Holy buckets.

Two buckets.

Evening, Buckets. Evening.

It's raining buckets outside.

They carried water in buckets.

The rain came down in buckets.

That should slow those buckets down.

Attempts to fight the fire with buckets soon proved fruitless.

This is huge industrial buckets making this kind of bread.

Into the 100's, 10's and 1's buckets.

And a few buckets later, there's no way you'd know.

Let's say we have a hash table with only 2 buckets.

I have 1,000 now that I can distribute into these 3 buckets.

So, what computer architects have to do is we need to figure out how to take buckets and buckets of more transistors and turn them into more performance.

This is just the number of entries divided by the number of buckets.

If we double the number of buckets, the average lookup time is decreasing significantly.

It could be the case that with 2 buckets I have a good distribution.

Once the buckets get too full, or the load factor is too high, you want to create a new table with more buckets, and you've got to copy all your entries to it.

So anyway, just going back on the different tranches of debt, or buckets of debt.

Statement 3 says it is always better to have more buckets in a hash table.

So you'd end up with 3 million buckets, if you added one more entry beyond that.

And I can plot a histogram with more buckets, with more bins, with say, 50 bins.

But as you increase above that 2 3 threshold, then you'd double the size of the buckets.

Real world implementations of hash tables try to shoot for a ratio between the number of buckets they have and the number of keywords, increasing the number of buckets accordingly as the number of keywords in the hash table decrease and increase.

All the water for cooking, cleaning and bathing had to be carried in buckets and pails in from the outside.

But it means you're using less memory, because you don't have all those empty lists for all the empty buckets.

Then when you're done with that, you switch them over and you have the larger table with new buckets going.

The first reason is because changing the number of buckets, be it more or less, changes where keywords hash to.

You'd want billions of buckets in your hash tables so they'd never have to have more than one entry in a bucket.

We were doing example hash tables, where the number of buckets was very small, and each bucket had many entries in it.

If you have one bucket that holds two gallons... and another bucket that holds five gallons... how many buckets do you have?

So, if you view them as buckets you can say, okay, from the get go, the number in the sum bucket is 0.

I noticed they were looking under things and looking for things, and they wanted to know where the buckets were hidden, you know?

Once his mother had undertaken a major cleaning of Gregor's room, which she had only completed successfully after using a few buckets of water.

A common way to visualize (this isn't necessarily wrong, it's sometimes an easier way to think about it) is to view them as buckets.

And once you're in bankruptcy court it does tend to be a negotiation between the different, you can almost view it as buckets, of debt.

In order to show it, we need an example, so let me give you an example dictionary with 3 buckets and 3 keywords in here.

After 8 months of explosive growth in business, I hope so, Peter finds that he needs to double the number of buckets in his hash table.

Statement 2 says we should expect the time to look up most keywords in the hash table will decrease as we increase the number of buckets.

So this means that if you had about a million entries in your hash table, you would expect to have about a million and a half buckets.

He also finds that the hash buckets are not utilized evenly, since he did not understand the hashing of Klingon very well. Sounds like a hard problem.

The question where it was N divided by B where you're looking at the impact of the change of the number of buckets and the number of entries.

And they would just kind of throw the buckets down there and hope that it would somehow go away, and of course it never really would go away.

So in this case, we've got three Siftables configured to be paint buckets and I can use them to pour color into that central one, where they get mixed.

One thing you have to worry about when you do that is if you're just looking at the number of keywords and the number of buckets, that's the average size.