Data Fabric Design Patterns: Transactional Data Service

This article is the second in a series on data fabric design and introduces the transactional data service design pattern.  The previous article in this series introduced data fabrics, which are collections of off-the-shelf DBMS servers that applications can connect to like a single server.  They are implemented from data fabric design patterns, which are reusable arrangements of DBMS servers, replication, and connectivity.  With this article we begin to look at individual design patterns in detail.

Description and Responsibilities

The transactional data service is a basic building block of data fabric architectures.  A transactional data service is a DBMS server that processes transactions submitted by applications and stores data safely.  Transactional data services have the following responsibilities:

• Store data transactionally and recover data faithfully to the last full transaction following failure.  
• Provide a network-accessible application interface for accessing data
• Provide a reliable and reasonably quick method for backup and restore. 
• Maintain an accessible, serialized log of transactions.  This enables replication between services.  

The following diagram illustrates the moving parts of a transactional data service.   In future diagrams we will just use the standard database symbol for the entire transactional data service, but for now we need to be able to see the contents.  

Motivation

Durable storage of transactions is the most fundamental responsibility of database systems.  It is difficult to build reliable applications if stored data can disappear or become corrupted because transactions were not committed before a crash.  Both problems can cause data loss.  Moreover, they can break replication links very badly if the DBMS server comes up in an inconsistent state, for example with some updates committed but others randomly rolled back.  This condition affects not only the one server but potentially many others throughout the fabric.

The transactional data service therefore focuses on storing data safely and recovering to the last committed transaction after a restart.  With this basic capability we can construct more complex services knowing that individual changes are unlikely to disappear or be recorded inconsistently.  

Detailed Behavior

Let's look in detail at the properties required for a successful transactional data service.  Somewhat surprisingly, an off-the-shelf SQL DBMS does not necessarily fit the pattern, though it comes close.  It is important to understand the differences. 

The transactional store keeps data from getting lost and is the basis for recovery throughout the fabric.  Transactional stores support commit and rollback with multi-statement transactions.  In theory the transaction data service responsibility for data persistence matches MySQL/InnoDB and PostgreSQL behavior, both of which commit transactions safely in serial order.  However, the reality is not quite that simple.

Most DBMS allow applications to ignore transactions under certain conditions.  This results in  wormholes, which are violations in serial ordering of data.  There are a number of table definition options in SQL that undo transactional consistency.

• (MySQL) MyISAM table type.  MyISAM tables ignore transactions and commit immediately, even if the application later tries to roll back.  The tables may also become corrupted by server crashes.  
• (MySQL) Memory table type.  These tables are maintained in memory only.  They disappear on restart.  
• (PostgreSQL) UNLOGGED tables.  Such tables are not logged and disappear on crash or unclean shutdown (thanks Frederico).  

All of these allow data to disappear or become corrupted after a crash. However, there is a more subtle problem.  If applications depend on these tables, transaction results may then depend on when the server last crashed or restarted, which in turn makes updates across replicas non-deterministic.  Random updates create problems for data replication, which depends on replicas behaving identically when transactions are applied.  It is important to avoid application dependencies on any feature that creates wormholes or you might not be able to use other design patterns.

So is data loss always bad?  Surprisingly, no.  In some cases transactional stores can lose data provided that they do so by dropping the last transactions in serial order.   It's as if the data just reverted to an earlier point in time.  To understand why this might be OK, imagine three servers linked into a chain by asynchronous replication.

Data loss is sometimes not a big deal

It is bad to lose data on the first server, especially if those data are lost before replicating transactions to the downstream replicas.  However, data loss on the last server is fine.  Assuming that server stores the replication restart point transactionally, it will just re-apply the missing transactions and catch up.  This is exactly what happens when you restore a backup in slave in master/slave replication.

Data loss on the second server also may not be a problem.  It should restart replication and should generate identical transactions for itself as well as for replication to the last server.  In both cases we assume that replication will handle these cases correctly and can replay missing transactions from logs.  If so, you can not only tolerate such losses but even depend on recovering from them automatically.

Turning to the next responsibility of the transactional data service, the application interface may obviously include SQL via MySQL or PostgreSQL wire protocols.  However, any consistent interface that is accessible over a network will do.  The memcached protocol is also perfectly acceptable.  Subsets of SQL such as stored procedures work quite well.  Transactional data services are more general than SQL DBMS servers in this sense.  Full SQL or even a subset is not a requirement.  

Backup and restore are critical for data fabrics as they enable provisioning of new services as well as recovery of services that fail.  You restore a backup and then let the service catch up using replication.  Data fabrics can get along fine using a range of options from logical dumps of the DBMS (mysqldump or pgdump) to file system snapshots.  Fabric backups just need to be transactionally consistent and movable to other hosts.

Note that the backup required by the transaction data service design pattern is a less general form of backup than most businesses really require.  Businesses may need to recover data after accidental deletion or to keep copies of information for many years for legal reasons.  You can therefore use a general-purpose backup solution like Zmanda or Barman provided it meets the fabric design pattern requirements.  There's no need to do things twice.  

Finally, the replication log is a serialized list of transactions to replicate to other hosts.  Serialization enables the transactions to be replayed on another host and result in an identical copy.  Generally speaking, data fabrics require logical replication, which applies changes to replicas using SQL statements on a live server.  This is because other design patterns depend on being able to access and even write to the transactional data service when it is acting as a slave.   Binary replication methods like disk block replication, such as DRBD, do not meet this requirement and therefore are of limited use in data fabrics.  

Implementation

You can implement the transactional data service design pattern with any DBMS that meets the pattern responsibilities.  That said, implementation details are very important.  As we have seen, ensuring that DBMS servers live up to the responsibility to store transactions safely is a little harder than one might think.  

1. MySQL.  MySQL with InnoDB engine is generally a good choice.  It has stable SQL APIs and a wide range of capable client libraries.  However, MySQL must be correctly configured to maintain proper transactional guarantees.  Here are three properties that should be in your my.cnf file to help ensure MySQL lives up to its responsibilities: 

# Ensure durable flush to storage on transaction commit.   

innodb_flush_log_at_trx_commit=1

# Synchronize binlog with committed transactions. 

sync_binlog=1

# Use InnoDB as default storage engine.  (Unnecessary for MySQL 5.5 and above.) 

default-table-type=InnoDB

There are a variety of good backup mechanisms for MySQL databases, including mysqldump (with --single-transaction, useful only for small data sets), Percona XtraBackup, and file system snapshots. Snapshots are especially good when using NetApp or other capable storage.  NetApp snapshots can be restored in seconds and cost little in terms of performance overhead.  

2. PostgreSQL.  PostgreSQL with a trigger-based replication log, for example from Londiste or SLONY, and pgdump for backups is another good choice.  PostgreSQL has unusually good trigger support for DML changes at least, and permits users to encode them in a number of languages.  Be aware the PostgreSQL triggers do not capture DDL statements like CREATE TABLE, though.

PostgreSQL is fully transactional out of the box and triggers create a fully serialized replication log.  It does not have the problem that MySQL does with potentially unsafe table types like MyISAM.  However, you need to set a couple of parameters to ensure safe operation.  These ensure transactions are committed down to the storage level and prevent catastrophic corruption of the database and/or the WAL (write-ahead log).  Since PostgreSQL defaults to these values, you mostly need to avoid turning them off.

fsync = on                             # turns forced synchronization on or off
synchronous_commit = on                # immediate fsync at commit

Like MySQL, PostgreSQL SQL and APIs are stable and well-known.  Pgdump also loads and restores data without difficulty for smallish data sets.  For larger data sets file system snapshots work very well.  

Regardless of the DBMS type you choose, it is important to avoid application-level features that introduce wormholes, such as the PostgreSQL unlogged tables mentioned in the previous section.  Generally speaking, you should only skip transactions if there is a very strong reason for doing so.

Do other database types work for this design pattern?  Of course.  You can also use a commercial DBMS like Oracle.  Oracle fulfills the pattern responsibilities quite well, but is a bit more heavyweight than users want, particular when operating in the cloud.  

And Hardware Implementation, Too...

Even with a properly configured DBMS server you are still not completely out of the woods for data durability.  Database servers generally use an fsync() or similar system call to flush data to storage.  Unfortunately storage controller cards may just cache the data to be written in local RAM and return.  That can fool the DBMS server into thinking transactions are safely stored when they actually are still sitting in memory on a controller card.  The "committed" data will then vaporize in a host crash, which in turn can fatally corrupt both MySQL and PostgreSQL stores if you are very unlucky.  Just a few bad blocks can cause very serious problems.

Fortunately there is a cure to make data vaporization less likely.  On local storage you can invest in RAID with a battery-backed cache (BBU), which keeps power on for the cache even if the host fails completely.  SANs and network attached storage tend to have this capability built in.  (But check the specifications!)  Battery backed cache also tends to be fast, since controllers can safely return from an fsync() operation as soon as the data to be written are in the on-board cache.  Without this feature writes to storage can be painfully slow.

One interesting question is how to handle cloud environments like Amazon.  You just do not know how the storage actually works.  (That's really the point of a cloud, after all.)  Amazon provides SLAs for performance (example:  EBS provisioned IOPS), but there do not seem to be any SLAs about storage consistency.  There is a lot to learn here and lessons that apply to Amazon may not necessarily apply to others.  I suspect this will prompt some rethinking about data consistency--it's an interesting "what if" for transaction processing to suppose you cannot trust the underlying storage capabilities.

Data loss occurs sooner or later in virtually all systems, but the good new is that you can make it uncommon.  For more information check out data sources like this and this.  Also, other fabric design patterns like the Fault-Tolerant Data Service keep applications running when failures do occur and can even minimize the effects of data loss.  See the upcoming article on that design pattern for more information.

Implementations to Avoid

Here are two examples that do not meet the transactional data service design pattern responsibilities or at least not fully.  

1. MySQL with MyISAM table type.  MyISAM does not support transactions and is not crash safe.   You will lose data or incur downtime fixing problems.  MyISAM does not belong in data fabrics.

2. PostgreSQL with streaming replication.  Streaming replication replicates log updates in real-time and has the added benefit of permitting queries on replicas.  However, streaming replication does not allow you to write to replicates.  It therefore does not support online schema maintenance or multi-master replication.  It also does not help with heterogeneous replication.  Streaming replication is therefore an unattractive choice, even though it is far simpler and works better for ensuring high availability than logical replication solutions like SLONY.  

How do NoSQL stores fare in this design pattern?  Let's pick on MongoDB.  MongoDB supports atomic commit to single BSON documents but does not support transactions across multiple documents.  (Unless you think that the hacky two-phase commit proposed by the MongoDB manual is an answer.)  Atomic transactions are not one of the reasons people tend to choose NoSQL systems, so this is not surprising. It means that MongoDB cannot handle the responsibilities of this design pattern.  

Conclusion and Takeaways

The transactional data service design pattern can be implemented with a carefully configured SQL database.  As we have seen, however, there are a number of details about what "carefully configured" really means.

It is a good idea to use the transactional data service design pattern even if you are not planning to implement a data fabric.  Systems grow.  This pattern gives you the flexibility to build out later by adding other fabric design patterns, for example to introduce cross-site operation using the Multi-Site Data Server pattern.  It also protects your data at multiple levels that include transactions as well as regular backups.  Nobody really likes losing data if it can be avoided.

Another important point: the transactional data service design pattern keeps your entire fabric working.  Losing small amounts of data is typically just an inconvenience for users, especially if it does not occur too often.  Broken replication on the other hand due to replicas that diverge or become corrupt after failures can lead to time-consuming administration and significant downtime to repair.  The fabric is a network of servers.  Poor configuration on one server can cause problems for multiple others.

Finally, the biggest error people make with this design pattern is to neglect backups.  There's something inherently human about it:  backups are painful to test so most of us don't.  My first and biggest IT mistake involved a problem with backups.  It nearly caused a large medical records company to lose a week of data entry records.  Put in backups and test them regularly to ensure they work.  This is a theme that will recur in later articles.

Speaking of which, the next article in this series will cover the fabric connector design pattern.  Stay tuned! 

Introducing Data Fabric Design for Commodity SQL Databases

Data management is undergoing a revolution.  Many businesses now depend on data sets that vastly exceed the capacity of DBMS servers.  Applications operate 24x7 in complex cloud environments using small and relatively unreliable VMs.  Managers need to act on new information from those systems in real-time. Users want constant and speedy access to their data in locations across the planet.

It is tempting to think popular SQL databases like MySQL and PostgreSQL have no place in this new world.  They manage small quantities of data, lack scalability features like parallel query, and have weak availability models.  One reaction is to discard them and adopt alternatives like Cassandra or MongoDB.  Yet open source SQL databases have tremendous strengths:  simplicity, robust transaction support, lightning fast operation, flexible APIs, and broad communities of users familiar with their operation.  The question is how to design SQL systems that can meet the new requirements for data management.

This article introduces an answer to that question:  data fabric design.  Data fabrics arrange off-the-shelf DBMS servers so that applications can connect to them as if they were a single database server.  Under the covers a data fabric consists of a network of servers linked by specialized connectivity and data replication.  Connectivity routes queries transparently from applications to DBMS servers.  Replication creates replicas to ensure fault tolerance, distribute data across locations, and move data into and out of other DBMS types.  The resulting lattice of servers can handle very large data sets and meet many other requirements as well.

Data fabric design is a big topic, so I am going to spread the discussion over several articles.  This first article provides a definition of data fabric architecture and introduces a set of design patterns to create successful data fabrics.  In the follow-on articles I will explore each design pattern in detail.  The goal is to make it possible for anyone with a background in database and application construction to design data management systems that operate not only today but far into the future.  At the very least you should understand the issues behind building these systems.

Some readers may see data fabric design as just another reaction to NoSQL.  This would be a mistake.  Building large systems out of small, reliable parts is a robust engineering approach that derives from ground-breaking work by Jim Grey, Pat Helland, and others dating back to the 1970s.  Data fabrics consist of DBMS servers that you can look at and touch, whereas NoSQL systems tend to build storage, replication, and access into a single distributed system.  It is an open question which approach is more complex or difficult to use.  There are trade-offs and many systems actually require both of them.  You can read this article and those that follow it, then make up your own mind about the proper balance.

Acknowledgements

The data fabric concept is largely based on practical experience on Continuent Tungsten.  I am indebted to Continuent as well as our customers for the opportunity to work on this problem.  I am likewise indebted to Ed Archibald, Continuent CTO, with whom I have worked for many years.  Ed among other things came up with the data fabric moniker.  Our interest in this topic goes back to shared experiences with Sybase OpenServer and Sybase Replication Server, which were ground-breaking products in the fields of connectivity and replication.  Two decades later we are still applying the insights gained from working on them.

What Is a Data Fabric Architecture?

Let's start the discussion of data fabrics with a practical problem.  We want to design a SaaS application for customer relationship management that will support up to a million users using a commodity open source DBMS like MySQL.  What are the requirements?

Obviously, our system must be able to scale over time to hundreds of app servers operating on hundreds of terabytes data.  It must hide failures, maintenance, and schema upgrades on individual DBMS hosts.  It must permit data to distribute across geographic regions.  It must deliver and accept transactions from NoSQL, data warehouses, and commercial DBMS in real time.  It must allow smooth technology upgrade and replacement.  Finally, it must look as much like a single DBMS server to applications as possible.  Here's a picture of what we want: 

Conceptual Data Fabric

The last requirement is especially important and goes to the essential nature of data fabric architecture.  The fabric may contain dozens or even hundreds of servers but encapsulates their locations and number.  Applications connect to the fabric the same way they connect to individual DBMS servers, a property we call transparency.  Transparency permits developers to build applications using a DBMS on a laptop and push out code to production through increasingly capable test environments without changes in behavior.   This is a potentially confusing requirement so let's look at a couple of examples.

Transparency does not mean that you get access to all data all the time from everywhere.  Say our sample application stores customer data across many servers.  The following SQL query to list regions with SaaS users who have sales to their own customers greater than $100,000 would typically not work:

mysql> select region, count(cust_id), sum(sales) as sales_total 
  from customer group by region having sales_total > 100000;

This is not as big a problem as it sounds, as SaaS applications for the most part operate within a single SaaS user at a time and do not ask for data across users.  Moreover, it is easy to understand that you need to do something special for this particular query, such as connect to all servers explicitly or load transactions into a data warehouse. Most SaaS designers are pretty comfortable with this limitation, which just makes explicit something that you know anyway.

On the other hand transparency does mean that your application talks to what looks like a single server for individual SaaS user data.  For instance the following sequence of commands on a single customer to insert a row and get the generated auto-increment key back must work in a fabric just as it does in a single DBMS server.

mysql> insert into customer(name, region, sales) values('bob', 'sfo', 10035.0);
Query OK, 1 row affected (0.00 sec)

mysql> select last_insert_id();
+------------------+
| last_insert_id() |
+------------------+
|                1 |
+------------------+
1 row in set (0.00 sec)

Selecting the last inserted ID is a standard idiom for adding rows to table with synthetic keys in MySQL.  It is baked into widely used libraries like PHP mysqli.  Change it and you break thousands of applications.  This could happen if the fabric switched a DBMS connection across servers between these two commands.  When operating on data for a single SaaS user fabric transparency needs to be as close to perfect as possible.

Any architecture that meets the preceding requirements including transparency is a data fabric.  The next topic is what is actually inside a fabric architecture.

What Are Data Fabrics Made of?  

Combining individual components into lattices is hardly a new idea.  Such compositions are common in fields from bridge-building to art.  One of my favorite examples is the famous arabesques of the Alhambra in Granada, which combine simple motifs into patterns then combine those to create still more complex patterns that cover walls and ceilings throughout the palace.  The resulting compositions are works of stunning beauty.  

Detail of Arabesque from Alhambra, Spain (Source: Wikipedia)

Arabesque construction is far from random.  Arabesques combine plant-like elements into geometric patterns.  Only certain elements are allowed--there are typically no human representations--and only certain patterns work well.  Arabesques are also expressed in a particular medium such as stone, plaster, tiles, or paint.

Much as arabesques do, data fabrics combine very specific elements in a medium consisting of the following logical parts: 

1. A partitioned network of recoverable transactional stores (DBMS servers) connected by reliable messaging (replication).  Partitioned means that not every service exchanges information with every other, in the same way that DBMS servers for different applications may be separate silos.  
2. A routing layer that connects applications to relevant data based on simple hints provided by the application itself, such as the name of a schema, a primary key, or whether the current user transaction is a set of writes or an auto-commit read.  

Stores, replication, and routing logic are the fundamental elements of fabric implementations.  These are powerful tools for building very large applications.  Let's take a quick tour, as their particular properties are critical for building systems that actually work.

Transactional stores are DBMS servers that apply changes as atomic units that either commit or roll back as a whole.  Transactional stores convert these changes into a serial history, which orders concurrent transactions in its log so that they can replay as if they had executed one after the other all by themselves.  Serial ordering enables recovery, which is the procedure to bring data back cleanly to the last committed transaction after a crash or restart.  These properties are at the heart of all relational DBMS systems today and are necessary for the fabric to operate.  Non-transactional stores are close to useless in fabrics.  Use them at your peril.

Reliable messaging transmits changes between stores without losing them or applying them twice.  The usual way to do this is through replication, which moves transactions automatically on commit and applies them to replicas.   Replication needs to support transactions, apply them in serial order, and recover just as transactional stores do.  Replication systems that fail to meet any of these three requirements do not cut the mustard and do not belong in data fabrics.

Data replication implementations differ in at least three major ways.  There is not really a "right approach" to replication--different methods work better for some applications than others.

1. Synchronous vs. asynchronous.  Synchronous replication moves transactions before reporting commit back to applications.  It minimizes problems with data loss if a replica fails but may slow or block applications if replication is slow.  It can also lead to deadlocks.  Asynchronous replication moves transactions after commit.  It does not block applications but leads to latency between replicas and may result in data loss if there is a failure before transactions replicate from a particular replica. 

2. Master-master vs. master/slave.  Master-master replication allows updates on any replica.  It requires conflict management, typically either through locking in advance or fixing up after the fact (also known as conflict resolution).  Master/slave replication requires applications to use a single master for updates.  It works well with asynchronous replication, which is easier to implement.  

3. Log-based vs. trigger-based.  Log-based replication reads the database journal or some representation of it like the MySQL binlog.  Log-based replication has lower impact on DBMS servers but can be very hard to implement correctly.  Trigger-based replication uses triggers to capture transaction content, typically using transfer tables.  Trigger-based replication is simpler to implement but adds load to DBMS servers, can add unwanted serialization points to transactions, and may not handle certain types of changes, such as DDL data.  

The routing layer directs application DBMS connections to specific copies of data.  An application sees what appears to be a single session for each connection with consistent settings, such as character set encodings and session variables.  Underneath the fabric may actually switch the connection across DBMS servers at opportune times.  Transparency is absolutely critical.  As mentioned previously, even tiny changes in session behavior breaks libraries that applications depend on to access data.  

There are many methods to route connections as well as transactions.  As with replication, there is no "best" way.  It all depends on your application and the type of environment in which you are operating.  Here are four common approaches. 

1. Gateways.  A gateway is a proxy process that sits between the application and DBMS server.  The gateway looks like a server to applications.  It establishes connections on behalf of the applications, hence can perform very flexible transformations on data at the cost of some performance due to the double network hop the gateway introduces.  Gateways are hard to implement initially, but good ones are relatively easy to program once they work properly.  

2. IP Routers.  Routing software switches IP packet paths between servers.  It has the lowest overheard and potentially highest transparency but require substantial effort to implement correctly.  

3. Library wrappers.  Library wrappers re-implement standard interfaces like JDBC or Perl DBI, then route application requests through underlying connectors like the MySQL or PostgreSQL JDBC driver.    Wrappers are relatively easy to implement and have excellent performance but do not handle traffic that goes outside the library.  Compiled versions can have nasty library dependencies that introduce new forms of RPM hell for users if you try to use them generally.

4. Custom routing layers.  Any interface that your applications use to access data can implement routing logic.  For instance, SOAP or JSON servers work perfectly well for this purpose.   Internal data access libraries can also implement routing.  This approach is specific to single applications, and like library wrappers does not cover other means of access.  

The biggest constraint for any routing method is to keep it simple.  Fat routing layers with complex logic tend to introduce bugs as well as change database semantics.  This in turn violates the requirement for transparency.

Fabrics use the preceding elements over and over.  As we build up data fabric designs it is helpful to use consistent notation for data stores, replication, and routing.  The following summarizes the main notation that you will see in diagrams.

Data Fabric Notation

At this point we understand that data fabrics consist of a specific set of elements.  However, we have not provided any organization yet.  The next question is therefore how to arrange them into real systems.

What Are the Design Patterns for Data Fabrics?

Having a model of fabric elements is not the same as an actual implementation.  We need to define how the elements are composed into a complete system that can be deployed.  This is where design patterns come in.

Design patterns are reusable solutions that combine flexibly with each other to create large-scale architectures.  In the case of data fabrics, design patterns offer guidelines to organize fabric elements in ways that work well for large data sets spread over multiple geographic locations.  They serve the same function as the geometric arrangements that organize individual motifs in an arabesque.

Data fabric design patterns arrange data services.  A data service is an abstraction for a transactional store with a well-defined API.  Design patterns either link data services in some way or create more capable data services from simpler ones.  The ability to create new services in this way is called service composability.  Composability is a fundamental attribute of fabric design patterns.  It is the reason that fabric designs can handle very large data sets flexibly.  Service composition is analogous to the patterns-within-patterns layout that allows arabesques to cover an entire building without seeming repetitive or boring.

There are six design patterns that are particularly useful for SQL databases.  The following diagram shows them using italized blue font and illustrates how they organize the implementation of our sample CRM system.

Fully Implemented Data Fabric

Here is a short description of each fabric design pattern.  

• Transactional Data Service - A transactional store that can restart without losing data following failure.  It has a well-defined API, a mechanism for backup, and a transaction log for replication.   This is the building block for all other design patterns.  
• Fabric Connector -- A routing method that hides the location of databases and enables data services consisting of multiple DBMS servers to look like a single server to applications.  
• Fault-Tolerant Data Service -- A data service that protects against failures and enables zero-downtime maintenance using a set of redundant DBMS servers.  
• Sharded Data Service -- A data service that handles large data sets by dividing them into shards distributed across a number of underlying data services
• Multi-Site Data Service -- A data service that enables data to spread across multiple geographic locations using data services on each site linked by replication
• Real-Time Data Bridge -- A link that enables real-time replication of data between heterogeneous data services, for example between MySQL and a data warehouse like Vertica.  

You can implement fabric design patterns in many different ways.  This means there are many possible implementations of data fabrics as well.  As long as you meet the assumptions of each particular design pattern, the result is likely to work well.  We will delve into some of the variations in the next few articles.

A Little Bit about Naming

You may have encountered the term "data fabric" in products (example here or here) or as a concept (a nice example here).  This is because the ideas behind fabrics are very general and apply to many types of data management systems, perhaps most notably storage area networks.  The name data fabric is very natural and seems to have occurred to many people independently, especially after storage vendors popularized it during the 1990s.

In our case the term "data fabric" always means a data fabric architecture as I defined it a couple of sections back.

Conclusion and More

This has been a longish introduction but introduces terminology and sets the stage for looking at specific design patterns used to build data fabrics.  Follow-on articles will look at individual design patterns in detail.

One final thought:  there are no doubt other ways of factoring design patterns for data fabrics.  This particular set of patterns works very well for SQL databases, and I have seen them used successfully in many large systems over the years.  I would argue they work well for other DBMS types as well but welcome your comments on other approaches.

p.s., The next article covering the transactional data service is now available.  

Replicating from MySQL to Amazon RDS

There have been a number of comments that Amazon RDS does not allow users access to MySQL replication capabilities (for example here and here).  This is a pity. Replication is one of the great strengths of MySQL and the lack of it is a show-stopper for many users.  As of the latest build of Tungsten Replicator half of this problem is on the way to being solved. You can now set up real-time replication from an external MySQL master into an Amazon RDS instance.

In the remainder of this article I will explain how to set up Tungsten replication to an Amazon RDS slave, then add a few thoughts about why this feature is useful along with some suggestions for improvement.   To keep the article reasonably short I assume you understand the basics of installing Tungsten Replicator.  If you need more information, check out the online documentation.

Readying an RDS Test Instance

Amazon RDS is Amazon's on-demand relational database.  RDS supports several database types including MySQL, which I will use for this demonstration.  Launching a new instance is simple.  Login to the Amazon AWS Console using an account that has RDS enabled, then switch to the Amazon RDS Console.  Press the Launch a DB Instance button, whereupon a screen like the following appears:

RDS Database Selection

Press the Select button for MySQL Community Edition, which starts the configuration window.  (You can also replicate into Oracle if you are up for a challenge.  If you do this, please post what you did to get it to work!)  Next fill out properties for MySQL.

MySQL Instance Configuration

Among other things, you create a master login and password.  Note these carefully as you will need them to configure replication.  Then continue for another couple of screens, at which point you can launch your instance.  It takes about 10 minutes for new instances to spin up, after which you can see the instance properties in the AWS RDS Console.  Here's a screen shot of my test instance.

AWS RDS Console

Once the instance is up, test access.  This is of course necessary to prove the instance is running properly and that we can login from a remote location.  Note the host name in the Endpoint field.  This is a DNS entry for the new MySQL instance.  Using that and the master login, we can now fire up the mysql client from a remote host where we plan to run replication.

$ mysql -utungsten -p -htest.c4villnbpuq1.us-west-1.rds.amazonaws.com
Enter password: 
Welcome to the MySQL monitor.  Commands end with ; or \g.
Your MySQL connection id is 2125
Server version: 5.5.27-log Source distribution

Type 'help;' or '\h' for help. Type '\c' to clear the current input statement.

mysql> show databases;
+--------------------+
| Database           |
+--------------------+
| information_schema |
| innodb             |
| mysql              |
| performance_schema |
| test               |
+--------------------+
5 rows in set (0.01 sec)

This looks quite good.  We are now ready to install and start replication.

Important note!  If you have trouble connecting you may need to tweak your Amazon security group settings to open up ports, especially if you are replicating from non-Amazon locations.  Amazon has a very cool feature that can guess your originating host IP and offer a CIDR address that covers the port range from which you are operating.  I used this when configuring my security groups.

Setting Up Tungsten Replication

It is possible to set up replication from a MySQL master directly to Amazon RDS using a single Tungsten Replicator process.  However, it is more versatile and simpler to set up two replicators:  one to read from the MySQL master, and another replicator to apply to the Amazon RDS slave.  I will therefore demonstrate this configuration.

We will assume you have a MySQL master already running and that it meets prerequisites for running Tungsten.  Let's now grab the Tungsten code and install a master replicator.  You can get fresh builds from the Tungsten Replicator builds page.

We will take a recent replicator build that contains the RDS changes, which are documented in Issue 425.  Use 2.0.7 build 177 or later. The main improvement is to add a non-privileged slave mode that avoids invoking any of the operations forbidden by Amazon.  Among other things Tungsten normally uses commands like 'SET SESSION SQL_LOG_BIN=0' to suppress writing to the binlog when it applies data on a slave.  This command requires SUPER privilege, hence causes problems for underprivileged RDS logins.

Unpack the code and install the master replicator in /opt/continuent.  This is no different from installing a normal Tungsten master.  My master host is logos1.  Here are sample commands to pull the code and set up the master.  The example shows the minimum options--if you have MySQL installed in a non-standard location or otherwise differ from a stock installation you may need to add additional options.

mkdir ~/staging
cd staging
wget --no-check-certificate https://s3.amazonaws.com/files.continuent.com/builds/nightly/tungsten-2.0-snapshots/tungsten-replicator-2.0.7-177.tar.gz
tar -xf tungsten-replicator-2.0.7-177.tar.gz
tungsten-replicator-2.0.7-177/tools/tungsten-installer \
  --master-slave  \
  --master-host=logos1 \
  --datasource-user=tungsten  \
  --datasource-password=your_passord  \
  --service-name=aws \
  --home-directory=/opt/continuent \
  --cluster-hosts=logos1 \
  --start-and-report

Next, set up the slave replicator.  For convenience I am going to install the slave on a separate host, named logos2, to avoid port clashes between the two replicators.  If you install on the same host, you'll need to install into another release directory location and use the --rmi-port and --thl-port options to avoid port overlaps.  Here is the command to set up the Amazon RDS slave.  Note that the tungsten-installer program can install code between hosts, which is an extremely useful feature.

tungsten-replicator-2.0.7-177/tools/tungsten-installer \
  --master-slave  \
  --cluster-hosts=logos2 \
  --master-host=logos1 \
  --datasource-host=test.c4villnbpuq1.us-west-1.rds.amazonaws.com \
  --datasource-user=tungsten  \
  --datasource-password=your_password  \
  --service-name=aws \
  --slave-privileged-updates=false \
  --home-directory=/opt/continuent \
  --skip-validation-check=InstallerMasterSlaveCheck \
  --skip-validation-check=MySQLPermissionsCheck \
  --start-and-report

You may see a few warnings during the RDS installation, as the tungsten-installer cannot verify some settings on the Amazon RDS host.  These can be ignored.  If everything goes well, you now have two replicators up and running.  You can check the status of the master and slave using the trepctl command, as in:

/opt/continuent/tungsten/tungsten-replicator/bin/trepctl -host logos1 status

/opt/continuent/tungsten/tungsten-replicator/bin/trepctl -host logos2 status

Both replicators should report that they are online.  Now complete the exercise by proving that replication works between the replicators.  We start by logging into the local MySQL instance, creating a new table in the test schema, and adding data.

$ mysql -uroot test
Welcome to the MySQL monitor.  Commands end with ; or \g.
Your MySQL connection id is 231488
Server version: 5.5.21-rel25.1-log Percona Server with XtraDB (GPL), Release rel25.1, Revision 234

Type 'help;' or '\h' for help. Type '\c' to clear the current input statement.

mysql> create table foo(id int primary key);
Query OK, 0 rows affected (0.24 sec)

mysql> insert into foo values (256);
Query OK, 1 row affected (0.00 sec)

Now login to the Amazon RDS instance and look for table foo.  

$ mysql -utungsten -p -htest.c4villnbpuq1.us-west-1.rds.amazonaws.com test

Enter password: 

Reading table information for completion of table and column names

You can turn off this feature to get a quicker startup with -A

Welcome to the MySQL monitor.  Commands end with ; or \g.

Your MySQL connection id is 2161

Server version: 5.5.27-log Source distribution

Type 'help;' or '\h' for help. Type '\c' to clear the current input statement.

mysql> select * from foo;

+-----+

| id  |

+-----+

| 256 |

+-----+

1 row in set (0.01 sec)

Mission accomplished!  We have real-time replication enabled from a MySQL master to Amazon RDS.  At this point you can replicate more or less normally.  There are some obvious limitations due to the fact that Amazon RDS is locked down and does not grant our login full privileges.  

1. Temp table replication may not work.  Tungsten depends on being able to issue commands of the form "set @@session.pseudo_thread_id=23531" and the like.  This prevents clashes between temp tables of the same name on different master sessions.  You may need to enable row replication on the master, which suppresses temp table replication.  (For other approaches, see my previous article on temp tables and the binlog.) 
2. Any command that requires SUPER privilege will not work.  As an obvious example, you will not be able to grant SUPER privilege to new accounts.  Such commands will break replication. 
3. All replicated commands go into the binlog, which is a potential performance drag and may slow down Amazon RDS slaves.  Parallel replication may not help in this case, since committing to the binlog is a serialization point that blocks other transactions.  This problem may be cured if Amazon picks up group commit fixes from MySQL 5.6 and/or MariaDB.  

All things considered, however, these are minor inconveniences.  Most applications should be able to replicate without difficulties, especially if the master transaction rate is not too high.

Configuring SSL for Connections to RDS

In the previous demonstration I used a master host running outside Amazon.  This means my test transactions traveled across the Internet, where they were visible to all and sundry along the way.  To illustrate, we can run tcpdump and watch traffic as it goes by.

$ sudo tcpdump -A -vvv -s 256 host test.c4villnbpuq1.us-west-1.rds.amazonaws.com
...
logos2.46657 > ec2-54-241-56-140.us-west-1.compute.amazonaws.com.mysql: Flags [P.], cksum 0xb0f7 (incorrect -> 0x995d), seq 3012:3073, ack 3412, win 94, options [nop,nop,TS val 89630514 ecr 74223746], length 61
E..qw>@.@......n6.8..A..4..L...f...^.......
.W.2.l..9....insert into foo values (256) /* ___SERVICE___ = [aws] */
22:55:36.316049 IP (tos 0x8, ttl 51, id 26226, offset 0, flags [DF], proto TCP (6), length 63)

If we were handling confidential data, exposing traffic like this to possible evildoers would be a serious problem.  Fortunately, Amazon RDS supports SSL encrypted connections from clients.  Here is how to use it with Tungsten.

First, you need to get the Amazon RDS certificate, which is used to sign certificates for individual RDS instances.

mkdir /opt/continuent/certs
cd /opt/continuent/certs
wget https://rds.amazonaws.com/doc/mysql-ssl-ca-cert.pem

Next, you need to create a trust store that Java can access containing the certificates of signing authorities whom you trust.  For this you will need the Java keytool utility, which is included in the JDK.  If you are just using the Java runtime in production, you will need to generate the store on another host, then copy it over to your test hosts.  I used the password "secret" in this example.

$ keytool -import -alias rds -file mysql-ssl-ca-cert.pem -keystore truststore
Enter keystore password:  
Re-enter new password: 
Owner: CN=aws.amazon.com/rds/, OU=RDS, O=Amazon.com, L=Seattle, ST=Washington, C=US
Issuer: CN=aws.amazon.com/rds/, OU=RDS, O=Amazon.com, L=Seattle, ST=Washington, C=US
...
Trust this certificate? [no]:  yes
Certificate was added to keystore

We now need to tell the slave replicator about the truststore file using Java VM options.  On the slave host, edit /opt/continuent/tungsten/tungsten-replicator/conf/wrapper.conf and add the extra options shown in bold face.  

# Java Additional Parameters

wrapper.java.additional.1=-Dreplicator.home.dir=../../tungsten-replicator/

wrapper.java.additional.2=-Dreplicator.log.dir=../../tungsten-replicator/log

wrapper.java.additional.3=-Dcom.sun.management.jmxremote

wrapper.java.additional.4=-Djavax.net.ssl.trustStore=/opt/continuent/certs/truststore

wrapper.java.additional.5=-Djavax.net.ssl.trustStorePassword=secret

The last step is to enable SSL encryption when applying data.  We need to set an extra URL option on the drizzle JDBC driver to turn on SSL. For this we need to edit the static-svc.properties file that configures replication.  In my example this file is located in /opt/continuent/tungsten/tungsten-replicator/conf/static-aws.properties.  Open the file and look for the section that starts with APPLIERS. Edit the text to add additional urlOptions line as shown below.  

############

# APPLIERS #

############

replicator.applier.dbms=com.continuent.tungsten.replicator.applier.MySQLDrizzleApplier

replicator.applier.dbms.host=${replicator.global.db.host}

replicator.applier.dbms.port=${replicator.global.db.port}

replicator.applier.dbms.user=${replicator.global.db.user}

replicator.applier.dbms.password=${replicator.global.db.password}

replicator.applier.dbms.urlOptions=?useSSL=true

replicator.applier.dbms.ignoreSessionVars=autocommit

replicator.applier.dbms.getColumnMetadataFromDB=true

Restart the replicator process (/opt/continuent/tungsten/tungsten-replicator/bin/replicator restart) and you will now be using SSL encryption.  If we now look back at the tcpdump outout, it looks like garbage as the following example shows. 

...

23:46:52.370904 IP (tos 0x0, ttl 64, id 5899, offset 0, flags [DF], proto TCP (6), length 105)

    logos2.44717 > ec2-54-241-56-140.us-west-1.compute.amazonaws.com.mysql: Flags [P.], cksum 0xb0ef (incorrect -> 0xd834), seq 3087:3140, ack 4838, win 102, options [nop,nop,TS val 89938121 ecr 74992771], length 53

E..i..@.@.r....n6.8......zSR.b.....f.......

.\X..xL.....07..|..x.)...T..888.H/...iz...^.W8....'...... ..J

...

This is much better.  We are replicating to an Amazon RDS, and the transactions are safe from prying eyes.  If you have gotten this far you are ready to try your own applications.  

Benefits of Replication into Amazon RDS

Amazon RDS is convenient thanks to its quick and simple setup, but the lack of replication is a severe limitation in building systems that need more than a single MySQL instance.  In particular it makes it hard to integrate RDS into systems that consist of more than Amazon RDS itself.  Adding the ability to replicate in real-time into RDS therefore has a number of benefits.  The most obvious include using RDS to extend existing systems.

First, Amazon RDS can offer a quick way to add read capacity to existing MySQL applications.  This is especially useful if you have a cluster, such as Tungsten, which handles your transaction processing and overall HA.  You can now add Amazon RDS read slaves that you discard when no longer needed.  Tungsten Replicator has a number of other useful features like the ability to read from a group of nodes, not just one, that make such topologies easy to set up and maintain.  Clusters other than Tungsten will likewise benefit from this feature. 

Second, Amazon RDS is suitable for applications that do not need 24x7 high availability (limitations include slow failover, no online maintenance, no cross-cloud capabilities, etc.)  You can now pull data from other sources and send them to Amazon RDS slaves for processing, which amounts to extending overall processing capacity.  For example, you could use RDS to run back-office tasks using transactions replicated in from MySQL masters.  Tungsten Replicator also replicates data from Oracle, so this is an additional source of transactions.  

There are of course other ways to replicate data to and from RDS, for example using batch ETL tools like Talend.  However, these are not real-time and often require application changes to add timestamp columns or otherwise mark transactions that need to be extracted.  Log-based replication as implemented by Tungsten is fast and has minimal impact on applications or MySQL itself.  

Thoughts about Further Improvements for Amazon RDS Replication

On our side, i.e., at Continuent, we need to do more testing, add documentation, and fix problems as they arise.  We are starting a beta test with one of our customers in the next few days, who incidentally was the same customer who requested this feature in the first place after hacking it for themselves.  RDS also has some interesting provisioning capabilities that I would like to understand better.  We are also adding options that eliminate the need for manual configuration of security settings.  This will keep us busy for a while.

Other improvements depend on changes to RDS itself.  An obvious and huge improvement would be to permit replication out of Amazon RDS.  Unfortunately, Tungsten needs a login that has REPLICATION_SLAVE privilege so that we can download binlog data.  That privilege is not yet available to Amazon users.  Once it is, Tungsten extraction will also work in very short order. We actually don't need other commands like START/STOP SLAVE or FLUSH LOGS--just ability to issue a COM_BINLOG_EVENT from a client connection and receive binlog records.  I am sure other products would use this capability as well.  (RDS developers, if you are listening here's an easy way to extend your product usability significantly...)

Replication is such a valuable feature of MySQL that Amazon RDS feels somewhat crippled without it.  For this reason I would imagine that Amazon will open up additional capabilities in the future.  Until then we will polish up replication from MySQL masters to Amazon RDS slaves, awaiting a time when we can add more features.  

In the meantime, I hope you will try the new replication to Amazon RDS.  As noted in this article you can grab the latest builds and try it yourself.  Please report your experiences through either Continuent Support if you are a customer or the Tungsten discussion list if you use the open source Tungsten Replicator.  I look forward to your feedback and suggestions for making Amazon RDS support better. 

Tungsten University

We have started a new series of webinars at Continuent that we call Tungsten University.  They provide education on Tungsten clustering and replication in handy one-hour chunks.  These are not sales pitches.  Our goal is to provide accessible education about setting up and operating Tungsten without any marketing fluff.

The first Tungsten University webinar entitled "Configure & provision Tungsten clusters" will take place on Thursday January 17th at 10:00 PST.  It will show you how to set up a cluster in Amazon EC2.  There will be a repeat on January 22nd at 15:00 GMT.  We usually record webinars, so you can look at them later as well. 

You do not have to be a customer to attend these webinars, just interested in Tungsten.  I hope users of our open source Tungsten Replicator will attend, since we will have a number of presentations on replication.  Here are some of the future webinar topics we are considering: 

• Setting up, deploying, and upgrading Tungsten Replicator 
• Setting up multi-master and fan-in replication topologies
• Configuring Tungsten Connector for transparent SQL routing and load balancing
• Replication tips and tricks (such as how to improve performance and fix broken replicators)
• Implementing zero-downtime maintenance and schema upgrade
• Replicating between MySQL and Oracle
• Loading MySQL data into a Vertica data warehouse

There will be more titles out soon, so watch our webinar list and the announcements on the Continuent Tungsten blog.  If there are topics that you would like to hear about please suggest them as comments on this blog or the official Continuent blog.  

Meanwhile, I am looking forward to doing some of the replicator presentations and attending the talks on clustering.  I mostly write code for the replicator, so talks about other parts of Tungsten tend to be learning experiences.  The Tungsten Connector is particularly interesting because it makes off-the-shelf database replicas look like a single DBMS server and transparently switches connections between them.  It is our secret sauce for creating master/slave clusters.  If you have not seen Tungsten Connector  before, I recommend attending that talk when it comes up.  I'm still amazed how well it functions even after working with it for a number of years.  

Questions about MariaDB JDBC Driver

The recent release of the MariaDB client libraries has prompted questions about their purpose as well as provenance.  Colin Charles posted that some of these would be answered in the very near future.  I have a couple of specific questions about the MariaDB JDBC driver, which I hope will be addressed at that time.  

1.) What is really in the MariaDB JDBC driver and how exactly does it differ from the drizzle JDBC driver?  What, if any, relation is there to Connector/J code?  There is a JIRA project but it contains only four bugs, hence is not very informative.  The launchpad bzr history shows detailed check-ins but not overall intent. 

2.) Why relicense from BSD to LGPL?  I have checked the class headers and so far as attributions are concerned everything seems to be done quite properly.  However, the license change appears to prevent those of us currently using the drizzle JDBC driver from transferring code changes back to the drizzle driver.  If so, that seems a little unneighborly.  

Here is some background on the relationship between the drivers.  The MariaDB JDBC client is a fork of the BSD-licensed drizzle JDBC driver originally developed by Marcus Eriksson, who continues to maintain the code.  According to the bzr change history the code forked after rev 253, which was 24 April 2011.  There are still many similarities in the Java classes.  For instance, a number of classes in the org.mariadb.jdbc.internal.common package differ by little other than licensing headers and package names.  The MariaDB code is now up to rev 375 and includes substantial changes that appear to be designed to bring the MariaDB JDBC driver closer to the capabilities of the MySQL Connector/J driver.  

At Continuent we have a lively interest in the drizzle JDBC driver, as we adopted it for Tungsten Replicator some time ago.  The code had fewer bugs than Connector/J, which was attractive.  More importantly, Marcus kindly accepted a patch from my colleague Stephane Giron (working as a Continuent employee) that made it easy for us to send queries using binary data rather than the usual Unicode data required by the JDBC standard.  This fix allows Tungsten to replicate codesets and binary data correctly.  We have since contributed a few other patches.  Our modest contribution in part reflects the quality of the base code. 

While waiting for answers I would like to commend Marcus as well as other drizzle contributors for their work.  We are particularly indebted to Marcus for starting and continuing the drizzle JDBC project.  Tungsten Replicator users have applied many trillions of transactions using the drizzle driver.  If the MariaDB JDBC driver gains wide acceptance, the rest of the MySQL community owes Marcus Eriksson substantial thanks as well.  

The MySQL Community: Beleaguered or Better than Ever?

The  MariaDB Foundation announcement spawned some interesting commentary about the state of open source databases.  One recent headline cited the "beleaguered MySQL community." Beleaguered is a delightful adjective.  The OED tells us that it means beset, invested, or besieged.  Much as I like the word, I do not think it is an accurate or useful description of the MySQL community.  This article and others like it miss the point of what is happening to MySQL and its users.

Let's start by disproving that the notion that the MySQL community is beleaguered.  I don't know everyone who uses MySQL, but in my job I talk to numerous companies that have made sizable investments in MySQL and stand to lose big if they are wrong.  They do not seem especially nervous.

1.  Nobody seriously questions MySQL viability.  I have yet to meet a manager with a substantial business on MySQL who is deeply worried about it disappearing or being ruined by Oracle.  They are too busy working on software upgrades or keeping their sites running.  The future of MySQL is well down the list of problems keeping them awake at night.  

2.  MySQL meets or beats the immediate alternatives.  There is of course discussion about dropping MySQL for PostgreSQL but it is mostly idle talk.  I'm sure some companies have switched (actually in both directions), but I not seen a single customer migrate a working business app from MySQL to PostgreSQL.  Once you get past the religion, it's clear MySQL and PostgreSQL are just too similar to supplant each other easily:  reliable, row-based stores with single threaded SQL query engines that handle a few terabytes of data at most.  Companies need far stronger reasons to switch to something new, especially given the large ecosystem and deep pool of MySQL expertise. 

3. MySQL is not the only game in town.  Virtually every large web site I know uses at least one NoSQL store alongside MySQL.  Column stores are increasingly common for data warehouses.  Production Hadoop clusters are no longer a novelty.  On the surface this might look like a failure of MySQL.  What's really happening in many cases is that small businesses that started on MySQL are now large, profitable enterprises that require more than just economical OLTP.  This is a mark of success, not a deficiency.

If this is what beleaguered looks like I can't wait to see something that's actually successful.

Turning the argument around, can we say that the MySQL community is better than it was?  In at least one important way, yes.  The community is now multi-polar.  MySQL long benefitted from having a large community of open source users to find bugs, help focus development direction, and construct a wide range of robust tools like language bindings.  However, innovation on MySQL itself was largely gated by a single company:  MySQL AB.  Multiple groups are now competing to improve MySQL, and it's a very good thing for users.  Let me count the ways.

There are three major versions of MySQL:  Oracle, Percona, and MariaDB, not to mention cloud-only versions like Amazon RDS.  There are at least four companies working directly on major upgrades to replication:  Continuent, Oracle, Codership, and Monty Program.  Oracle is continuing to make improvements in InnoDB like online schema change and multi-core scaling, efforts that are complemented by Percona's persistent focus on all aspect of performance.  Aside from Amazon RDS, all of this work is available in open source, and there is an unusual degree of sharing across otherwise competitive groups.  I could keep going for a while but to be frank there's so much it's hard to track all the improvements or give them their proper due.

The MySQL community is therefore competitive in a way that did not exist a few years ago. That's good, because innovation in data management is no longer centered around the web-facing applications that MySQL helped enable. Businesses are grappling with massive data volumes that far exceed the capacity of single DBMS servers while simultaneously moving to Amazon or VMWare. There is a whole new set of problems such as deploying in unstable cloud environments, adjusting to polyglot persistence, managing sharded data effectively, distributing data across multiple regions, and enabling real-time analytics on MySQL transactions. As a group, the MySQL community is well-positioned to address them.

If there is a problem, it is how to keep a strong multi-polar community going for as long as possible.   Competition creates uncertainty for users, because change is a given.  Pointy-haired bosses have to make decisions with incomplete information or even reverse them later. Competition is hard for vendors, because it is more difficult to make money in efficient markets.  Competition even strikes against the vanity of community contributors, who have to try harder to get recognition.  It is clear there will be pressures to make the community less competitive.  They won't necessarily be from Oracle, which thrives on competition.

This gets back to the MariaDB Foundation reference that started this article. Anything that ensures long-term competitiveness and vitality of MySQL is good.   Foundations in general seem well suited to this task.  At Continuent we have already had some discussions about joining. So far we are undecided, for reasons that are somewhat similar to Peter Zaitsev's comments on this subject.  If the MariaDB Foundation helps maintain a stable multi-polar community, we're in.  

Slides from Percona Live London and a Request

Percona hosted another excellent Percona Live conference this past December 3-4 in London.  It was my pleasure to deliver 3 talks including the first keynote following Peter Zaitsev.  Percona does a great job of organizing these conferences--this year's London conference was well attended and in an excellent location in Kensington.  My thanks to the entire Percona team for putting this together.

Here are the slides for my talks in case you would like to see them.

Keynote:  Future-Proofing MySQL for the World-Wide Data Revolution -- Covering the greatly exaggerated death of MySQL and design patterns for robust MySQL systems that can last for decades

Talk:  Why, What, and How of Data Warehouses for MySQL -- Why you need a data warehouse for MySQL, some standard choices, and how to move data in real time from MySQL to Vertica.  There was even a demo of sysbench data replicating into Vertica automatically.

Talk:  Multi-Master, Multi-Site MySQL Databases Made Easy with Continuent Tungsten -- How to build clusters that span multiple sites using multi-master and primary/DR techniques.  I did this with Giuseppe Maxia, who did a couple of great demos along the way, including one that I found kind of terrifying to do in front of an audience.   (It involved killing a lot of database servers, something Giuseppe does for a living.)

Speaking of talks, there are many conferences with database tracks coming up in the next few months.  If you have not done a talk on MySQL before I would encourage you to think about submitting for upcoming conferences like future Percona Live events, OSCON, O'Reilly Strata, or one of the many  local meet-ups.  There are many people doing interesting things with open source databases.  It's great to hear your stories, so speak up!