The Big Hash Split Decision Mp3 Download

Hash-based user assignment brings experimenters many benefits in running split tests. It's used extensively in the industry (section 4.1.2, page 6), at companies like LinkedIn, Google, Microsoft and many others. Compared to the ephemeral random numbers generated from PRNGs, hash-based decisioning gives you far greater control:

The meta discussion around cryptographic hash functions === PRNGs is beyond the scope of this article, but there's a lot of discussion in the literature about why this works. And there's also some approachable Stack Overflow posts that make the case for Hash-based PRNGs.

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Using a user/cookie ID and Mojito's decisionAdapter, we can control how users are bucketed. We'll take the user/cookie ID and send it through a hashing function to deterministically generate a really solid random numbers between 0 and 1:

Each 'decision' is capable of producing over 4 billion values - more than enough granularity for our purposes (it's probably overkill). More 'secure' hash functions exist, but we only need speed & reliability. We picked it because MSFT/LinkedIn et al use it, MD5 digests are pervasive across DBs/languages, and from our testing at Mint Metrics, it produces nice flat & even distributions:

Warm & flaky buttermilk biscuit filled with crispy chicken breast, hash browns, pickle chips & country gravy. Served with choice of hash browns, French fries, 2 buttermilk pancakes, or seasonal fresh fruit.

Warm & flaky buttermilk biscuit filled with 2 fried eggs*, Processed cheese, 2 strips of crispy bacon & cheese sauce. Served with choice of hash browns, French fries, 2 buttermilk pancakes, or seasonal fresh fruit.

Grilled multigrain bread topped with freshly sliced avocado & roasted cherry tomatoes. Served with choice of fresh fruit or hash browns. Seasonal availability may vary.

Top it off with 2 eggs. additional cost

Top it off with 2 eggs & 2 bacon strips. additional cost

I guess this needs a litte explanation: What's happening is we're constructing a URL object with an empty hash on the current URL as base, i.e. if there is a hash, it gets replace by an empty one. We then remove the last character ('#') from the implicitly cast string (href of the URL object).

Whilst improving Mojito's PRNG & devising an ITP2.X workaround last year we introduced a modular splitting tool in Mojito that lets users split traffic with hash functions. We're amazed by the features that hash functions enable in split testing such as:

But hiding in plain sight was a novel ramping process that Lukas Vermeer, Booking.com's Director of Experimentation, pointed out to us. We'd not encountered it before. But now that we were using hash functions, it was possible...

As with a straight ramp, users with an inconsistent experience often skew toward your most loyal users. If your split test reporting is sufficiently flexible, you can segment out these users from your results to mute this effect.

With Mojito's modular [decisionAdapter]( -delivery-api-decision-adapter), you can define your own bucketing logic or orchestrate even more sophisticated partitioned ramps. You only need a couple of parameters and our recommended decision adaptor to implement this:

Minimum size reduction to the main chunk (bundle), in bytes, needed for a chunk to be generated. Meaning if splitting into a chunk does not reduce the size of the main chunk (bundle) by the given amount of bytes, it won't be split, even if it meets the splitChunks.minSize value.

splitChunks.minRemainingSize option was introduced in webpack 5 to avoid zero sized modules by ensuring that the minimum size of the chunk which remains after splitting is above a limit. Defaults to 0 in 'development' mode. For other cases splitChunks.minRemainingSize defaults to the value of splitChunks.minSize so it doesn't need to be specified manually except for the rare cases where deep control is required.

Using maxSize (either globally optimization.splitChunks.maxSize per cache group optimization.splitChunks.cacheGroups[x].maxSize or for the fallback cache group optimization.splitChunks.fallbackCacheGroup.maxSize) tells webpack to try to split chunks bigger than maxSize bytes into smaller parts. Parts will be at least minSize (next to maxSize) in size.The algorithm is deterministic and changes to the modules will only have local effects. So that it is usable when using long term caching and doesn't require records. maxSize is only a hint and could be violated when modules are bigger than maxSize or splitting would violate minSize.

When the chunk has a name already, each part will get a new name derived from that name. Depending on the value of optimization.splitChunks.hidePathInfo it will add a key derived from the first module name or a hash of it.

Like maxSize, maxAsyncSize can be applied globally (splitChunks.maxAsyncSize), to cacheGroups (splitChunks.cacheGroups.{cacheGroup}.maxAsyncSize), or to the fallback cache group (splitChunks.fallbackCacheGroup.maxAsyncSize).

Like maxSize, maxInitialSize can be applied globally (splitChunks.maxInitialSize), to cacheGroups (splitChunks.cacheGroups.{cacheGroup}.maxInitialSize), or to the fallback cache group (splitChunks.fallbackCacheGroup.maxInitialSize).

splitChunks.cacheGroups.{cacheGroup}.name can be used to move modules into a chunk that is a parent of the source chunk. For example, use name: "entry-name" to move modules into the entry-name chunk. You can also use on demand named chunks, but you must be careful that the selected modules are only used under this chunk.

Running webpack with following splitChunks configuration would also output a chunk of the group common with next name: commons-main-lodash.js.e7519d2bb8777058fa27.js (hash given as an example of real world output).

Cache groups can inherit and/or override any options from splitChunks.*; but test, priority and reuseExistingChunk can only be configured on cache group level. To disable any of the default cache groups, set them to false.

What's the reasoning behind this? react probably won't change as often as your application code. By moving it into a separate chunk this chunk can be cached separately from your app code (assuming you are using chunkhash, records, Cache-Control or other long term cache approach).

This module describes how to load split IP multicast traffic over Equal Cost Multipath (ECMP). Multicast traffic from different sources or from different sources and groups are load split across equal-cost paths to take advantage of multiple paths through the network.

Load splitting and load balancing are not the same. Load splitting provides a means to randomly distribute (*, G) and (S, G) traffic streams across multiple equal-cost reverse path forwarding (RPF) paths, which does not necessarily result in a balanced IP multicast traffic load on those equal-cost RPF paths. By randomly distributing (*, G) and (S, G) traffic streams, the methods used for load splitting IP multicast traffic attempt to distribute an equal amount of traffic flows on each of the available RPF paths not by counting the flows, but, rather, by making a pseudorandom decision. These methods are collectively referred to as ECMP multicast load splitting methods. ECMP multicast load splitting methods, thus, result in better load-sharing in networks where there are many traffic streams that utilize approximately the same amount of bandwidth.

If there are just a few (S, G) or (*, G) states flowing across a set of equal-cost links, the chance that they are well balanced is quite low. To overcome this limitation, precalculated source addresses--for (S, G) states--or rendezvous point (RP) addresses--for (*, G) states--can be used to achieve a reasonable form of load balancing. This limitation applies equally to the per-flow load splitting in Cisco Express Forwarding (CEF) or with EtherChannels: As long as there are only a few flows, those methods of load splitting will not result in good load distribution without some form of manual engineering.

The default behavior (the highest PIM neighbor behavior) does not result in any form of ECMP load-splitting in IP multicast, but instead selects the PIM neighbor that has the highest IP address among the next-hop PIM neighbors for the available paths. A next hop is considered to be a PIM neighbor when it displays in the output of the show ip pim neighbor command, which is the case when PIM hello messages have been received from it and have not timed out. If none of the available next hops are PIM neighbors, then simply the next hop with the highest IP address is chosen.

ECMP multicast load splitting traffic based on source address uses the S-hash algorithm, enabling the RPF interface for each (*, G) or (S, G) state to be selected among the available equal-cost paths, depending on the RPF address to which the state resolves. For an (S, G) state, the RPF address is the source address of the state; for a (*, G) state, the RPF address is the address of the RP associated with the group address of the state.

When ECMP multicast load splitting based on source address is configured, multicast traffic for different states can be received across more than just one of the equal-cost interfaces. The method applied by IPv4 multicast is quite similar in principle to the default per-flow load splitting in IPv4 CEF or the load splitting used with Fast and Gigabit EtherChannels. This method of ECMP multicast load splitting, however, is subject to polarization.

ECMP multicast load splitting based on source and group address uses a simple hash, referred to as the basic S-G-hash algorithm, which is based on source and group address. The basic S-G-hash algorithm is predictable because no randomization is used in coming up with the hash value. The S-G-hash mechanism, however, is subject to polarization because for a given source and group, the same hash is always picked irrespective of the device this hash is being calculated on.

The method used by ECMP multicast load splitting in IPv4 multicast allows for consistent load splitting in a network where the same number of equal-cost paths are present in multiple places in a topology. If an RP address or source addresses are calculated once to have flows split across N paths, then they will be split across those N paths in the same way in all places in the topology. Consistent load splitting allows for predictability, which, in turn, enables load splitting of IPv4 multicast traffic to be manually engineered. 006ab0faaa