Article: If all these new DBMS technologies are so scalable, why are Oracle and DB2 still on top of TPC-C? A roadmap to end their dominance.

http://dbmsmusings.blogspot.com/2012/05/if-all-these-new-dbms-technologies-are.html

(This post is coau­thored by Alexan­der Thom­son and Daniel Abadi)
In the last decade, data­base tech­nol­o­gy has arguably pro­gressed fur­thest along the scal­a­bil­i­ty dimen­sion. There have been hun­dreds of research papers, dozens of open-source projects, and numer­ous star­tups attempt­ing to improve the scal­a­bil­i­ty of data­base tech­nol­o­gy. Many of these new tech­nolo­gies have been extreme­ly influential---some papers have earned thou­sands of cita­tions, and some new sys­tems have been deployed by thou­sands of enter­pris­es.

So let's ask a sim­ple ques­tion: If all these new tech­nolo­gies are so scal­able, why on earth are Ora­cle and DB2 still on top of the TPC-C stand­ings? Go to the TPC-C Web­site with the top 10 results in raw trans­ac­tions per sec­ond. As of today (May 16th, 2012), Ora­cle 11g is used for 3 of the results (includ­ing the top result), 10g is used for 2 of the results, and the rest of the top 10 is filled with var­i­ous ver­sions of DB2. How is tech­nol­o­gy designed decades ago still dom­i­nat­ing TPC-C? What hap­pened to all these new tech­nolo­gies with all these scal­a­bil­i­ty claims?

The sur­pris­ing truth is that these new DBMS tech­nolo­gies are not list­ed in theTPC-C top ten results not because that they do not care enough to enter, but rather because they would not win if they did.

To under­stand why this is the case, one must under­stand that scal­a­bil­i­ty does not come for free. Some­thing must be sac­ri­ficed to achieve high scal­a­bil­i­ty. Today, there are three major cat­e­gories of trade­off that can be exploit­ed to make a sys­tem scale. The new tech­nolo­gies basi­cal­ly fall into two of these cat­e­gories; Ora­cle and DB2 fall into a third. And the later parts of this blog post describes research from our group at Yale that intro­duces a fourth cat­e­go­ry of trade­off that pro­vides a roadmap to end the dom­i­nance of Ora­cle and DB2.

These cat­e­gories are:

(1) Sac­ri­fice ACID for scal­a­bil­i­ty. Our pre­vi­ous post on this topic dis­cussed this in detail. Basi­cal­ly we argue that a major class of new scal­able tech­nolo­gies fall under the cat­e­go­ry of "NoSQL" which achieves scal­a­bil­i­ty by drop­ping ACID guar­an­tees, there­by allow­ing them to eschew two phase lock­ing, two phase com­mit, and other imped­i­ments to con­cur­ren­cy and proces­sor inde­pen­dence that hurt scal­a­bil­i­ty. All of these sys­tems that relax ACID are imme­di­ate­ly inel­i­gi­ble to enter the TPC-C com­pe­ti­tion since ACID guar­an­tees are one of TPC-C's require­ments. That's why you don't see NoSQL data­bas­es in the TPC-C top 10---they are imme­di­ate­ly dis­qual­i­fied.

(2) Reduce trans­ac­tion flex­i­bil­i­ty for scal­a­bil­i­ty. There are many so-called"NewSQL" data­bas­es that claim to be both ACID-compliant and scal­able. And these claims are true---to a degree. How­ev­er, the fine print is that they are only lin­ear­ly scal­able when trans­ac­tions can be com­plete­ly iso­lat­ed to a sin­gle "par­ti­tion" or "shard" of data. While these NewSQL data­bas­es often hide the com­plex­i­ty of shard­ing from the appli­ca­tion devel­op­er, they still rely on the shards to be fair­ly inde­pen­dent. As soon as a trans­ac­tion needs to span mul­ti­ple shards (e.g., update two dif­fer­ent user records on two dif­fer­ent shards in the same atom­ic trans­ac­tion), then these NewSQL sys­tems all run into prob­lems. Some sim­ply reject such trans­ac­tions. Oth­ers allow them, but need to per­form two phase com­mit or other agree­ment pro­to­cols in order to ensure ACID com­pli­ance (since each shard may fail inde­pen­dent­ly). Unfor­tu­nate­ly, agree­ment pro­to­cols such as two phase com­mit come at a great scal­a­bil­i­ty cost (see our 2010 paper that explains why). There­fore, NewSQL data­bas­es only scale well if multi-shard trans­ac­tions (also called "dis­trib­uted trans­ac­tions" or "multi-partition trans­ac­tions") are very rare. Unfor­tu­nate­ly for these data­bas­es, TPC-C mod­els a fair­ly rea­son­able retail appli­ca­tion where cus­tomers buy prod­ucts and the inven­to­ry needs to be updat­ed in the same atom­ic trans­ac­tion. 10% of TPC-C New Order trans­ac­tions involve cus­tomers buy­ing prod­ucts from a "remote" ware­house, which is gen­er­al­ly stored in a sep­a­rate shard. There­fore, even for basic appli­ca­tions like TPC-C, NewSQL data­bas­es lose their scal­a­bil­i­ty advan­tages. That's why the NewSQL data­bas­es do not enter TPC-C results --- even just 10% of multi-shard trans­ac­tions caus­es their per­for­mance to degrade rapid­ly.

(3) Trade cost for scal­a­bil­i­ty. If you use high end hard­ware, it is pos­si­ble to get stun­ning­ly high trans­ac­tion­al through­put using old data­base tech­nolo­gies that don't have shared-nothing hor­i­zon­tal­ly scal­a­bil­i­ty. Ora­cle tops TPC-C with an incred­i­bly high through­put of 500,000 trans­ac­tions per sec­ond. There exists no appli­ca­tion in the mod­ern world that pro­duces more than 500,000 trans­ac­tions per sec­ond (as long as humans are ini­ti­at­ing the transactions---machine-generated trans­ac­tions are a dif­fer­ent story). There­fore, Ora­cle basi­cal­ly has all the scal­a­bil­i­ty that is need­ed for human scale appli­ca­tions. The only down­side is cost---the Ora­cle sys­tem that is able to achieve 500,000 trans­ac­tions per sec­ond costs a pro­hib­i­tive $30,000,000!

Since the first two types of trade­offs are imme­di­ate dis­qual­i­fiers for TPC-C, the only remain­ing thing to give up is cost-for-scale, and that's why the old data­base tech­nolo­gies are still dom­i­nat­ing TPC-C. None of these new tech­nolo­gies can han­dle both ACID and 10% remote trans­ac­tions.

A fourth approach...

TPC-C is a very rea­son­able appli­ca­tion. New tech­nolo­gies should be able to han­dle it. There­fore, at Yale we set out to find a new dimen­sion in this trade­off space that could allow a sys­tem to han­dle TPC-C at scale with­out cost­ing $30,000,000. Indeed, we are pre­sent­ing a paper next week at SIG­MOD (see the full paper) that describes a sys­tem that can achieve 500,000 ACID-compliant TPC-C New Order trans­ac­tions per sec­ond using com­mod­i­ty hard­ware in the cloud. The cost to us to run these exper­i­ments was less than $300 (of course, this is rent­ing hard­ware rather than buy­ing, so it's hard to com­pare prices --- but still --- a fac­tor of 100,000 less than $30,000,000 is quite large).

Calvin, our pro­to­type sys­tem designed and built by a large team of researchers at Yale that include Thad­deus Dia­mond, Shu-Chun Weng, Kun Ren, Philip Shao, Anton Petrov, Michael Giuf­fri­da, and Aaron Segal (in addi­tion to the authors of this blog post), explores a trade­off very dif­fer­ent from the three described above. Calvin requires all trans­ac­tions to be exe­cut­ed fully server-side and sac­ri­fices the free­dom to non-deterministically abort or reorder trans­ac­tions on-the-fly dur­ing exe­cu­tion. In return, Calvin gets scal­a­bil­i­ty, ACID-compliance, and extreme­ly low-overhead multi-shard trans­ac­tions over a shared-nothing archi­tec­ture. In other words, Calvin is designed to han­dle high-volume OLTP through­put on shard­ed data­bas­es on cheap, com­mod­i­ty hard­ware stored local­ly or in the cloud. Calvin sig­nif­i­cant­lyimproves the scal­a­bil­i­ty over our pre­vi­ous approach to achiev­ing deter­min­ism in data­base sys­tems.

Scal­ing ACID

The key to Calvin's strong per­for­mance is that it reor­ga­nizes the trans­ac­tion exe­cu­tion pipeline nor­mal­ly used in DBMSs accord­ing to the prin­ci­ple: do all the "hard" work before acquir­ing locks and begin­ning exe­cu­tion. In par­tic­u­lar, Calvin moves the fol­low­ing stages to the front of the pipeline:

• Repli­ca­tion. In tra­di­tion­al sys­tems, repli­cas agree on each mod­i­fi­ca­tion to data­base state only after some trans­ac­tion has made the change at some "mas­ter" repli­ca. In Calvin, all repli­cas agree in advance on the sequence of trans­ac­tions that they will (deter­min­is­ti­cal­ly) attempt to exe­cute.
• Agree­ment between par­tic­i­pants in dis­trib­uted trans­ac­tions. Data­base sys­tems tra­di­tion­al­ly use two-phase com­mit (2PC) to han­dle dis­trib­uted trans­ac­tions. In Calvin, every node sees the same glob­al sequence of trans­ac­tion requests, and is able to use this already-agreed-upon infor­ma­tion in place of a com­mit pro­to­col.
• Disk access­es. In our VLDB 2010 paper, we observed that deter­min­is­tic sys­tems per­formed ter­ri­bly in disk-based envi­ron­ments due to hold­ing locks for the 10ms+ dura­tion of read­ing the need­ed data from disk, since they can­not reorder con­flict­ing trans­ac­tions on the fly. Calvin gets around this set­back by prefetch­ing into mem­o­ry all records that a trans­ac­tion will need dur­ing the repli­ca­tion phase---before locks are even acquired.

As a result, each trans­ac­tion's user-specified logic can be exe­cut­ed at each shard with an absolute min­i­mum of run­time syn­chro­niza­tion between shards or repli­cas to slow it down, even if the trans­ac­tion's logic requires it to access records at mul­ti­ple shards. By min­i­miz­ing the time that locks are held, con­cur­ren­cy can be great­ly increased, there­by lead­ing to near-linear scal­a­bil­i­ty on a com­mod­i­ty clus­ter of machines. 

Strong­ly con­sis­tent glob­al repli­ca­tion 

Calvin's deter­min­is­tic exe­cu­tion seman­tics pro­vide an addi­tion­al ben­e­fit: repli­cat­ing trans­ac­tion­al input is suf­fi­cient to achieve strong­ly con­sis­tent repli­ca­tion. Since repli­cat­ing batch­es of trans­ac­tion requests is extreme­ly inex­pen­sive and hap­pens before the trans­ac­tions acquire locks and begin exe­cut­ing, Calvin's trans­ac­tion­al through­put capac­i­ty does not depend at all on its repli­ca­tion con­fig­u­ra­tion. 

In other words, not only can Calvin can run 500,000 trans­ac­tions per sec­ond on 100 EC2 instances in Ama­zon's US East (Vir­ginia) data cen­ter, it can main­tain strongly-consistent, up-to-date 100-node repli­cas in Ama­zon's Europe (Ire­land) and US West (Cal­i­for­nia) data centers---at no cost to through­put. 

Calvin accom­plish­es this by hav­ing repli­cas per­form the actu­al pro­cess­ing of trans­ac­tions com­plete­ly inde­pen­dent­ly of one anoth­er, main­tain­ing strong con­sis­ten­cy with­out hav­ing to con­stant­ly syn­chro­nize trans­ac­tion results between repli­cas. (Calvin's end-to-end trans­ac­tion laten­cy does depend on mes­sage delays between repli­cas, of course---there is no get­ting around the speed of light.) 

Flex­i­ble data model 

So where does Calvin fall in the OldSQL/NewSQL/NoSQL tri­choto­my? 

Actu­al­ly, nowhere. Calvin is not a data­base sys­tem itself, but rather a trans­ac­tion sched­ul­ing and repli­ca­tion coor­di­na­tion ser­vice. We designed the sys­tem to inte­grate with any data stor­age layer, rela­tion­al or oth­er­wise. Calvin allows user trans­ac­tion code to access the data layer freely, using any data access lan­guage or inter­face sup­port­ed by the under­ly­ing stor­age engine (so long as Calvin can observe which records user trans­ac­tions access). The exper­i­ments pre­sent­ed in the paper use a cus­tom key-value store. More recent­ly, we've hooked Calvin up to Google's Lev­elDB and added sup­port for SQL-based data access with­in trans­ac­tions, build­ing rela­tion­al tables on top of Lev­elDB's effi­cient sorted-string stor­age. 

From an appli­ca­tion devel­op­er's point of view, Calvin's pri­ma­ry lim­i­ta­tion com­pared to other sys­tems is that trans­ac­tions must be exe­cut­ed entire­ly server-side. Calvin has to know in advance what code will be exe­cut­ed for a given trans­ac­tion. Users may pre-define trans­ac­tions direct­ly in C++, or sub­mit arbi­trary Python code snip­pets on-the-fly to be parsed and exe­cut­ed as trans­ac­tions. 

For some appli­ca­tions, this require­ment of com­plete­ly server-side trans­ac­tions might be a dif­fi­cult lim­i­ta­tion. How­ev­er, many appli­ca­tions pre­fer to exe­cute trans­ac­tion code on the data­base serv­er any­way (in the form of stored pro­ce­dures), in order to avoid mul­ti­ple round trip mes­sages between the data­base serv­er and appli­ca­tion serv­er in the mid­dle of a trans­ac­tion. 

If this lim­i­ta­tion is accept­able, Calvin presents a nice alter­na­tive in the trade­off space to achiev­ing high scal­a­bil­i­ty with­out sac­ri­fic­ing ACID or multi-shard trans­ac­tions. Hence, we believe that ourSIG­MOD paper may present a roadmap for over­com­ing the scal­a­bil­i­ty dom­i­nance of the decades-old data­base solu­tions on tra­di­tion­al OLTP work­loads. We look for­ward to debat­ing the mer­its of this approach in the weeks ahead (and Alex will be pre­sent­ing the paper at SIG­MOD next week).

InfoQ: Panel: Multicore, Manycore, and Cloud Computing

InfoQ: Panel: Multicore, Manycore, and Cloud Computing

Note: Biggest challenges are the correctness and performance of parallel execution.
In term of correctness: false sharing, atomicity, consistency model.
In term of performance: locking, synchronization, deadlock.

Building Highly Available Systems in Erlang

InfoQ: Building Highly Available Systems in Erlang:

Key ideas:


The process approach to fault isolation advocates that the process
software be fail-fast, it should either function correctly or it
should detect the fault, signal failure and stop operating.

  Processes are  made fail-fast  by defensive programming.  They check
all their inputs, intermediate results and data structures as a matter
of course. If any error is detected, they signal a failure and stop. In
the  terminology of  [Christian],  fail-fast software  has small  fault
detection latency.

Are Cloud Based Memory Architectures the Next Big Thing?

Are Cloud Based Memory Architectures the Next Big Thing?:

We are on the edge of two potent technological changes: Clouds and Memory Based Architectures. This evolution will rip open a chasm where new players can enter and prosper. Google is the master of disk. You can't beat them at a game they perfected. Disk based databases like SimpleDB and BigTable are complicated beasts, typical last gasp products of any aging technology before a change. The next era is the age of Memory and Cloud which will allow for new players to succeed. The tipping point will be soon.

Let's take a short trip down web architecture lane:

It's 1993: Yahoo runs on FreeBSD, Apache, Perl scripts and a SQL database

It's 1995: Scale-up the database.

It's 1998: LAMP

It's 1999: Stateless + Load Balanced + Database + SAN

It's 2001: In-memory data-grid.

It's 2003: Add a caching layer.

It's 2004: Add scale-out and partitioning.

It's 2005: Add asynchronous job scheduling and maybe a distributed file system.

It's 2007: Move it all into the cloud.

It's 2008: Cloud + web scalable database.

It's 20??: Cloud + Memory Based Architectures

You may disagree with the timing of various innovations and you would be correct. I couldn't find a history of the evolution of website architectures, so I just made stuff up. If you have any better information please let me know.

Why might cloud based memory architectures be the next big thing? For now we'll just address the memory based architecture part of the question, the cloud component is covered a little later.

Behold the power of keeping data in memory:

Google query results are now served in under an astonishingly fast 200ms, down from 1000ms in the olden days. The vast majority of this great performance improvement is due to holding indexes completely in memory. Thousands of machines process each query in order to make search results appear nearly instantaneously.

This text was adapted from notes on Google Fellow Jeff Dean keynote speech at WSDM 2009.

Google isn't the only one getting a performance bang from moving data into memory. Both LinkedInand Digg keep the graph of their network social network in memory. Facebook has northwards of 800 memcached servers creating a reservoir of 28 terabytes of memory enabling a 99% cache hit rate. Even little guys can handle 100s of millions of events per day by using memory instead of disk.

With their new Unified Computing strategy Cisco is also entering the memory game. Their new machines "will be focusing on networking and memory" with servers crammed with 384 GB of RAM, fast processors, and blazingly fast processor interconnects. Just what you need when creating memory based systems.

Memory Is The System Of Record

What makes Memory Based Architectures different from traditional architectures is that memory is the system of record. Typically disk based databases have been the system of record. Disk has been King, safely storing data away within its castle walls. Disk being slow we've ended up wrapping disks in complicated caching and distributed file systems to make them perform.

Sure, memory is used as all over the place as cache, but we're always supposed to pretend that cache can be invalidated at any time and old Mr. Reliable, the database, will step in and provide the correct values. In Memory Based Architectures memory is where the "official" data values are stored.

Caching also serves a different purpose. The purpose behind cache based architectures is to minimize the data bottleneck through to disk. Memory based architectures can address the entire end-to-end application stack. Data in memory can be of higher reliability and availability than traditional architectures.

Memory Based Architectures initially developed out of the need in some applications spaces for very low latencies. The dramatic drop of RAM prices along with the ability of servers to handle larger and larger amounts of RAM has caused memory architectures to verge on going mainstream. For example, someone recently calculated that 1TB of RAM across 40 servers at 24 GB per server would cost an additional $40,000. Which is really quite affordable given the cost of the servers. Projecting out, 1U and 2U rack-mounted servers will soon support a terabyte or more or memory.

RAM = High Bandwidth And Low Latency

Why are Memory Based Architectures so attractive? Compared to disk RAM is a high bandwidth and low latency storage medium. Depending on who you ask the bandwidth of RAM is 5 GB/s. The bandwidth of disk is about 100 MB/s. RAM bandwidth is many hundreds of times faster. RAM wins. Modern hard drives have latencies under 13 milliseconds. When many applications are queued for disk reads latencies can easily be in the many second range. Memory latency is in the 5 nanosecond range. Memory latency is 2,000 times faster. RAM wins again.

RAM Is The New Disk

The superiority of RAM is at the heart of the RAM is the New Disk paradigm. As an architecture it combines the holy quadrinity of computing:

Performance is better because data is accessed from memory instead of through a database to a disk.

Scalability is linear because as more servers are added data is transparently load balanced across the servers so there is an automated in-memory sharding.

Availability is higher because multiple copies of data are kept in memory and the entire system reroutes on failure.

Application development is faster because there’s only one layer of software to deal with, the cache, and its API is simple. All the complexity is hidden from the programmer which means all a developer has to do is get and put data.

Access disk on the critical path of any transaction limits both throughput and latency. Committing a transaction over the network in-memory is faster than writing through to disk. Reading data from memory is also faster than reading data from disk. So the idea is to skip disk, except perhaps as an asynchronous write-behind option, archival storage, and for large files.

Or Is Disk Is The The New RAM

To be fair there is also a Disk is the the new RAM, RAM is the New Cache paradigm too. This somewhat counter intuitive notion is that a cluster of about 50 disks has the same bandwidth of RAM, so the bandwidth problem is taken care of by adding more disks.

The latency problem is handled by reorganizing data structures and low level algorithms. It's as simple as avoiding piecemeal reads and organizing algorithms around moving data to and from memory in very large batches and writing highly parallelized programs. While I have no doubt this approach can be made to work by very clever people in many domains, a large chunk of applications are more time in the random access domain space for which RAM based architectures are a better fit.

Grids And A Few Other Definitions

There's a constellation of different concepts centered around Memory Based Architectures that we'll need to understand before we can understand the different products in this space. They include:

Compute Grid - parallel execution. A Compute Grid is a set of CPUs on which calculations/jobs/work is run. Problems are broken up into smaller tasks and spread across nodes in the grid. The result is calculated faster because it is happening in parallel.

Data Grid - a system that deals with data — the controlled sharing and management of large amounts of distributed data.

In-Memory Data Grid (IMDG) - parallel in-memory data storage. Data Grids are scaled horizontally, that is by adding more nodes. Data contention is removed removed by partitioning data across nodes.

Colocation - Business logic and object state are colocated within the same process. Methods are invoked by routing to the object and having the object execute the method on the node it was mapped to. Latency is low because object state is not sent across the wire.

Grid Computing - Compute Grids + Data Grids

Cloud Computing - datacenter + API. The API allows the set of CPUs in the grid to be dynamically allocated and deallocated.

Who Are The Major Players In This Space?

With that bit of background behind us, there are several major players in this space (in alphabetical order):

Coherence - is a peer-to-peer, clustered, in-memory data management system. Coherence is a good match for applications that need write-behind functionality when working with a database and you require multiple applications have ACID transactions on the database. Java, JavaEE, C++, and .NET.

GemFire - an in-memory data caching solution that provides low-latency and near-zero downtime along with horizontal & global scalability. C++, Java and .NET.

GigaSpaces - GigaSpaces attacks the whole stack: Compute Grid, Data Grid, Message, Colocation, and Application Server capabilities. This makes for greater complexity, but it means there's less plumbing that needs to be written and developers can concentrate on writing business logic. Java, C, or .Net.

GridGain - A compute grid that can operate over many data grids. It specializes in the transparent and low configuration implementation of features. Java only.

Terracotta - Terracotta is network-attached memory that allows you share memory and do anything across a cluster. Terracotta works its magic at the JVM level and provides: high availability, an end of messaging, distributed caching, a single JVM image. Java only.

WebSphere eXtreme Scale. Operates as an in-memory data grid that dynamically caches, partitions, replicates, and manages application data and business logic across multiple servers.

This class of products has generally been called In-Memory Data Grids (IDMG), though not all the products fit snugly in this category. There's quite a range of different features amongst the different products.

I tossed IDMG the acronym in favor of Memory Based Architectures because the "in-memory" part seems redundant, the grid part has given way to the cloud, the "data" part really can include both data and code. And there are other architectures that will exploit memory yet won't be classic IDMG. So I just used Memory Based Architecture as that's the part that counts.

Given the wide differences between the products there's no canonical architecture. As an example here's a diagram of how GigaSpaces In-Memory-Data-Grid on the Cloud works.





Some key points to note are:

A POJO (Plain Old Java Object) is written through a proxy using a hash-based data routing mechanism to be stored in a partition on a Processing Unit. Attributes of the object are used as a key. This is straightforward hash based partitioning like you would use with memcached.

You are operating through GigaSpace's framework/container so they can automatically handle things like messaging, sending change events, replication, failover, master-worker pattern, map-reduce, transactions, parallel processing, parallel query processing, and write-behind to databases.

Scaling is accomplished by dividing your objects into more partitions and assigning the partitions to Processing Unit instances which run on nodes-- a scale-out strategy. Objects are kept in RAM and the objects contain both state and behavior. A Service Grid component supports the dynamic creation and termination of Processing Units.

Not conceptually difficult and familiar to anyone who has used caching systems like memcached. Only is this case memory is not just a cache, it's the system of record.

Obviously there are a million more juicy details at play, but that's the gist of it. Admittedly GigaSpaces is on the full featured side of the product equation, but from a memory based architecture perspective the ideas should generalize. When you shard a database, for example, you generally lose the ability to execute queries, you have to do all the assembly yourself. By using GigaSpaces framework you get a lot of very high-end features like parallel query processing for free.

The power of this approach certainly comes in part from familiar concepts like partitioning. But the speed of memory versus disk also allows entire new levels of performance and reliability in a relatively simple and easy to understand and deploy package.

NimbusDB - The Database In The Cloud

Jim Starkey, President of NimbusDB, is not following the IDMG gang's lead. He's taking a completely fresh approach based on thinking of the cloud as a new platform unto itself. Starting from scratch, what would a database for the cloud look like?

Jim is in position to answer this question as he has created a transactional database engine for MySQL named Falcon and added multi-versioning support to InterBase, the first relational database to feature MVCC (Multiversion Concurrency Control).

What defines the cloud as a platform? Here's are some thoughts from Jim I copied out of the Cloud Computing group. You'll notice I've quoted Jim way way too much. I did that because Jim is an insightful guy, he has a lot of interesting things to say, and I think he has a different spin on the future of databases in the cloud than anyone else I've read. He also has the advantage of course of not having a shipping product, but we shall see.

I've probably said this before, but the cloud is a new computing platform that some have learned to exploit, others are scrambling to master, but most people will see as nothing but a minor variation on what they're already doing. This is not new. When time sharing as invented, the batch guys considered it as remote job entry, just a variation on batch. When departmental computing came along (VAXes, et al), the timesharing guys considered it nothing but timesharing on a smaller scale. When PCs and client/server computing came along, the departmental computing guys (i.e. DEC), considered PCs to be a special case of smart terminals. And when the Internet blew into town, the client server guys considered it as nothing more than a global scale LAN. So the batchguys are dead, the timesharing guys are dead, the departmental computing guys are dead, and the client server guys are dead. Notice a pattern?

The reason that databases are important to cloud computing is that virtually all applications involve the interaction of client data with a shared, persistent data store. And while application processing can be easily scaled, the limiting factor is the database system. So if you plan to do anything more than play Tetris in the cloud, the issue of database management should be foremost in your mind.

Disks are the limiting factors in contemporary database systems. Horrible things, disk. But conventional wisdom is that you build a clustered database system by starting with a distributed file system. Wrong. Evolution is faster processors, bigger memory, better tools. Revolution
is a different way of thinking, a different topology, a different way of putting the parts together.

What I'm arguing is that a cloud is a different platform, and what works well for a single computer doesn't work at all well in cloud, and things that work well in a cloud don't work at all on the single computer system. So it behooves us to re-examine a lot an ancient and honorable assumptions to see if they make any sense at all in this brave new world.

Sharing a high performance disk system is fine on a single computer, troublesome in a cluster, and miserable on a cloud.

I'm a database guy who's had it with disks. Didn't much like the IBM 1301, and disks haven't gotten much better since. Ugly, warty, slow, things that require complex subsystems to hide their miserable characteristics. The alternative is to use the memory in a cloud as a distributed L2
cache. Yes, disks are still there, but they're out of the performance loop except for data so stale that nobody has it memory.

Another machine or set of machines is just as good as a disk. You can quibble about reliable power, etc, but write queuing disks have the same problem.

Once you give up the idea of logs and page caches in favor of asynchronous replications, life gets a great deal brighter. It really does make sense to design to the strengths of cloud(redundancy) rather than their weaknesses (shared anything).

And while one guys is fetching his 100 MB per second, the disk is busy and everyone else is waiting in line contemplating existence. Even the cheapest of servers have two gigabit ethernet channels and switch. The network serves everyone in parallel while the disk is single threaded

I favor data sharing through a formal abstraction like a relational database. Shared objects are things most programmers are good at handling. The fewer the things that application developers need to manage the more likely it is that the application will work.

I buy the model of object level replication, but only as a substrate for something with a more civilized API. Or in other words, it's a foundation, not a house.

I'd much rather have a pair of quad-core processors running as independent servers than contending for memory on a dual socket server. I don't object to more cores per processor chip, but I don't want to pay for die size for cores perpetually stalled for memory.

The object substrate worries about data distribution and who should see what. It doesn't even know it's a database. SQL semantics are applied by an engine layered on the object substrate. The SQL engine doesn't worry or even know that it's part of a distributed database -- it just executes SQL statements. The black magic is MVCC.

I'm a database developing building a database system for clouds. Tell me what you need. Here is my first approximation: A database that scales by adding more computers and degrades gracefully when machines are yanked out; A database system that never needs to be shut down; Hardware and software fault tolerance; Multi-site archiving for disaster survival; A facility to reach into the past to recover from human errors (drop table customers; oops;); Automatic load balancing

MySQL scales with read replication which requires a full database copy to start up. For any cloud relevant application, that's probably hundreds of gigabytes. That makes it a mighty poor candidate for on-demand virtual servers.

Do remember that the primary function of a database system is to maintain consistency. You don't want a dozen people each draining the last thousand buckets from a bank account or a debit to happen without the corresponding credit.

Whether the data moves to the work or the work moves to the data isn't that important as long as they both end up a the same place with as few intermediate round trips as possible.

In my area, for example, databases are either limited by the biggest, ugliest machine you can afford *or* you have to learn to operation without consistent, atomic transactions. A bad rock / hard place choice that send the cost of scalable application development through the ceiling. Once we solve that, applications that server 20,000,000 users will be simple and cheap to write. Who knows where that will go?

To paraphrase our new president, we must reject the false choice between data consistency and scalability.

Cloud computing is about using many computers to scale problems that were once limited by the capabilities of a single computer. That's what makes clouds exciting, at least to me. But most will argue that cloud computing is a better economic model for running many instances of a
single computer. Bah, I say, bah!

Cloud computing is a wonder new platform. Let's not let the dinosaurs waiting for extinction define it as a minor variation of what they've been doing for years. They will, of course, but this (and the dinosaurs) will pass.

The revolutionary idea is that applications don't run on a single computer but an elastic cloud of computers that grows and contracts by demand. This, in turn, requires an applications infrastructure that can a) run a single application across as many machines as necessary, and b) run many applications on the same machines without any of the cross talk and software maintenance problems of years past. No, the software infrastructure required to enable this is not mature and certainly not off the shelf, but many smart folks are working on it.

There's nothing limiting in relational except the companies that build them. A relational database can scale as well as BigTable and SimpleDB but still be transactional. And, unlike BigTable and SimpleDB, a relational database can model relationships and do exotic things like transferring money from one account to another without "breaking the bank.". It is true that existing relational database systems are largely constrained to single cpu or cluster with a shared file system, but we'll get over that.

Personally, I don't like masters any more than I like slaves. I strongly favor peer to peer architectures with no single point of failure. I also believe that database federation is a work-around
rather than a feature. If a database system had sufficient capacity, reliability, and availability, nobody would ever partition or shard data. (If one database instance is a headache, a million tiny ones is a horrible, horrible migraine.)

Logic does need to be pushed to the data, which is why relational database systems destroyed hierarchical (IMS), network (CODASYL), and OODBMS. But there is a constant need to push semantics higher to further reduce the number of round trips between application semantics and the database systems. As for I/O, a database system that can use the cloud as an L2 cache breaks free from dependencies on file systems. This means that bandwidth and cycles are the limiting factors, not I/O capacity.

What we should be talking about is trans-server application architecture, trans-server application platforms, both, or whether one will make the other unnecessary.

If you scale, you don't/can't worry about server reliability. Money spent on (alleged) server reliability is money wasted.

If you view the cloud as a new model for scalable applications, it is a radical change in computing platform. Most people see the cloud through the lens of EC2, which is just another way to run a server that you have to manage and control, then the cloud is little more than a rather
boring business model. When clouds evolve to point that applications and databases can utilize whatever resources then need to meet demand without the constraint of single machine limitations, we'll have something really neat.

On MVCC: Forget about the concept of master. Synchronizing slaves to a master is hopeless. Instead, think of a transaction as a temporal view of database state; different transactions
will have different views. Certain critical operations must be serialized, but that still doesn't require that all nodes have identical views of database state.

Low latency is definitely good, but I'm designing the system to support geographically separated sub-clouds. How well that works under heavy load is probably application specific. If the amount of volatile data common to the sub-clouds is relatively low, it should work just fine provided there is enough bandwidth to handle the replication messages.

MVCC tracks multiple versions to provide a transaction with a view of the database consistent with the instant it started while preventing a transaction from updating a piece of data that it could not see. MVCC is consistent, but it is not serializable. Opinions vary between academia and the real world, but most database practitioners recognize that the consistency provided by MVCC is sufficient for programmers of modest skills to product robust applications.

MVCC, heretofore, has been limited to single node databases. Applied to the cloud with suitable bookkeeping to control visibility of updates on individual nodes, MVCC is as close to black magic as you are likely to see in your lifetime, enabling concurrency and consistency with mostly non-blocking, asynchronous messaging. It does, however, dispense with the idea that a cloud has at any given point of time a single definitive state. Serializability implemented with record locking is an attempt to make distributed system march in lock-step so that the result is as if there there no parallelism between nodes. MVCC recognizes that parallelism is the key to scalability. Data that is a few microseconds old is not a problem as long as updates don't collide.

Jim certainly isn't shy with his opinions :-)

My summary of what he wants to do with NimbusDB is:

Make a scalable relational database in the cloud where you can use normal everyday SQL to perform summary functions, define referential integrity, and all that other good stuff.

Transactions scale using a distributed version of MVCC, which I do not believe has been done before. This is the key part of the plan and a lot depends on it working.

The database is stored primarily in RAM which makes cloud level scaling of an RDBMS possible.

The database will handle all the details of scaling in the cloud. To the developer it will look like just a very large highly available database.

I'm not sure if NimbusDB will support a compute grid and map-reduce type functionality. The low latency argument for data and code collocation is a good one, so I hope it integrates some sort of extension mechanism.

Why might NimbusDB be a good idea?

Keeps simple things simple. Web scale databases like BigTable and SimpleDB make simple things difficult. They are full of quotas, limits, and restrictions because by their very nature they are just a key-value layer on top of a distributed file system. The database knows as little about the data as possible. If you want to build a sequence number for a comment system, for example, it takes complicated sharding logic to remove write contention. Developers are used to SQL and are comfortable working within the transaction model, so the transition to cloud computing would be that much easier. Now, to be fair, who knows if NimbusDB will be able to scale under high load either, but we need to make simple things simple again.

Language independence. Notice the that IDMG products are all language specific. They support some combination of .Net/Java/C/C++. This is because they need low level object knowledge to transparently implement their magic. This isn't bad, but it does mean if you use Python, Erlang, Ruby, or any other unsupported language then you are out of luck. As many problems as SQL has, one of its great gifts is programmatic universal access.

Separates data from code. Data is forever, code changes all the time. That's one of the common reasons for preferring a database instead of an objectbase. This also dovetails with the language independence issue. Any application can access data from any language and any platform from now and into the future. That's a good quality to have.

The smart money has been that cloud level scaling requires abandoning relational databases and distributed transactions. That's why we've seen an epidemic of key-value databases and eventually consistent semantics. It will be fascinating to see if Jim's combination of Cloud + Memory + MVCC can prove the insiders wrong.

Are Cloud Based Memory Architectures The Next Big Thing?

We've gone through a couple of different approaches to deploying Memory Based Architectures. So are they the next big thing?

Adoption has been slow because it's new and different and that inertia takes a while to overcome. Historically tools haven't made it easy for early adopters to make the big switch, but that is changing with easier to deploy cloud based systems. And current architectures, with a lot of elbow grease, have generally been good enough.

But we are seeing a wide convergence on caching as way to make slow disks perform. Truly enormous amounts of effort are going into adding cache and then trying to keep the database and applications all in-sync with cache as bottom up and top down driven changes flow through the system.

After all that work it's a simple step to wonder why that extra layer is needed when the data could have just as well be kept in memory from the start. Now add the ease of cloud deployments and the ease of creating scalable, low latency applications that are still easy to program, manage, and deploy. Building multiple complicated layers of application code just to make the disk happy will make less and less sense over time.

We are on the edge of two potent technological changes: Clouds and Memory Based Architectures. This evolution will rip open a chasm where new players can enter and prosper. Google is the master of disk. You can't beat them at a game they perfected. Disk based databases like SimpleDB and BigTable are complicated beasts, typical last gasp products of any aging technology before a change. The next era is the age of Memory and Cloud which will allow for new players to succeed. The tipping point is soon.

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Disk-Based Parallel Computation, Rubik's Cube, and Checkpointing by Gene Cooperman, Northeastern Professor, High Performance Computing Lab - Disk is the the new RAM and RAM is the new cache

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The Power of B-trees

The Power of B-trees

CouchDB uses a data structure called a B-tree to index its documents and views. We’ll look at B-trees enough to understand the types of queries they support and how they are a good fit for CouchDB.

This is our first foray into CouchDB internals. To use CouchDB, you don’t need to know what’s going on under the hood, but if you understand how CouchDB performs its magic, you’ll be able to pull tricks of your own. Additionally, if you understand the consequences of the ways you are using CouchDB, you will end up with smarter systems.

If you weren’t looking closely, CouchDB would appear to be a B-tree manager with an HTTP interface.

CouchDB is actually using a B+ tree, which is a slight variation of the B-tree that trades a bit of (disk) space for speed. When we say B-tree, we mean CouchDB’s B+ tree.

A B-tree is an excellent data structure for storing huge amounts of data for fast retrieval. When there are millions and billions of items in a B-tree, that’s when they get fun. B-trees are usually a shallow but wide data structure. While other trees can grow very high, a typical B-tree has a single-digit height, even with millions of entries. This is particularly interesting for CouchDB, where the leaves of the tree are stored on a slow medium such as a hard drive. Accessing any part of the tree for reading or writing requires visiting only a few nodes, which translates to a few head seeks (which are what make a hard drive slow), and because the operating system is likely to cache the upper tree nodes anyway, only the seek to the final leaf node is needed.

From a practical point of view, B-trees, therefore, guarantee an access time of less than 10 ms even for extremely large datasets.

—Dr. Rudolf Bayer, inventor of the B-tree

CouchDB’s B-tree implementation is a bit different from the original. While it maintains all of the important properties, it adds Multi-Version Concurrency Control (MVCC) and an append-only design. B-trees are used to store the main database file as well as view indexes. One database is one B-tree, and one view index is one B-tree.

MVCC allows concurrent reads and writes without using a locking system. Writes are serialized, allowing only one write operation at any point in time for any single database. Write operations do not block reads, and there can be any number of read operations at any time. Each read operation is guaranteed a consistent view of the database. How this is accomplished is at the core of CouchDB’s storage model.

The short answer is that because CouchDB uses append-only files, the B-tree root node must be rewritten every time the file is updated. However, old portions of the file will never change, so every old B-tree root, should you happen to have a pointer to it, will also point to a consistent snapshot of the database.

Early in the book we explained how the MVCC system uses the document’s _revvalue to ensure that only one person can change a document version. The B-tree is used to look up the existing _rev value for comparison. By the time a write is accepted, the B-tree can expect it to be an authoritative version.

Since old versions of documents are not overwritten or deleted when new versions come in, requests that are reading a particular version do not care if new ones are written at the same time. With an often changing document, there could be readers reading three different versions at the same time. Each version was the latest one when a particular client started reading it, but new versions were being written. From the point when a new version is committed, new readers will read the new version while old readers keep reading the old version.

In a B-tree, data is kept only in leaf nodes. CouchDB B-trees append data only to the database file that keeps the B-tree on disk and grows only at the end. Add a new document? The file grows at the end. Delete a document? That gets recorded at the end of the file. The consequence is a robust database file. Computers fail for plenty of reasons, such as power loss or failing hardware. Since CouchDB does not overwrite any existing data, it cannot corrupt anything that has been written and committed to disk already. See Figure 1, “Flat B-tree and append-only”.

Committing is the process of updating the database file to reflect changes. This is done in the file footer, which is the last 4k of the database file. The footer is 2k in size and written twice in succession. First, CouchDB appends any changes to the file and then records the file’s new length in the first database footer. It then force-flushes all changes to disk. It then copies the first footer over to the second 2k of the file and force-flushes again.

Figure 1. Flat B-tree and append-only

If anywhere in this process a problem occurs—say, power is cut off and CouchDB is restarted later—the database file is in a consistent state and doesn’t need a checkup. CouchDB starts reading the database file backward. When it finds a footer pair, it makes some checks: if the first 2k are corrupt (a footer includes a checksum), CouchDB replaces it with the second footer and all is well. If the second footer is corrupt, CouchDB copies the first 2k over and all is well again. Only once both footers are flushed to disk successfully will CouchDB acknowledge that a write operation was successful. Data is never lost, and data on disk is never corrupted. This design is the reason for CouchDB having no offswitch. You just terminate it when you are done.

There’s a lot more to say about B-trees in general, and if and how SSDs change the runtime behavior. The Wikipedia article on B-trees is a good starting point for further investigations. Scholarpedia includes notes by Dr. Rudolf Bayer, inventor of the B-tree.

Understanding CPU caching and performance

Understanding CPU caching and performance:

Block sizes

In the section on spatial locality I mentioned that storing whole blocks is one way that caches take advantage of spatial locality of reference. Now that we know a little more about how caches are organized internally, we can look a bit closer at the issue of block size. You might think that as cache sizes increase you could take even better advantage of spatial locality by making block sizes even bigger. Surely fetching more bytes per block into the cache would decrease the odds that some part of the working set will be evicted because it resides in a different block. This is true, to some extent, but we have to be careful. If we increase the block size while keeping the cache size the same, then we decrease the number of blocks that the cache can hold. Fewer blocks in the cache means fewer sets, and fewer sets means that collisions and therefore misses are more likely. And of course, with fewer blocks in the cache the likelihood that any particular block that the CPU needs will be available in the cache decreases.

The upshot of all this is that smaller block sizes allow us to exercise more fine-grained control of the cache. We can trace out the boundaries of a working set with a higher resolution by using smaller cache blocks. If our cache blocks are too large, we wind up with a lot of wasted cache space because many of the blocks will contain only a few bytes from the working set while the rest is irrelevant junk. If we think of this issue in terms of cache pollution, we can say that large cache blocks are more prone to pollute the cache with non-reusable data than small cache blocks.

The following image shows the memory map we've been using, with large block sizes.

This next image shows the same map, but with the block sizes decreased. Notice how much more control the smaller blocks allow over cache pollution.

The other problems with large block sizes are bandwidth-related. Since the larger the block size the more data is fetched with each LOAD, large block sizes can really eat up memory bus bandwidth, especially if the miss rate is high. So a system has to have plenty of bandwidth if it's going to make good use of large cache blocks. Otherwise, the increase in bus traffic can increase the amount of time it takes to fetch a cache block from memory, thereby adding latency to the cache.

Write Policies: Write through vs. Write back

So far, this entire article has dealt with only one type of memory traffic: loads, or requests for data from memory. I've only talked about loads because they make up the vast majority of memory traffic. The remainder of memory traffic is made up of stores, which in simple uniprocessor systems are much easier to deal with. In this section, we'll cover how to handle stores in single-processor systems with just an L1 cache. When you throw in more caches and multiple processors, things get more complicated than I want to go into, here.??

Once a retrieved piece of data is modified by the CPU, it must be stored or written back out to main memory so that the rest of the system has access to the most up-to-date version of it. There are two ways to deal with such writes to memory. The first way is to immediately update all the copies of the modified data in each level of the hierarchy to reflect the latest changes. So a piece of modified data would be written to the L1 and main memory so that all of its copies are current. Such a policy for handling writes is called write through, since it writes the modified data through to all levels of the hierarchy.?

A write through policy can be nice for multiprocessor and I/O-intensive system designs, since multiple clients are reading from memory at once and all need the most current data available. However, the multiple updates per write required by this policy can greatly increase memory traffic. For each STORE, the system must update multiple copies of the modified data. If there's a large amount of data that has been modified, then that could eat up quite a bit of memory bandwidth that could be used for the more important LOAD traffic.?

The alternative to write through is write back, and it can potentially result in less memory traffic. With a write back policy, changes propagate down to the lower levels of the hierarchy as cache blocks are evicted from the higher levels. So an updated piece of data in an L1 cache block will not be updated in main memory until it's evicted from the L1.?

Conclusions

There is much, much more that can be said about caching, and this article has covered only the basic concepts. In the next article, we'll look in detail at the caching and memory systems of both the P4 and the G4e. This will provide an opportunity note only to fill in the preceding, general discussion with some real-world specifics, but also to introduce some more advanced caching concepts like data prefetching and cache coherency.????

Bibliography

• David A. Patterson and John L. Hennessy, Computer Architecture: A Quantitative Approach. Second Edition. Morgan Kaufmann Publishers, Inc.: San Francisco, 1996.?
• Dennis C. Lee, Patrick J. Crowley, Jean-Loup Baer, Thomas E. Anderson, and Brian N. Bershad, "Execution Characteristics of Desktop Applications on Windows NT." 1998.http://citeseer.nj.nec.com/lee98execution.html
• Institute for System-Level Integration, "Chapter 5: The Memory Hierarchy."?
• Manish J. Bhatt, "Locality of Reference." Proceedings of the 4th Pattern Languages of Programming Conference. 1997. http://st-www.cs.uiuc.edu/users/hanmer/PLoP-97/
• James R. Goodman, "Using Cache Memory to Reduce Processor-Memory Traffic." 25 Years ISCA: Retrospectives and Reprints 1998: 255-262
• Luiz Andre Barroso, Kourosh Gharachorloo, and Edouard Bugnion, "Memory System Characterization of Commercial Workloads." Proceedings of the 25th International Symposium on Computer Architecture. June 1998.

Revision History

Database Cracking

http://research.microsoft.com/apps/video/dl.aspx?id=148616
Adaptive Indexing targets dynamic environments where there is no workload knowledge and there is not enough time to invest in physical design preparations and tuning, e.g., due to very large data sets. With adaptive indexing, each query is seen as an advice of how data should be stored. With each incoming query, data is reorganized on-the-fly as part of the query operators. Future queries can exploit and enhance this knowledge. Autonomously, adaptively and without any external human administration, the system continuously adjusts to ever changing workload patterns, updates and storage restrictions. Adaptive indexing is designed on top of modern column-store architectures exploiting several new features such as one column at a time processing, vectorization, late tuple reconstruction and cache conscious algorithms.