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More frustrating still is the revolving door between Wall Street and government agencies. As the banks became more deregulated, the more speculation became a problem.
Derivatives, and credit default swaps, complicated trading schemes that most people do not understand is what caused the collapse of Lehman Brothers sending shockwaves through financial centres all over the world.
Credit agencies like Moody's and Standard and Poor gave firms like Bear Stearns, Lehman brothers, and Morgan Stanley A grade credit ratings within weeks before they nearly collapsed.
And also having one of their executives standing up in front of a congressional committee and telling congressmen that their ratings are just merely 'opinion'.
It becomes clear that this is not a problem that emerged from the housing boom early in president George W.
Bush's second term. Rather this was a systematic breakdown driven by a neoliberal ideology supported by Ivey league economic schools across the United States.
Inside Job is simply a story of bankers more interested in collecting bonuses and making more money than providing what should be an essential service.
What makes it even more frustrating is that many of the key figures behind the crisis are currently on Barak Obama's staff. The film leaves us with a bitter pill to swallow.
As Ferguson notes, Wall Street has returned to normal with no federal prosecutions against any of the guilty. And one of the most poignant scenes in the film comes from Robert Gnaizda, the former head of the Greenlining Institute, a consumer lobbyist group who laughingly dismisses recent legislation to regulate banks with a simple 'Hah'.
Inside Job helps explain many of the complex terms such as derivatives and insurance backed securities that confuse those not immersed in the banking community.
It is essential viewing for any citizen concerned about our broken system. Sign In. Keep track of everything you watch; tell your friends.
Full Cast and Crew. Release Dates. Official Sites. Company Credits. Technical Specs. Plot Summary. Plot Keywords. Parents Guide.
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Alternate Versions. Rate This. Takes a closer look at what brought about the financial meltdown. Director: Charles Ferguson.
Available on Amazon. Added to Watchlist. From metacritic. Everything New on Netflix in June. My Film Rankings for Great Documentary Films.
A third problem is cold start. If you want to create a new cache, you need some way of bootstrapping its contents without overloading other parts of the system.
So, here we have a contrast: on the one hand, creating a secondary index in a database is beautifully simple, one line of SQL — the database handles it automatically, keeping everything up-to-date, and even making the index transactionally consistent.
On the other hand, application-level cache maintenance is a complete mess of complicated invalidation logic, race conditions and operational problems.
Why should it be that way? Secondary indexes and caches are not fundamentally different. We said earlier that a secondary index is just a redundant data structure on the side, which structures the same data in a different way, in order to speed up read queries.
A cache is just the same. In other words, the contents of the cache are derived from the contents of the database.
We said that a secondary index is built by picking out one field from every record, and using that as the key in a dictionary.
But the end result is similar: if you lose your cache, you can rebuild it from the underlying database; thus, the contents of the cache are derived from the database.
In a read-through cache, this transformation happens on the fly, when there is a cache miss. But we could perhaps imagine making the process of building and updating a cache more systematic, and more similar to secondary indexes.
I said I was going to talk about four different aspects of database. They work like this:. When you now look at this view in the database, it looks somewhat like a table — you can use it in read queries like any other table.
So you can think of a view as a kind of convenient alias, a wrapper that allows you to create an abstraction, hiding a complicated query behind a simpler interface.
Contrast that with a materialized view, which is defined using almost identical syntax:. However, the implementation is totally different.
The database scans over the entire contents of those tables, executes that SELECT query on all of the data, and copies the results of that query into something like a temporary table.
Remember that with the non-materialized view, the database would expand the view into the underlying query at query time.
On the other hand, when you query a materialized view, the database can read its contents directly from disk, just like a table.
However, the big difference between a materialized view and application-managed caches is the responsibility for keeping it up-to-date. With a materialized view, you declare once how you want the materialized view to be defined, and the database takes care of building that view from a consistent snapshot of the underlying tables much like building a secondary index.
Moreover, when the data in the underlying tables changes, the database takes responsibility for maintaining the materialized view, keeping it up-to-date.
Some databases do this materialized view maintenance on an ongoing basis, and some require you to periodically refresh the view so that changes take effect.
Another feature of application-managed caches is that you can apply arbitrary business logic to the data before storing it in the cache, so that you can do less work at query time, or reduce the amount of data you need to cache.
Could a materialized view do something similar? In a relational database, materialized views are defined using SQL, so the transformations they can apply to the data are limited to the operations that are built into SQL which are very restricted compared to a general-purpose programming language.
However, many databases can also be extended using stored procedures — code that runs inside the database and can be called from SQL.
This would let you implement something like an application-level cache, including arbitrary business logic, running as a materialized view inside a database.
However, the idea of materialized views is nevertheless interesting. What they all have in common is that they are dealing with derived data in some way: some secondary data structure is derived from an underlying, primary dataset, via a transformation process.
I see a materialized view almost as a kind of cache that magically keeps itself up-to-date. Instead of putting all of the complexity of cache invalidation in the application risking race conditions and all the discussed problems , materialized views say that cache maintenance should be the responsibility of the data infrastructure.
If we started with a clean slate, without the historical baggage of existing databases, what would the ideal architecture for applications look like?
Think back to leader-based replication which we discussed earlier. You make your writes to a leader, which first applies the writes locally, and then sends those writes over the network to follower nodes.
In other words, the leader sends a stream of data changes to the followers. We discussed a logical log as one way of implementing this.
In a traditional database architecture, application developers are not supposed to think about that replication stream.
Yes, there are tools that do this , but in traditional databases they are on the periphery of what is supported, whereas the SQL interface is the dominant access method.
And in some ways this is reasonable — the relational model is a pretty good abstraction, which is why it has been so popular for several decades.
But SQL is not the last word in databases. What if we took that replication stream, and made it a first-class citizen in our data architecture?
What if we changed our infrastructure so that the replication stream was not an implementation detail, but a key part of the public interface of the database?
What if we turn the database inside out , take the implementation detail that was previously hidden, and make it a top-level concern?
What would that look like? You can format all your writes as immutable events facts , like we saw earlier in the context of a logical log.
Now each write is just an immutable event that you can append to the end of the transaction log. The transaction log is a really simple, append-only data structure.
There are various ways of implementing this, but one good choice for the transaction log is to use Apache Kafka.
It easily handles millions of writes per second on very modest hardware. Going through a leader would still be useful if you want to validate that writes meet certain constraints before writing them to the log.
But what about reads? Reading data that has been written to the log is now really inconvenient, because you have to scan the entire log to find the thing that you want.
The solution is to build materialized views from the writes in the transaction log. The materialized views are just like the secondary indexes we talked about earlier: data structures that are derived from the data in the log, and optimized for fast reading.
A materialized view is just a cached subset of the log , and you could rebuild it from the log at any time. There could be many different materialized views onto the same data: a key-value store, a full-text search index, a graph index, an analytics system, and so on.
All the stuff that was previously packed into a single monolithic software package is being broken out into modular components that can be composed in flexible ways.
If you use Kafka to implement the log, how do you implement these materialized views? With Samza, you write jobs that consume the events in a log, and build cached views of the data in the log.
When a job first starts up, it can build up its state by consuming all the events in the log. And on an ongoing basis, whenever a new event appears in the stream, it can update the view accordingly.
The view can be any existing database or index — Samza just provides the framework for processing the stream.
Anyone who wants to read data can now query those materialized views that are maintained by the Samza jobs. Those views are just databases, indexes or caches, and you can send read-only requests to them in the usual way.
Instead, you write to the log, and there is an explicit transformation process which takes the data on the log and applies it to the materialized views.
This separation of reads and writes is really the key idea here. Instead, we just keep an append-only log of immutable events.
These ideas are nothing new. To mention just a few examples, Event Sourcing is a data modelling technique based on the same principle; query languages like Datalog have been based on immutable facts for decades; databases like Datomic are built on immutability, enabling neat features like point-in-time historical queries; and the Lambda Architecture is one possible approach for dealing with immutable datasets at scale.
At many levels of the stack, immutability is being applied successfully. On the read side, we need to start thinking less about querying databases, and more about consuming and joining streams, and maintaining materialized views of the data in the form in which we want to read it.
Modern column stores have become very good at that. But in situations where you might use application-managed caches namely, an OLTP context where the queries are known in advance and predictable , materialized views are very helpful.
There are a few differences between a read-through cache which gets invalidated or updated within the application code and a materialized view which is maintained by consuming a log :.
Of course, such a big change in application architecture and database architecture means that many practical details need to be figured out: how do you deploy and monitor these stream processing jobs, how do you make the system robust to various kinds of fault, how do you integrate with existing systems, and so on?
But the good news is that all of these issues are being worked on. We are still figuring out how to build large-scale applications well — what techniques we can use to make our systems scalable, reliable and maintainable.
Put more bluntly, we need to figure out ways to stop our applications turning into big balls of mud.
One major subject not listed here is Project Scarlett, Microsoft's next-generation Xbox platform. While it's possible the console may be shown off as a surprise, fans probably shouldn't expect many new details about the upcoming system if any at all.
After all, if Microsoft wants to show off Scarlett in the best light possible, at this point the company would be more likely to do so at a dedicated press conference or at E3 in June.
Maybe we'll see a Scarlett game or two for a few seconds, but hardware news seems especially unlikely. Speaking of a lack of hardware, the prognosis for Project xCloud seems way more exciting.
With Google Stadia just days away from its founders launch date, Microsoft is likely ready to pounce on its streaming competition with a few punches of its own.
Early Stadia adopters have been frustrated by the service's not-so-wireless controller, weak launch game lineup and strange shipment schedule, so now would be the time for Microsoft to go in big.
Maybe they'll finally open up the xCloud beta to iOS users since it's currently exclusive to Android.
Overall we wouldn't expect X to be a blow-the-doors-off conference, but there will likely be a least one exciting reveal for everyone.
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