(did, uid) g.setDID(uid, did) } While the service is running, we can also keep track of the UIDs of actors who have added or removed a follow since the last time we saved the DB, allowing us to periodically flush changes to the on-disk SQLite only for bitmaps that have updated. Syncing our data every 5 seconds while tailing the production firehose takes 2ms and writes an average of only ~5MB to disk per flush. The crazy part of this is, the on-disk representation of our entire follow network is only ~1.6GB! Because we’re making use of Roaring’s compressed serialized format, we can turn the ~6.5GB of in-memory maps into 1.6GB of on-disk data. Our largest bitmap, the followers of the bsky.app account with over 876k members, becomes ~500KB as a blob stored in SQLite. So, to wrap up our exploration of Roaring Bitmaps for first-degree graph databases, we saw: A ~20% reduction in resident memory size compared to HashSets and HashMaps A ~84% reduction in the on-disk size of the graph compared to an adjacency list A ~93% reduction in startup time compared to loading from an adjacency list A ~66% increase in throughput of worst-case requests under load A ~59% reduction in p99 latency of worst-case requests under low My next iteration on this problem will likely be to make use of DGraph’s in-memory Serialized Roaring Bitmap library that allows you to operate on fully-compressed bitmaps so there’s no need to serialize and deserialize them when reading from or writing to disk. It also probably results in significant memory savings as well! If you’re interested in solving problems like these, take a look at our open Backend Developer Job Rec. You can find me on Bluesky here, you can chat about this post here.

a year ago 13 votes

More in programming

How should Stripe deprecate APIs? (~2016)

While Stripe is a widely admired company for things like its creation of the Sorbet typer project, I personally think that Stripe’s most interesting strategy work is also among its most subtle: its willingness to significantly prioritize API stability. This strategy is almost invisible externally. Internally, discussions around it were frequent and detailed, but mostly confined to dedicated API design conversations. API stability isn’t just a technical design quirk, it’s a foundational decision in an API-driven business, and I believe it is one of the unsung heroes of Stripe’s business success. This is an exploratory, draft chapter for a book on engineering strategy that I’m brainstorming in #eng-strategy-book. As such, some of the links go to other draft chapters, both published drafts and very early, unpublished drafts. Reading this document To apply this strategy, start at the top with Policy. To understand the thinking behind this strategy, read sections in reverse order, starting with Explore. More detail on this structure in Making a readable Engineering Strategy document. Policy & Operation Our policies for managing API changes are: Design for long API lifetime. APIs are not inherently durable. Instead we have to design thoughtfully to ensure they can support change. When designing a new API, build a test application that doesn’t use this API, then migrate to the new API. Consider how integrations might evolve as applications change. Perform these migrations yourself to understand potential friction with your API. Then think about the future changes that we might want to implement on our end. How would those changes impact the API, and how would they impact the application you’ve developed. At this point, take your API to API Review for initial approval as described below. Following that approval, identify a handful of early adopter companies who can place additional pressure on your API design, and test with them before releasing the final, stable API. All new and modified APIs must be approved by API Review. API changes may not be enabled for customers prior to API Review approval. Change requests should be sent to api-review email group. For examples of prior art, review the api-review archive for prior requests and the feedback they received. All requests must include a written proposal. Most requests will be approved asynchronously by a member of API Review. Complex or controversial proposals will require live discussions to ensure API Review members have sufficient context before making a decision. We never deprecate APIs without an unavoidable requirement to do so. Even if it’s technically expensive to maintain support, we incur that support cost. To be explicit, we define API deprecation as any change that would require customers to modify an existing integration. If such a change were to be approved as an exception to this policy, it must first be approved by the API Review, followed by our CEO. One example where we granted an exception was the deprecation of TLS 1.2 support due to PCI compliance obligations. When significant new functionality is required, we add a new API. For example, we created /v1/subscriptions to support those workflows rather than extending /v1/charges to add subscriptions support. With the benefit of hindsight, a good example of this policy in action was the introduction of the Payment Intents APIs to maintain compliance with Europe’s Strong Customer Authentication requirements. Even in that case the charge API continued to work as it did previously, albeit only for non-European Union payments. We manage this policy’s implied technical debt via an API translation layer. We release changed APIs into versions, tracked in our API version changelog. However, we only maintain one implementation internally, which is the implementation of the latest version of the API. On top of that implementation, a series of version transformations are maintained, which allow us to support prior versions without maintaining them directly. While this approach doesn’t eliminate the overhead of supporting multiple API versions, it significantly reduces complexity by enabling us to maintain just a single, modern implementation internally. All API modifications must also update the version transformation layers to allow the new version to coexist peacefully with prior versions. In the future, SDKs may allow us to soften this policy. While a significant number of our customers have direct integrations with our APIs, that number has dropped significantly over time. Instead, most new integrations are performed via one of our official API SDKs. We believe that in the future, it may be possible for us to make more backwards incompatible changes because we can absorb the complexity of migrations into the SDKs we provide. That is certainly not the case yet today. Diagnosis Our diagnosis of the impact on API changes and deprecation on our business is: If you are a small startup composed of mostly engineers, integrating a new payments API seems easy. However, for a small business without dedicated engineers—or a larger enterprise involving numerous stakeholders—handling external API changes can be particularly challenging. Even if this is only marginally true, we’ve modeled the impact of minimizing API changes on long-term revenue growth, and it has a significant impact, unlocking our ability to benefit from other churn reduction work. While we believe API instability directly creates churn, we also believe that API stability directly retains customers by increasing the migration overhead even if they wanted to change providers. Without an API change forcing them to change their integration, we believe that hypergrowth customers are particularly unlikely to change payments API providers absent a concrete motivation like an API change or a payment plan change. We are aware of relatively few companies that provide long-term API stability in general, and particularly few for complex, dynamic areas like payments APIs. We can’t assume that companies that make API changes are ill-informed. Rather it appears that they experience a meaningful technical debt tradeoff between the API provider and API consumers, and aren’t willing to consistently absorb that technical debt internally. Future compliance or security requirements—along the lines of our upgrade from TLS 1.2 to TLS 1.3 for PCI—may necessitate API changes. There may also be new tradeoffs exposed as we enter new markets with their own compliance regimes. However, we have limited ability to predict these changes at this point.

5 days ago 7 votes
Requirements change until they don't

Recently I got a question on formal methods1: how does it help to mathematically model systems when the system requirements are constantly changing? It doesn't make sense to spend a lot of time proving a design works, and then deliver the product and find out it's not at all what the client needs. As the saying goes, the hard part is "building the right thing", not "building the thing right". One possible response: "why write tests"? You shouldn't write tests, especially lots of unit tests ahead of time, if you might just throw them all away when the requirements change. This is a bad response because we all know the difference between writing tests and formal methods: testing is easy and FM is hard. Testing requires low cost for moderate correctness, FM requires high(ish) cost for high correctness. And when requirements are constantly changing, "high(ish) cost" isn't affordable and "high correctness" isn't worthwhile, because a kinda-okay solution that solves a customer's problem is infinitely better than a solid solution that doesn't. But eventually you get something that solves the problem, and what then? Most of us don't work for Google, we can't axe features and products on a whim. If the client is happy with your solution, you are expected to support it. It should work when your customers run into new edge cases, or migrate all their computers to the next OS version, or expand into a market with shoddy internet. It should work when 10x as many customers are using 10x as many features. It should work when you add new features that come into conflict. And just as importantly, it should never stop solving their problem. Canonical example: your feature involves processing requested tasks synchronously. At scale, this doesn't work, so to improve latency you make it asynchronous. Now it's eventually consistent, but your customers were depending on it being always consistent. Now it no longer does what they need, and has stopped solving their problems. Every successful requirement met spawns a new requirement: "keep this working". That requirement is permanent, or close enough to decide our long-term strategy. It takes active investment to keep a feature behaving the same as the world around it changes. (Is this all a pretentious of way of saying "software maintenance is hard?" Maybe!) Phase changes In physics there's a concept of a phase transition. To raise the temperature of a gram of liquid water by 1° C, you have to add 4.184 joules of energy.2 This continues until you raise it to 100°C, then it stops. After you've added two thousand joules to that gram, it suddenly turns into steam. The energy of the system changes continuously but the form, or phase, changes discretely. Software isn't physics but the idea works as a metaphor. A certain architecture handles a certain level of load, and past that you need a new architecture. Or a bunch of similar features are independently hardcoded until the system becomes too messy to understand, you remodel the internals into something unified and extendable. etc etc etc. It's doesn't have to be totally discrete phase transition, but there's definitely a "before" and "after" in the system form. Phase changes tend to lead to more intricacy/complexity in the system, meaning it's likely that a phase change will introduce new bugs into existing behaviors. Take the synchronous vs asynchronous case. A very simple toy model of synchronous updates would be Set(key, val), which updates data[key] to val.3 A model of asynchronous updates would be AsyncSet(key, val, priority) adds a (key, val, priority, server_time()) tuple to a tasks set, and then another process asynchronously pulls a tuple (ordered by highest priority, then earliest time) and calls Set(key, val). Here are some properties the client may need preserved as a requirement: If AsyncSet(key, val, _, _) is called, then eventually db[key] = val (possibly violated if higher-priority tasks keep coming in) If someone calls AsyncSet(key1, val1, low) and then AsyncSet(key2, val2, low), they should see the first update and then the second (linearizability, possibly violated if the requests go to different servers with different clock times) If someone calls AsyncSet(key, val, _) and immediately reads db[key] they should get val (obviously violated, though the client may accept a slightly weaker property) If the new system doesn't satisfy an existing customer requirement, it's prudent to fix the bug before releasing the new system. The customer doesn't notice or care that your system underwent a phase change. They'll just see that one day your product solves their problems, and the next day it suddenly doesn't. This is one of the most common applications of formal methods. Both of those systems, and every one of those properties, is formally specifiable in a specification language. We can then automatically check that the new system satisfies the existing properties, and from there do things like automatically generate test suites. This does take a lot of work, so if your requirements are constantly changing, FM may not be worth the investment. But eventually requirements stop changing, and then you're stuck with them forever. That's where models shine. As always, I'm using formal methods to mean the subdiscipline of formal specification of designs, leaving out the formal verification of code. Mostly because "formal specification" is really awkward to say. ↩ Also called a "calorie". The US "dietary Calorie" is actually a kilocalorie. ↩ This is all directly translatable to a TLA+ specification, I'm just describing it in English to avoid paying the syntax tax ↩

5 days ago 10 votes
We'll always need junior programmers

We received over 2,200 applications for our just-closed junior programmer opening, and now we're going through all of them by hand and by human. No AI screening here. It's a lot of work, but we have a great team who take the work seriously, so in a few weeks, we'll be able to invite a group of finalists to the next phase. This highlights the folly of thinking that what it'll take to land a job like this is some specific list of criteria, though. Yes, you have to present a baseline of relevant markers to even get into consideration, like a great cover letter that doesn't smell like AI slop, promising projects or work experience or educational background, etc. But to actually get the job, you have to be the best of the ones who've applied! It sounds self-evident, maybe, but I see questions time and again about it, so it must not be. Almost every job opening is grading applicants on the curve of everyone who has applied. And the best candidate of the lot gets the job. You can't quantify what that looks like in advance. I'm excited to see who makes it to the final stage. I already hear early whispers that we got some exceptional applicants in this round. It would be great to help counter the narrative that this industry no longer needs juniors. That's simply retarded. However good AI gets, we're always going to need people who know the ins and outs of what the machine comes up with. Maybe not as many, maybe not in the same roles, but it's truly utopian thinking that mankind won't need people capable of vetting the work done by AI in five minutes.

5 days ago 13 votes
Brian Regan Helped Me Understand My Aversion to Job Titles

I like the job title “Design Engineer”. When required to label myself, I feel partial to that term (I should, I’ve written about it enough). Lately I’ve felt like the term is becoming more mainstream which, don’t get me wrong, is a good thing. I appreciate the diversification of job titles, especially ones that look to stand in the middle between two binaries. But — and I admit this is a me issue — once a title starts becoming mainstream, I want to use it less and less. I was never totally sure why I felt this way. Shouldn’t I be happy a title I prefer is gaining acceptance and understanding? Do I just want to rebel against being labeled? Why do I feel this way? These were the thoughts simmering in the back of my head when I came across an interview with the comedian Brian Regan where he talks about his own penchant for not wanting to be easily defined: I’ve tried over the years to write away from how people are starting to define me. As soon as I start feeling like people are saying “this is what you do” then I would be like “Alright, I don't want to be just that. I want to be more interesting. I want to have more perspectives.” [For example] I used to crouch around on stage all the time and people would go “Oh, he’s the guy who crouches around back and forth.” And I’m like, “I’ll show them, I will stand erect! Now what are you going to say?” And then they would go “You’re the guy who always feels stupid.” So I started [doing other things]. He continues, wondering aloud whether this aversion to not being easily defined has actually hurt his career in terms of commercial growth: I never wanted to be something you could easily define. I think, in some ways, that it’s held me back. I have a nice following, but I’m not huge. There are people who are huge, who are great, and deserve to be huge. I’ve never had that and sometimes I wonder, ”Well maybe it’s because I purposely don’t want to be a particular thing you can advertise or push.” That struck a chord with me. It puts into words my current feelings towards the job title “Design Engineer” — or any job title for that matter. Seven or so years ago, I would’ve enthusiastically said, “I’m a Design Engineer!” To which many folks would’ve said, “What’s that?” But today I hesitate. If I say “I’m a Design Engineer” there are less follow up questions. Now-a-days that title elicits less questions and more (presumed) certainty. I think I enjoy a title that elicits a “What’s that?” response, which allows me to explain myself in more than two or three words, without being put in a box. But once a title becomes mainstream, once people begin to assume they know what it means, I don’t like it anymore (speaking for myself, personally). As Brian says, I like to be difficult to define. I want to have more perspectives. I like a title that befuddles, that doesn’t provide a presumed sense of certainty about who I am and what I do. And I get it, that runs counter to the very purpose of a job title which is why I don’t think it’s good for your career to have the attitude I do, lol. I think my own career evolution has gone something like what Brian describes: Them: “Oh you’re a Designer? So you make mock-ups in Photoshop and somebody else implements them.” Me: “I’ll show them, I’ll implement them myself! Now what are you gonna do?” Them: “Oh, so you’re a Design Engineer? You design and build user interfaces on the front-end.” Me: “I’ll show them, I’ll write a Node server and setup a database that powers my designs and interactions on the front-end. Now what are they gonna do?” Them: “Oh, well, we I’m not sure we have a term for that yet, maybe Full-stack Design Engineer?” Me: “Oh yeah? I’ll frame up a user problem, interface with stakeholders, explore the solution space with static designs and prototypes, implement a high-fidelity solution, and then be involved in testing, measuring, and refining said solution. What are you gonna call that?” [As you can see, I have some personal issues I need to work through…] As Brian says, I want to be more interesting. I want to have more perspectives. I want to be something that’s not so easily definable, something you can’t sum up in two or three words. I’ve felt this tension my whole career making stuff for the web. I think it has led me to work on smaller teams where boundaries are much more permeable and crossing them is encouraged rather than discouraged. All that said, I get it. I get why titles are useful in certain contexts (corporate hierarchies, recruiting, etc.) where you’re trying to take something as complicated and nuanced as an individual human beings and reduce them to labels that can be categorized in a database. I find myself avoiding those contexts where so much emphasis is placed in the usefulness of those labels. “I’ve never wanted to be something you could easily define” stands at odds with the corporate attitude of, “Here’s the job req. for the role (i.e. cog) we’re looking for.” Email · Mastodon · Bluesky

6 days ago 10 votes
Bike Brooklyn! zine

I've been biking in Brooklyn for a few years now! It's hard for me to believe it, but I'm now one of the people other bicyclists ask questions to now. I decided to make a zine that answers the most common of those questions: Bike Brooklyn! is a zine that touches on everything I wish I knew when I started biking in Brooklyn. A lot of this information can be found in other resources, but I wanted to collect it in one place. I hope to update this zine when we get significantly more safe bike infrastructure in Brooklyn and laws change to make streets safer for bicyclists (and everyone) over time, but it's still important to note that each release will reflect a specific snapshot in time of bicycling in Brooklyn. All text and illustrations in the zine are my own. Thank you to Matt Denys, Geoffrey Thomas, Alex Morano, Saskia Haegens, Vishnu Reddy, Ben Turndorf, Thomas Nayem-Huzij, and Ryan Christman for suggestions for content and help with proofreading. This zine is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, so you can copy and distribute this zine for noncommercial purposes in unadapted form as long as you give credit to me. Check out the Bike Brooklyn! zine on the web or download pdfs to read digitally or print here!

6 days ago 10 votes