At first glance, a group deploy might seem simple, but it can quickly turn into an operational nightmare. With separate teams managing the operational processes for each bidder, a group deploy means coordinating the deploy schedules of four bidders services plus ads-selection, making sure that all five services agree on the same shard count for ads-selection. At Twitter, we partition ads service instances into clusters. All instances of a service within a cluster run the same version of a service in a steady state. Normally, independent services within a single cluster can be deployed independently within a cluster. Since a bidder instance is not compatible with both old and new versions of ads-selection for a resharding change, the group deploy requires shutting down all the services one cluster at a time, and then rolling out the new version of each service one cluster at a time. What normally would be five independent, automated, and decoupled service deployments becomes one massive coordinated deployment, with potentially significant manual intervention required from on-call engineers if any one service has to get rolled back.

ShardLib introduces the concept of a placement, a unified abstraction that encapsulates the sharding scheme (the total shard count and hash ranges) for a service. The placement is a centralized source of truth for the service's own sharding scheme and for clients of the service to discover the sharding scheme of the service during runtime.


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The above JSON file defines a sharding scheme that has two shards, each of which is responsible for equal halves of the range [0, LONG.MAX_VALUE]. The placement version is essential for resharding, since it allows for disambiguation between two competing sharding schemes that might be active at the same time.

Additionally, we created a new HTTP endpoint that lets clients fetch a placement.json from the server at runtime, when they encounter a placement version for the first time, rather than hard-coding this information in the client at compile time.

Because the client can respond to downstream sharding scheme changes dynamically and adjust the request fanout strategy, we can change the sharding scheme of the downstream without worrying about redeploying the client services. For this purpose, the ShardLib client maintains logic to distribute requests between multiple sharding schemes since two sharding schemes can be active at the same time during resharding. It uses a weighted random balancer algorithm where weighting is based on the minimum number of replicas present for each shard of a particular sharding scheme.

Migrating the previous ads-serving service-to-service communication architecture to ShardLib had its own set of challenges. The rollout plan needed to be gradual without any downtime on any of the onboarded services. Shutting down traffic to services and redeploying the ads services was not an option.


After the ads-video bidder migration was completed, we proceeded to onboard each of the remaining bidders (ads-takeover, ads-map, and ads-default bidder services) as sharded clients of ads-selection, one by one onto ShardLib. Naively, one might expect subsequent bidder service migrations to be simpler and quicker because of previous learnings. But as we discovered during the process, each bidder service had evolved to set up its sharding logic in a conceptually similar fashion, but using slightly different code structure. Thus, the testing and correctness verification process for each bidder migration often became the most time-consuming step, as the onboarding process also required us to fix code discrepancies between the services.

One week after fumbling an early six-goal lead to the Redwoods, the Waterdogs proved capable of flipping that script. And once again, Connor Kelly showed his clutch gene for the defending Premier Lacrosse League champions.

The Whipsnakes went into the break up 13-3 to breeze into their first victory of the season. Will Manny had seven points, including a two pointer, Matt Rambo scored four times in a six-point day and Tucker Dordevic added four points.

Players are usually classified as "clutch" or "unclutch" based solely on their ability or inability to make the final shot of a close game. Basically, the first 47 minutes and 59 seconds are insignificant, as long as you're in position to win the game, and proceed to do so by making a clutch final shot.

Because of the various definitions clutch (Only the fourth quarter? The final 5 minutes? The final minute? Last possession? Does the regular season matter? Only Playoffs?), the limited data available and the relatively small sample sizes, labeling players as clutch has become an extremely subjective exercise, and will probably remain so for all of eternity. Mythical factors such as, the "clutch gene" and the possession of "killer instinct/a naturally-protruding-jaw-clinch", also stand in the way of an agreement between the general public on what's clutch and who's clutch in the NBA.

Since the idea of clutch is so vague, no matter how eloquently written, or statistically-stuffed an argument is, most fans, bloggers, analysts and GMs alike will never alter their opinions of who's clutch and who's not. That said, over the past 48 hours I've completed the ultimate exercise in futility by compiling every "clutch" NBA statistic since 2000 (12 years).

These numbers do not represent the end-all-be-all of "clutchness" in the NBA, because there are still a lot of flaws and missing variables (i.e. not all clutch moments are created equal). The objective here is not to identify who's clutch and who's not, but rather provide a statistical snap-shot of what NBA players have both excelled and failed in certain situations that may be considered "clutch" over the past decade-plus, sample sizes considered. It'd be an added bonus if minds were opened to the idea that Kobe wasn't the clutchest thing since sliced bread and that LeBron couldn't close the lid on Snooki's pickle jar. (I just made both those sayings up, and neither worked as planned, although the LeBron-Snooki one is pretty innuendo-ish. I'm hungry).

The second table is the same as the first, except instead of dating back to 2000, it begins during the 2006-07 season to get a better snapshot of today's stars, and less Andre Miller. I made the cut-off at 370 "clutch" attempts "that occur during the 4th quarter or overtime, with less than five minutes remaining, and neither team ahead by more than five points", because I wanted to include Brandon Roy. 15 active players are compared, playoffs included.

Finally, I threw in three small bonus tables to touch on two sentiments. First, that Kobe has performed significantly better than LeBron in "the clutch", specifically "when it matters" aka the Playoffs. And two, that LeBron is clearly the Robin to Wade's Batman because, like Kobe, Wade has proven to be so much "more clutch" in big moments.

In no way shape or form does this statistical look at who has performed well "in the clutch" in the NBA since 2000 definitively tell us which players are more clutch than others, because we still aren't clear on what "clutch" is, or whether it actually exists.

Statistics clearly cannot measure the fear Kobe Bryant puts into opposing teams when he disses Chris Rock on the bench of a playoff game, or the innate weakness shown by LeBron James every time he bites his nails.

The Heat took a 128-126 lead in the dying seconds of overtime. Milwaukee had a chance to tie or win the game on their next shot. However, Grayson Allen simply dribbled the ball as the clock expired, ending their season.

The Bucks became the sixth first-seed in NBA history to be eliminated by an eight-seed. And it marks the first time they failed to advance to the second round since 2017-18 when they lost to the Boston Celtics.

On the road, you fade these Dolphins every time. As for the Ravens, it might take another one of those Playoff Joe Flacco miracles (and one from Myles Garrett) to put an end to this run they are on. Cleveland is the only team to beat the Ravens in their last 11 games.

This time the defense was even better, holding the Titans to 3 points and returning a fumble from Will Levis for a touchdown. They knocked Levis out of the game and kept pounding Ryan Tannehill with 5 sacks. It was an all-around strong 26-3 win for the Texans, who will be in Indy for a huge game this Saturday night.

By the time the offense finally got another drive in the end zone with 2:42 left to make it 20-14, they were basically in 3-and-out or bust mode for the defense. You would think the defense would sell out to stop the run, but the Raiders had no problem finding running lanes for Zamir White, and he finished the Chiefs off with runs of 6, 43, and 15 yards to finish with 145 yards.

But that defense is still unreliable, and this was a 1-score game after they allowed a 69-yard touchdown pass to Darius Slayton. Tyrod had to go 75 yards in 70 seconds and get a 2-point conversion just to force overtime. But he did get to the Philadelphia 26 before holding the ball forever on the final snap before finally throwing a game-ending interception in the end zone to help the Eagles escape with the 33-25 win.

Beyond the fumble on the opening drive that was the only turnover in the game, Dallas also lost this one at the end of each half. Miami scored a touchdown before halftime and managed the clock perfectly on the final drive to make sure the Cowboys had no time left after taking the lead.

The win was largely a celebration of recent draft successes with Amon-Ra St. Brown going over 100 yards and scoring a touchdown, a pair of touchdowns for rookie back Jahmyr Gibbs, and the defense intercepted Nick Mullens four times, including a game-clincher in the final minute with Minnesota 30 yards away from the lead.

But as it turns out, Lawrence was the turnover machine in this game and probably should have been sidelined earlier than he was before yet another injury (shoulder) was added to his 2023 season. Lawrence turned it over 3 times and the Buccaneers turned them all into touchdown drives to build a 27-0 lead early in the third quarter. e24fc04721

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