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Из курса от партнера University of Washington

Data Manipulation at Scale: Systems and Algorithms

648 оценки

University of Washington

Data Manipulation at Scale: Systems and Algorithms

648 оценки

Курс 1 из 4 — Specialization Data Science at Scale

Data analysis has replaced data acquisition as the bottleneck to evidence-based decision making --- we are drowning in it. Extracting knowledge from large, heterogeneous, and noisy datasets requires not only powerful computing resources, but the programming abstractions to use them effectively. The abstractions that emerged in the last decade blend ideas from parallel databases, distributed systems, and programming languages to create a new class of scalable data analytics platforms that form the foundation for data science at realistic scales. In this course, you will learn the landscape of relevant systems, the principles on which they rely, their tradeoffs, and how to evaluate their utility against your requirements. You will learn how practical systems were derived from the frontier of research in computer science and what systems are coming on the horizon. Cloud computing, SQL and NoSQL databases, MapReduce and the ecosystem it spawned, Spark and its contemporaries, and specialized systems for graphs and arrays will be covered. You will also learn the history and context of data science, the skills, challenges, and methodologies the term implies, and how to structure a data science project. At the end of this course, you will be able to: Learning Goals: 1. Describe common patterns, challenges, and approaches associated with data science projects, and what makes them different from projects in related fields. 2. Identify and use the programming models associated with scalable data manipulation, including relational algebra, mapreduce, and other data flow models. 3. Use database technology adapted for large-scale analytics, including the concepts driving parallel databases, parallel query processing, and in-database analytics 4. Evaluate key-value stores and NoSQL systems, describe their tradeoffs with comparable systems, the details of important examples in the space, and future trends. 5. “Think” in MapReduce to effectively write algorithms for systems including Hadoop and Spark. You will understand their limitations, design details, their relationship to databases, and their associated ecosystem of algorithms, extensions, and languages. write programs in Spark 6. Describe the landscape of specialized Big Data systems for graphs, arrays, and streams

Из урока

NoSQL: Systems and Concepts

NoSQL systems are purely about scale rather than analytics, and are arguably less relevant for the practicing data scientist. However, they occupy an important place in many practical big data platform architectures, and data scientists need to understand their limitations and strengths to use them effectively.

HBase, Megastore3:53

Spanner cont'd, Google Systems6:25

MapReduce-based Systems5:06

Bringing Back Joins4:32

NoSQL Rebuttal4:55

Познакомьтесь с преподавателями

Bill Howe
Director of Research
Scalable Data Analytics

[MUSIC]

Fast forward one more year and so there's 2012 paper on the system called Spanner.

And I just wanna mention these quotes and

then we'll talk a little bit about the system.

And this one is still sort of being explored by the community.

It's not available actually for use, but the paper is being explored and

the idea is being explored.

So for example, you don't see an open source, actually that's not true you do

see, there has been a couple open source with limitations of the ideas in Spanner.

But they're not quite as popular as some of the open source limitations of

the other Google systems.

Okay. So, it says even though many

projects happily use Bigtable,

we've also consistently received complaints from users of Bigtable.

It can be difficult to use for certain kinds of applications.

Those that have complex, evolving schemas or those that want strong consistency in

the presence of wide-area replication, okay?

And so then he goes on to say, we believe it is better to have application

programmers deal with performance problems due to overuse of transactions as

bottlenecks arise, rather than always cutting around the lack of transactions.

And so this, the database community could have said well,

sure, you know well duh, right?

That's exactly the point is that system supplied support for

transactions is always a win, right?

Because it's, difficult, error prone, and

expensive to try to do this at the application level.

And more importantly,

it's fundamentally wrong in some sense to do it at the application level,

because the application doesn't have global knowledge of what's going on.

Only the system does.

So, all those scanners scalable in the number of nodes, or the final quote here.

The node-local data structures have relatively poor performance on complex SQL

queries, because they were designed for simple key-value accesses.

And then, algorithms and data structures from the database literature can improve

singlenode performance a great deal.

You know, it's

somewhat of a Google-style approach to the problem of reboot everything,

rebuild it all from scratch and then sort of cherry pick and bring things in.

So, this has been working pretty well.

And they have fantastic impact in the community.

But there is a lot out there in the database literature and

in database systems that could have been used from the start.

I mean, in fact, trying to start from the beginning and just say we're going to

build a big Google-style parallel database may have been a good choice.

Rather than sort of getting completely away from it and

then coming back incrementally and find yourself in a SQL system.

Now, I sort of skipped what Spanner is, but it's a planet scale database system.

There is a SQL like language, well actually,

let's go back to our, I know what I'm missing.

I'm missing our table here.

Let me flip back up to it.

So here it is down here.

I'm missing this slide here where I showed it.

So really big scale.

Primary axis is the ucaine axis.

By other attributes.

There are transactions, and in fact, they're global this time.

They're real, real, actual transactions.

It's not clear to me whether joins are supported.

I suspect they are because cuz they keep talking about SQL, but

I couldn't find an example on whether it is or not.

There is notion of schema and

they do sort of protect against data that doesn't conform to the schema.

There is some notion of logical data independence,

although they don't talk about it much.

There is a SQL-like decorative language on top of it.

I didn't see much evidence that they're doing a whole lot of fancy optimization.

And I did just show you that quote where they say that

performance is sort of poor on complex analytic queries.

But that's something that probably can come along, somewhat quickly.

So that's Spanner at a high level.

Let me give you a couple more details about what the system does.

So the data model here is this notion of directories.

And these are a set of continuous keys with a shared prefix.

So you can think of it kind of like a tablet was in Bigtable, but

now they have this notion of multiple, logical tables being sort of interleaved.

And so, if you're not used to staring at the syntax, don't worry too much.

But those of you who are thinking in terms of DDL and a relational database,

they have kind of a create table language that looks like this.

You create table users with two columns, and you give it this keyword,

DIRECTORY, and then you're gonna create table albums with some columns, and

then you have this keyword INTERLEAVE IN PARENT Users.

And what you end up with is something like this,

where there's a user with all of its albums.

There's a user with all of its albums.

As you can see here, that what we've been talking about, all these different systems

are experimenting with ways of getting these nested date structures, hierarchical

data structures that look a lot like what we saw way back in the sixties.

Right? And

the motivation is the same as it was then.

It's actually really, really fast.

When you're gonna access, when you wanna pull up a user and then immediately pull

up all of its albums, it's really fast accesses this way.

But, I'd probably speculate that the reasons why relational,

the relational approach eventually replace these can and will happen here as well.

Is that performance is not the number one priority a lot of the time.

It's minimizing the amount of developer headaches.

Okay.

So, needs to be seen.

But I think that this increment will walk step towards

a big new scalable relational database is underway.

Now again that doesn't mean that I'm saying use all the old databases.

They really were designed for a different workload and

there really is no evidence that they scale to some of these levels.

But that doesn't mean that you throw out everything that we learned.

[MUSIC]

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