“The data you’re likely to need for any real-world predictive model today is unlikely to be sitting in any one data management system. A data scientist will often combine transactional data from a NoSQL system, demographic data from a RDBMS, unstructured data from Hadoop, and social data from a streaming API” –David Smith.
On the subject of Big Data Analytics I have interviewed David Smith, Vice President of Marketing and Community at Revolution Analytics.
RVZ
Q1. How would you define the job of a data scientist?
David Smith: A data scientist is someone charged of analyzing and communicating insight from data.
It’s someone with a combination of skills: computer science, to be able to access and manipulate the data; statistical modeling, to be able to make predictions from the data; and domain expertise, to be able to understand and answer the question being asked.
Q2. What are the main technical challenges for Big Data predictive analytics?
David Smith: For a skilled data scientist, the main challenge is time. Big Data takes a long time just to move (so don’t do that, if you don’t have to!), not to mention the time required to apply complex statistical algorithms. That’s why it’s important to have software that can make use of modern data architectures to fit predictive models to Big Data in the shortest time possible. The more iterations a data scientist can make to improve the model, the more robust and accurate it will be.
Q3. R is an open source programming language for statistical analysis. Is R useful for Big Data as well? Can you analyze petabytes of data with R, and at the same time ensure scalability and performance?
David Smith: Petabytes? That’s a heck of a lot of data: even Facebook has “only” 70 Pb of data, total. The important thing to remember is that “Big Data” means different things in different contexts: while raw data in Hadoop may be measured in the petabytes, by the time a data scientist selects, filters and processes it you’re more likely to be in the terabytes or even gigabyte range when the data’s ready to be applied to predictive models.
Open Source R , with its in-memory, single-threaded engine, will still struggle even at this scale, though. That’s why Revolution Analytics added scalable, parallelized algorithms to R, making predictive modeling on terabytes of data possible. With Revolution R Enterprise , you can use SMP servers or MPP grids to fit powerful predictive models to hundreds of millions of rows of data in just minutes.
Q4. Could you give us some information on how Google, and Bank of America use R for their statistical analysis?
David Smith: Google has more than 500 R users , where R is used to study the effectiveness of ads, for forecasting, and for statistical modeling with Big Data.
In the financial sector, R is used by banks like Bank of America and Northern Trust and insurance companies like Allstate for a variety of applications, including data visualization, simulation, portfolio optimization, and time series forecasting.
Q5. How do you handle the Big Data Analytics “process” challenges with deriving insight?
– capturing data
– aligning data from different sources (e.g., resolving when two objects are the same)
– transforming the data into a form suitable for analysis
– modeling it, whether mathematically, or through some form of simulation
– understanding the output
– visualizing and sharing the results
David Smith: These steps reflect the fact that data science is an iterative process: long gone are the days where we would simply pump data through a black-box algorithm and hope for the best. Data transformation, evaluation of multiple model options, and visualizing the results are essential to creating a powerful and reliable statistical model. That’s why the R language has proven so popular: its interactive language encourages exploration, refinement and presentation, and Revolution R Enterprise provides the speed and big-data support to allow the data scientist to iterate through this process quickly.
Q6. What is the tradeoff between Accuracy and Speed that you usually need to make with Big Data?
David Smith: Real-time predictive analytics with Big Data are certainly possible. (See here for a detailed explanation.) Accuracy comes with real-time scoring of the model, which is dependent on a data scientist building the predictive model on Big Data. To maintain accuracy, that model will need to be refreshed on a regular basis using the latest data available.
Q7. In your opinion, is there a technology which is best suited to build Analytics Platform? RDBMS, or instead non relational database technology, such as for example columnar database engine? Else?
David Smith: The data you’re likely to need for any real-world predictive model today is unlikely to be sitting in any one data management system. A data scientist will often combine transactional data from a NoSQL system, demographic data from a RDBMS, unstructured data from Hadoop, and social data from a streaming API.
That’s one of the reasons the R language is so powerful: it provides interfaces to a variety of data storage and processing systems, instead of being dependent on any one technology.
Q8. Cloud computing: What role does it play with Analytics? What are the main differences between Ground vs Cloud analytics?
David Smith: Cloud computing can be a cost-effective platform for the Big-Data computations inherent in predictive modeling: if you occasionally need a 40-node grid to fit a big predictive model, it’s convenient to be able to spin one up at will. The big catch is in the data: if your data is already in the cloud you’re golden, but if it lives in a ground-based data center it’s going to be expensive (in time *and* money) to move it to the cloud.
———
David Smith, Vice President, Marketing & Community, Revolution Analytics
David Smith has a long history with the R and statistics communities. After graduating with a degree in Statistics from the University of Adelaide, South Australia, he spent four years researching statistical methodology at Lancaster University in the United Kingdom, where he also developed a number of packages for the S-PLUS statistical modeling environment.
He continued his association with S-PLUS at Insightful (now TIBCO Spotfire) overseeing the product management of S-PLUS and other statistical and data mining products. David smith is the co-author (with Bill Venables) of the popular tutorial manual, An Introduction to R, and one of the originating developers of the ESS: Emacs Speaks Statistics project.
Today, David leads marketing for REvolution R, supports R communities worldwide, and is responsible for the Revolutions blog.
Prior to joining Revolution Analytics, David served as vice president of product management at Zynchros, Inc.
—
Related Posts
–Big Data Analytics at Netflix. Interview with Christos Kalantzis and Jason Brown. February 18, 2013
– Lufthansa and Data Analytics. Interview with James Dixon. February 4, 2013
–On Big Data Velocity. Interview with Scott Jarr. on January 28, 2013
Resources
– Big Data and Analytical Data Platforms
Blog Posts | Free Software | Articles | PhD and Master Thesis |
– Cloud Data Stores
Blog Posts | Lecture Notes| Articles and Presentations| PhD and Master Thesis|
– NoSQL Data Stores
Blog Posts | Free Software | Articles, Papers, Presentations|Documentations, Tutorials, Lecture Notes | PhD and Master Thesis
Follow ODBMS.org on Twitter: @odbmsorg
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“Our experience with MongoDB is that it’s architecture requires nodes to be declared as Master, and others as Slaves, and the configuration is complex and unintuitive. C* architecture is much simpler. Every node is a peer in a ring and replication is handled internally by Cassandra based on your desired redundancy level. There is much less manual intervention, which allows us to then easily automate many tasks when managing a C* cluster.” — Christos Kalantzis and Jason Brown.
Netflix, Inc. (NASDAQ: NFLX) is an online DVD and Blu-Ray movie retailer offering streaming movies through video game consoles, Apple TV, TiVo and more.
Last year, Netflix’s had a total of 29.4 million subscribers worldwide for their streaming service (Source).
I have interviewed Christos Kalantzis , Engineering Manager – Cloud Persistence Engineering and Jason Brown, Senior Software Engineer both at Netflix. They were involved in deploying Cassandra in a production EC2 environment at Netflix.
RVZ
Q1. What are the main technical challenges that Big data analytics pose to modern Data Centers?
Kalantzis, Brown: As companies are learning how to extract value from the data they already have, they are also identifying all the value they could be getting from data they are currently not collecting.
This is creating an appetite for more data collection. This appetite for more data, is pushing the boundaries of traditional RDBMS systems and forcing companies to research alternative data stores.
This new data size, also requires companies to think about the extra costs involved storing this new data (space/power/hardware/redundant copies).
Q2. How do you handle large volume of data? (terabytes to petabytes of data)?
Kalantzis, Brown: Netflix does not have its own datacenter. We store all of our data and applications on Amazon’s AWS. This allows us to focus on creating really good applications without the overhead of thinking about how we are going to architect the Datacenter to hold all of this information.
Economy of Scale also allows us to negotiate a good price with Amazon.
Q3. Why did you choose Apache Cassandra (C*)?
Kalantzis, Brown: There’s several reasons we selected Cassandra. First, as Netflix is growing internationally, a solid multi-datacenter story is important to us. Configurable replication and consistency, as well as resiliency in the face of failure is an absolute requirement, and we have tested those capabilities more than once in production! Other compelling qualities include being an open source, Apache project and having an active and vibrant user community.
Q4: What do you exactly mean with “a solid multi-datacenter story is important to us”? Please explain.
Kalantzis, Brown: As we expand internationally we are moving and standing up new datacenters close to our new customers. In many cases we need a copy of the full dataset of an application. It is important that our Database Product be able to replicate across multiple datacenters, reliably, efficiently and with very little lag.
Q5. Tell us a little bit about the application powered by C*
Kalantzis, Brown: Almost everything we run in the cloud (which is almost the entire Netflix infrastructure) uses C* as a database. From customer/subscriber information, to movie metadata, to monitoring stats, it’s all hosted in Cassandra.
In most of our uses, Cassandra is the source of truth database. There are a few legacy datasets in other solutions, but are actively being migrated.
Q6: What are the typical data insights you obtained by analyzing all of these data? Please give some examples. How do you technically analyze the data? And by the way, how large are your data sets?
Kalantzis, Brown: All the data Netflix gathers goes towards improving the customer experience. We analyze our data to understand viewing preferences, give great recommendations and make appropriate choices when buying new content.
Our BI team has done a great job with the Hadoop platform and has been able to extract the information we need from the terabytes of data we capture and store.
Q7. What Availability expectations do you have from customers?
Kalantzis, Brown: Our internal teams are incredibly demanding on every part of the infrastructure, and databases are certainly no exception. Thus a database solution must have low latency, high throughput, strict uptime/availability requirements, and be scalable to massive amounts of data. The solution must, of course, be able to withstand failure and not fall over.
Q8: Be scalable up to?
Kalantzis, Brown: We don’t have an upper boundary to how much data an application can store. That being said, we also expect the application designers to be intelligent with how much data they need readily available in their OLTP system. Our Applications store anywhere from 10 GB to 100 terabyte [Edit corrected a typo] of data in their respective C* clusters. C* architecture is such that the cluster’s capacity grows linearly with every node added to the cluster. So in theory we can scale “infinitely”.
Q9. What other methods did you consider for continuous availability?
Kalantzis, Brown: We considered and experimented with MongoDB, yet the operational overhead and complexity made it unmanageable so we quickly backed away from it. One team even built a sharded RDBMS cluster, with every node in the cluster being replicated twice. This solution is also very complex to manage. We are currently working to migrate to C* for that application.
Q10. Could you please explain in a bit more detail, what kind of complexity made it unmanageable using MongoDB for you?
Kalantzis, Brown: Netflix strives to choose architectures that are simple and require very little in the way of manual intervention to manage and scale. Our experience with MongoDB is that it’s architecture requires nodes to be declared as Master, and others as Slaves, and the configuration is complex and unintuitive. C* architecture is much simpler. Every node is a peer in a ring and replication is handled internally by Cassandra based on your desired redundancy level. There is much less manual intervention, which allows us to then easily automate many tasks when managing a C* cluster.
Q11. How many data centers are you replicating among?
Kalantzis, Brown: For most workloads, we use two Amazon EC2 regions. For very specific workloads, up to four EC2 regions.
To slice that further, each region has two to three availability zones (you can think of an availability zone as the closest equivalent to a traditional data center). We shard and replicate the data within a region across it’s availability zones, and replicate between regions.
Q12: When you shard data, don’t you have a possible data consistency problem when updating the data?
Kalantzis, Brown: Yes, when writing across multiple nodes there is always the issue of consistency. C* “solves” this by allowing the client (Application) to choose the consistency level it desires when writing. You can choose to write to only 1 node, all nodes or a quorum of nodes. Each choice offers a different level of consistency and the application will only return from its write statement when the desired consistency is reached.
The same is with reading, you can choose the level of consistency you desire with every statement. The timestamp of each record will be compared, and make sure to only return the latest record.
In the end the application developer needs to understand the trade offs of each setting (speed vs consistency) and make the appropriate decision that best fits their use case.
Q13. How do you reduce the I/O bandwidth requirements for big data analytics workloads?
Kalantzis, Brown: What is great about C*, is that it allows you to scale linearly with commodity hardware. Furthermore adding more hardware is very simple and the cluster will rebalance itself. To solve the I/O issue we simply add more nodes. This reduces the amount of data stored in each node allowing us to get as close as possible to the ideal data:memory ratio of 1.
Q14. What is the tradeoff between Accuracy and Speed that you usually need to make with Big Data?
Kalantzis, Brown: When we chose C* we made a conscious decision to accept eventually consistent data.
We decided write/read speed and high availability was more important than consistency. Our application is such that this tradeoff, although might rarely provide inaccurate data (start a movie at the wrong location), it does not negatively impact a user. When an application does require accuracy, then we increase the consistency to quorum.
Q15. How do you ensure that your system does not becomes unavailable?
Kalantzis, Brown: Minimally, we run our c* clusters (over 50 in production now) across multiple availabilty zones within each region of EC2. If a cluster is multiregion (multi-datacenter), then one region is naturally isolated (for reads) from the other; all writes will eventually make it to all regions due to cass
Q16. How do you handle information security?
Kalantzis, Brown: We currently rely on Amazon’s security features to limit who can read/write to our C* clusters. As we move forward with our cloud strategy and want to store Financial data in C* and in the cloud, we are hoping that new security features in DSE 3.0 will provide a roadmap for us. This will enable us to move even more sensitive information to the Cloud & C*.
Q17. How is your experience with virtualization technologies?
Kalantzis, Brown: Netflix runs all of its infrastructure in the cloud (AWS). We have extensive experience with Virtualization, and fully appreciate all the Pros and Cons of virtualization.
Q18 How operationally complex is it to manage a multi-datacenter environment?
Kalantzis, Brown: Netflix invested heavily in creating tools to manage multi-datacenter and multi-region computing environments. Tools such as Asgard & Priam, has made that management easy and scalable.
Q19. How do you handle modularity and flexibility?
Kalantzis, Brown: At the data level, each service typically has it’s own Cassandra cluster so it can scale independently of other services. We are developing internal tools and processes for automatically consolidating and splitting clusters to optimize efficiency and cost. At the code level, we have built and open sourced our java Cassandra client, Astyanax , and added many recipes for extending the use patterns of Cassandra.
Q20. How do you reduce (if any) energy use?
Kalantzis, Brown: Currently, we do not. However, C* is introducing a new feature in version 1.2 called ‘virtual nodes’. The short explanation of virtual nodes is that it makes scaling up and down the size of a c* cluster much easier to manage. Thus, we are planning on using the virtual nodes concept with EC2 auto-scaling, so we would scale up the number of nodes in a C* cluster during peak times, and reduce the node count during troughs. We’ll save energy (and money) by simply using less resources when demand is not present.
Q21. What advice would you give to someone needing to ensure continuous availability?
Kalantzis, Brown: Availability is just one of the 3 dimensions of CAP (Concurrency, Availability and “Partitionability”). They should evaluate which of the other 2 dimensions are important to them and choose their technology accordingly. C* solves for A&P (and some of C).
Q22. What are the main lessons learned at Netflix in using and deploying Cassandra in a production EC2 environment?
Kalantzis, Brown: We learned that deploying C* (or any database product) in a virtualized environment has trade offs. You trade the fact that you have easy and quick access to many virtual machines, yet each virtual machine has MUCH less IOPS than a traditional server. Netflix has learned how to deal with those limitations and trade-offs, by sizing clusters appropriately (number of nodes to use).
It has also forced us to reimagine the DBA role as a DB Engineer role, which means we work closely with application developers to make sure that their schema and application design is as efficient as possible and not access the data store unnecessarily.
————-
Christos Kalantzis
(@chriskalan) Engineering Manager – Cloud Persistence Engineering Netflix
Previously Engineering-Platform Manager YouSendIt
A Tech enthusiast at heart, I try to focus my efforts in creating technology that enhances our lives.
I have built and lead teams at YouSendIt and Netflix which has lead to the scaling out of persistence layers, the creation of a cloud file system and the adoption of Apache Cassandra as a scalable and highly available data solution.
I’ve worked as a DB2, SQL Server and MySQL DBA for over 10 years and through, sometimes painful, trial and error I have learned the advantages and limitations of RDBMS and when the modern NoSQL solutions make sense.
I believe in sharing knowledge, that is why I am a huge advocate of Open Source software. I share my software experience through blogging, pod-casting and mentoring new start-ups. I sit on the tech advisory board of the OpenFund Project which is an Angel VC for European start-ups.
Jason Brown
(@jasobrown), Senior Software Engineer, Netflix
Jason Brown is a Senior Software Engineer at Netflix where he led the pilot project for using and deploying Cassandra in a production EC2 environment. Lately he’s been contributing to various open source projects around the Cassandra ecosystem, including the Apache Cassandra project itself. Holding a Master’s Degree in Music Composition, Jason longs to write a second string quartet.
———-
Related Posts
– Lufthansa and Data Analytics. Interview with James Dixon. February 4, 2013
–On Big Data, Analytics and Hadoop. Interview with Daniel Abadi. December 5, 2012
–Big Data Analytics– Interview with Duncan Ross.November 12, 2012
Follow ODBMS.org on Twitter: @odbmsorg
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“With MySQL 5.6, developers can now commingle the “best of both worlds” with fast key-value look up operations and complex SQL queries to meet user and application specific requirements” –Tomas Ulin.
On February 5, 2013, Oracle announced the general availability of MySQL 5.6.
I have interviewed Tomas Ulin, Vice President for the MySQL Engineering team at Oracle. I asked him several questions on the state of the union for MySQL.
RVZ
Q1. You support several different versions of the MySQL database. Why? How do they differ with each other?
Tomas Ulin: Oracle provides technical support for several versions of the MySQL database to allow our users to maximize their investments in MySQL. Additional details about Oracle’s Lifetime Support policy can be found here.
Each new version of MySQL has added new functionality and improved the user experience. Oracle just made available MySQL 5.6, delivering enhanced linear scalability, simplified query development, better transactional throughput and application availability, flexible NoSQL access, improved replication and enhanced instrumentation.
Q2. Could you please explain in some more details how MySQL can offer a NoSQL access?
Tomas Ulin: MySQL 5.6 provides simple, key-value interaction with InnoDB data via the familiar Memcached API. Implemented via a new Memcached daemon plug-in to mysqld, the new Memcached protocol is mapped directly to the native InnoDB API and enables developers to use existing Memcached clients to bypass the expense of query parsing and go directly to InnoDB data for lookups and transactional compliant updates. With MySQL 5.6, developers can now commingle the “best of both worlds” with fast key-value look up operations and complex SQL queries to meet user and application specific requirements. More information is available here.
MySQL Cluster presents multiple interfaces to the database, also providing the option to bypass the SQL layer entirely for native, blazing fast access to the tables. Each of the SQL and NoSQL APIs can be used simultaneously, across the same data set. NoSQL APIs for MySQL Cluster include memcached as well as the native C++ NDB API, Java (ClusterJ and ClusterJPA) and HTTP/REST. Additionally, during our MySQL Connect Conference last fall, we announced a new Node.js NoSQL API to MySQL Cluster as an early access feature. More information is available here.
Q3. No single data store is best for all uses. What are the applications that are best suited for MySQL, and which ones are not?
Tomas Ulin: MySQL is the leading open source database for web, mobile and social applications, delivered either on-premise or in the cloud. MySQL is increasingly used for Software-as-a-Service applications as well.
It is also a popular choice as an embedded database with over 3,000 ISVs and OEMs using it.
MySQL is also widely deployed for custom IT and departmental enterprise applications, where it is often complementary to Oracle Database deployments. It also represents a compelling alternative to Microsoft SQL Server with the ability to reduce database TCO by up to 90 percent.
From a developer’s perspective, there is a need to address growing data volumes, and very high data ingestion and query speeds, while also allowing for flexibility in what data is captured. For this reason, the MySQL team works to deliver the best of the SQL and Non-SQL worlds to our users, including native, NoSQL access to MySQL storage engines, with benchmarks showing 9x higher INSERT rates than using SQL, while also supporting online DDL.
At the same time, we do not sacrifice data integrity by trading away ACID compliance, and we do not trade away the ability to run complex SQL-based queries across the same data sets. This approach enables developers of new services to get the best out of MySQL database technologies.
Q4. Playful Play, a Mexico-based company, is using MySQL Cluster Carrier Grade Edition (CGE) to support three million subscribers on Facebook in Latin America. What are the technical challenges they are facing for such project? How do they solve them?
Tomas Ulin: As a start-up business, fast time to market at the lowest possible cost was their leading priority. As a result, they developed the first release of the game on the LAMP stack.
To meet both the scalability and availability requirements of the game, Playful Play initially deployed MySQL in a replicated, multi-master configuration.
As Playful Play’s game, La Vecidad de El Chavo, spread virally across Facebook, subscriptions rapidly exceeded one million users, leading Playful Play to consider how to best architect their gaming platforms for long-term growth.
The database is core to the game, responsible for managing:
• User profiles and avatars
• Gaming session data;
• In-app (application) purchases;
• Advertising and digital marketing event data.
In addition to growing user volumes, the El Chavo game also added new features that changed the profile of the database. Operations became more write-intensive, with INSERTs and UPDATEs accounting for up to 70 percent of the database load.
The game’s popularity also attracted advertisers, who demanded strict SLAs for both performance (predictable throughput with low latency) as well as uptime.
After their evaluation, PlayFul Play decided MySQL Cluster was best suited to meet their needs for scale and HA.
After their initial deployment, they engaged MySQL consulting services from Oracle to help optimize query performance for their environment, and started to use MySQL Cluster Manager to manage their installation, including automating the scaling of their infrastructure to support the growth from 30,000 new users every day. With subscriptions to MySQL Cluster CGE, which includes Oracle Premier Support and MySQL Cluster Manager in an integrated offering, Playful Play has access to qualified technical and consultative support which is also very important to them.
Playful Play currently supports more than four million subscribers with MySQL.
More details on their use of MySQL Cluster are here.
Q5. What are the current new commercial extensions for MySQL Enterprise Edition? How do commercial extensions differ from standard open source features of MySQL?
Tomas Ulin: MySQL Community Edition is available to all at no cost under the GPL. MySQL Enterprise Edition includes advanced features, management tools and technical support to help customers improve productivity and reduce the cost, risk and time to develop, deploy and manage MySQL applications. It also helps customers improve the performance, security and uptime of their MySQL-based applications.
This link is to a short demo that illustrates the added value MySQL Enterprise Edition offers.
Further details about all the commercial extensions can be found in this white paper. (Edit: You must be logged in to access this content.)
The newest additions to MySQL Enterprise Edition, released last fall during MySQL Connect, include:
* MySQL Enterprise Audit, to quickly and seamlessly add policy-based auditing compliance to new and existing applications.
* Additional MySQL Enterprise High Availability options, including Distributed Replicated Block Device (DRBD) and Oracle Solaris Clustering, increasing the range of certified and supported HA options for MySQL.
Q6. In September 2012, Oracle announced the first development milestone release of MySQL Cluster 7.3. What is new?
Tomas Ulin: The release comprises:
• Development Release 1: MySQL Cluster 7.3 with Foreign Keys. This has been one of the most requested enhancements to MySQL Cluster – enabling users to simplify their data models and application logic – while extending the range of use-cases.
• Early Access “Labs” Preview: MySQL Cluster NoSQL API for Node.js. Implemented as a module for the V8 engine, the new API provides Node.js with a native, asynchronous JavaScript interface that can be used to both query and receive results sets directly from MySQL Cluster, without transformations to SQL. This gives lower latency for simple queries, while also allowing developers to build “end-to-end” JavaScript based services – from the browser, to the web/application layer through to the database, for less complexity.
• Early Access “Labs” Preview: MySQL Cluster Auto-Installer. Implemented with a standard HTML GUI and Python-based web server back-end, the Auto-Installer intelligently configures MySQL Cluster based on application requirements and available hardware resources. This makes it simple for DevOps teams to quickly configure and provision highly optimized MySQL Cluster deployments – whether on-premise or in the cloud.
Additional details can be found here.
Q7. John Busch, previously CTO of former Schooner Information Technology commented in a recent interview (1): “legacy MySQL does not scale well on a single node, which forces granular sharding and explicit application code changes to make them sharding-aware and results in low utilization of severs”. What is your take on this?
Tomas Ulin: Improving scalability on single nodes has been a significant area of development, for example MySQL 5.6 introduced a series of enhancements which have been further built upon in the server, optimizer and InnoDB storage engine. Benchmarks are showing close to linear scalability for systems with 48 cores / threads, with 230% higher performance than MySQL 5.5. Details are here.
Q8. Could you please explain in your opinion the trade-off between scaling out and scaling up? What does it mean in practice when using MySQL?
Tomas Ulin: On the scalability front, MySQL has come a long way in the last five years and MySQL 5.6 has made huge improvements here – depending on your workload, you may scale well up to 32 or 48 cores. While the old and proven techniques can work well as well: you may use master -slave(s) replication and split your load by having writes on the master only, while reads on slave(s), or using “sharding” to partition data across multiple computers. The question here is still the same: do you hit any bottlenecks on a single (big) server or not?… – and if yes, then you may start to think which kind of “distributed” solution is more appropriate for you. And it’s not only MySQL server related — similar problems and solutions are coming with all applications today targeting a high load activity.
Some things to consider…
Scale-up pros:
– easier management
– easier to achieve consistency of your data
Scale-up cons:
– you need to dimension your server up-front, or take into account the cost of throwing away your old hardware when you scale
– cost/performance is typically better on smaller servers
– in the end higher cost
– at some point you reach the limit, i.e. you can only scale-up so much
Scale-out pros:
– you can start small with limited investment, and invest incrementally as you grow, reusing existing servers
– you can choose hardware with the optimal cost/performance
Scale-out cons:
– more complicated management
– you need to manage data and consistency across multiple servers, typically by making the application/middle tier aware of the data distribution and server roles in your scale-out (choosing MySQL Cluster for scale-out does not incur this con, only if you choose a more traditional master-slave MySQL setup)
Q9. How can you obtain scalability and high-performance with Big Data and at the same time offer SQL joins?
Tomas Ulin: Based on estimates from leading Hadoop vendors, around 80 percent of their deployments are integrated with MySQL.
As discussed above, there has been significant development in NoSQL APIs to InnoDB and MySQL Cluster storage engines which allow high speed ingestion of high velocity data Key/Value data, but which also allows complex queries, including JOIN operations to run across that same data set using SQL.
Technologies like Apache Sqoop are commonly used to load data to and from MySQL and Hadoop, so many users will JOIN structured, relational data from MySQL with unstructured data such as clickstreams within Map/Reduce processes within Hadoop. We are also working on our Binlog API to enable real time CDC with Hadoop. More on the Binlog API is here.
Q10. Talking about scalability and performance what are the main differences if the database is stored on hard drives, SAN, flash memory (Flashcache)? What happens when data does not fit in DRAM?
Tomas Ulin: The answer depends on your workload.
The most important question is: “how long is your active data set remaining cached?” — if not long at all, then it means your activity will remain I/O-bound, and having faster storage here will help. But, if the “active data set” is small enough to be cached most of the time, the impact of the faster storage will not be as noticeable. MySQL 5.6 delivers many changes improving performance on heavy I/O-bound workloads.
————–
Mr. Tomas Ulin has been working with the MySQL Database team since 2003. He is Vice President for the MySQL Engineering team, responsible for the development and maintenance of the MySQL related software products within Oracle, such as the MySQL Server, MySQL Cluster, MySQL Connectors, MySQL Workbench, MySQL Enterprise Backup, and MySQL Enterprise Monitor. Prior to working with MySQL his background was in the telecom industry, working for the Swedish telecom operator Telia and Telecom vendor Ericsson. He has a Masters degree in Computer Science and Applied Physics from Case Western Reserve University and a PhD in Computer Science from the Royal Institute of Technology.
Related Posts
(1): A super-set of MySQL for Big Data. Interview with John Busch, Schooner. on February 20, 2012
(2): Scaling MySQL and MariaDB to TBs: Interview with Martín Farach-Colton. on October 8, 2012.
–On Eventual Consistency– Interview with Monty Widenius.on October 23, 2012
Resources
ODBMS.org: Relational Databases, NewSQL, XML Databases, RDF Data Stores:
Blog Posts | Free Software | Articles and Presentations| Lecture Notes | Tutorials| Journals |
Follow us on Twitter: @odbmsorg
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“Lufthansa is now able to aggregate and feed data into a management cockpit to analyze collected data for key decision-making purposes in the future. Users get instantly notified of transmission errors, enabling the company to detect patterns on large amounts of data at a rapid speed. There is also an automatic alarm messages sent out to IT product management, and partner airlines are informed of errors right away in the case of transmission errors between different IT systems for passenger data. Lufthansa is now able to comprehensively monitor one of its most important core processes in real-time for quality management: the handover of passenger data between different airlines” — James Dixon.
On the state of the market for Big Data Analytics I have Interviewed James Dixon, co-founder and Chief Geek / CTO, Pentaho Corporation.
RVZ
Q1. What is In your opinion the expected realistic Market Demand for Big Data analytics?
James Dixon: Big. Until recently it has not been possible to perform analysis of sub-transactional and detailed operational data for a reasonable price-tag. Systems such as Hadoop and the NoSQL repositories such as MongoDB and Cassandra make it possible to economically store and process large amount of data. The first use of this data is often to answer operational and tactical questions. Shortly after that comes the desire to answer managerial and strategic questions, and this where Big Data Analytics comes in. I estimate that 90% of all Big Data repositories will have some form of reporting/visualization/analysis requirement applied to it.
Q2. Aren’t we too early with respect to the maturity of the Big Data Analytics technology and the market acceptance?
James Dixon: We are early, but not too early. There is significant market acceptance in certain domains already – financial services, SaaS application providers, and media companies to name a few. As these initial markets mature we will see common use cases emerge and public endorsements of these technologies, this will help to increase acceptance in other markets. We’ve seen a significant uptake in commercial deals over the last few quarters whereas 2011 was more tire-kicking and exploratory.
Q3. Pentaho has worked with Lufthansa to improve their passenger handling. Could you please tell us more about this? In particular what requirement and technical challenges did you have for this project? And how did you solve them?
James Dixon: Lufthansa needed a solution that would make the core processes of Inter Airline Through Check In (IATCI) accessible, measurable and available for real-time operational monitoring. They also wanted to deliver consolidated management reporting dashboards to inform decision making out of this information. This was implemented by Pentaho’s services organization with onsite training and consulting. Our Pentaho Business Analytics suite was used for the front-end for real-time data analysis and report generation. In the back-end, Pentaho Data Integration (aka Kettle) retrieves, transforms and loads the message data streams into the data warehouse on a continuous basis.
Q4. And what results did you obtain so far?
James Dixon: Lufthansa is now able to aggregate and feed data into a management cockpit to analyze collected data for key decision-making purposes in the future. Users get instantly notified of transmission errors, enabling the company to detect patterns on large amounts of data at a rapid speed. There is also an automatic alarm messages sent out to IT product management, and partner airlines are informed of errors right away in the case of transmission errors between different IT systems for passenger data. Lufthansa is now able to comprehensively monitor one of its most important core processes in real-time for quality management: the handover of passenger data between different airlines. With Pentaho, Lufthansa is now instantly aware if they are dealing with a single occurrence of an error or if there is a pattern. They can immediately take action in order to minimize the impact on their passengers.
Q5. What is special about Pentaho’s big data analytic platform? How does it differ with respect to other vendors?
James Dixon: We have an end-to-end offering that encompasses data integration/orchestration across Big Data and regular data stores/sources, data transformation, desktop and web-based reporting, slice-and-dice analysis tools, dash boarding, and predictive analytics. Very few vendors have the breadth of technology that we do, and those that do are mainly pushing hardware and services. We enable the creation of hybrid solutions that allow companies to use the most appropriate data storage technology for every part of their system – we don’t force you to load all your data into Hadoop, for example. From an architecture perspective our ability to run our data integration engine inside of MapReduce tasks on the data nodes is a unique capability. And we provide analytics directly on top of big data tech that gives users instant results via our schema-on-read approach – you don’t have to predefine ETL or Schemas or Data Marts – we do it on the fly.
Q6. What are the technical challenges in creating and viewing Analytics on the iPad?
James Dixon: The navigation concepts are different on mobile devices, so the overall user experience of the analysis software needs to be adapted for the iPad. Vendors need to be sensitive to the interaction techniques that the touch screens provide. We have changed the way that all of our end-user web-based interfaces work so that experience on the iPad is similar. It is possible to allow ad-hoc analysis and content authoring on the iPad, and Pentaho provides that with our recent V4.8 release.
Q7. Big Data and Mobile: what are the challenges and opportunities?
James Dixon: There are some use cases that are easy to identify. Report bursting to mobile and non-mobile devices is a technique that is easy to do today. Real-time analysis of Big Data combined with the alerting and notification capability of mobile devices is an interesting combination.
Q8. How are you supposed to view complex analytics with the limited display of a mobile phone?
James Dixon: Even with a desktop computer and a large monitor, analysis of Big Data requires lots of aggregation and/or lots of filtering. If you could display all the raw data from a Big Data repository, you would not be able to interpret it. As the display gets smaller the amount of aggregation and filtering has to go up, and the complexity has to come down. It is possible to do reasonably complex analysis on a tablet, but it is certainly a challenge on the smaller devices.
Q9. What are the main technical and business challenges that customers face when they want to use Cloud analytics deployments?
James Dixon: Moving large amounts of data around is a hurdle for some organizations. For this reason cloud analytics is not very appealing to companies with established data centers. However young companies that exclusively use hosted applications do not have their data on-premise. As these companies grow and mature we will see the market for cloud analytics increase.
Q10. Pentaho has announced in July this year a technical integration of their analytics platform with Cloudera. What is the technical and business meaning of this? What are results obtained so far?
James Dixon:We are working closely with Coudera on a technical and business level. For example we worked with Cloudera to test their new Impala database with Pentaho’s analytics, so that we could demo the integration on the day that Impala was announced. We also have joint marketing campaigns and sales field engagement, as customers of Cloudera find tremendous benefit in engaging with Pentaho and vice-versa. Our tech makes it much easier and 20x faster to get Hadoop productive so their customers gravitate to us naturally.
—–
James Dixon, Founder and Chief Geek / CTO, Pentaho Corporation
As “Chief Geek” (CTO) at Pentaho, James Dixon is responsible for Pentaho’s architecture and technology roadmap. James has over 15 years of professional experience in software architecture, development and systems consulting. Prior to Pentaho, James held key technical roles at AppSource Corporation (acquired by Arbor Software which later merged into Hyperion Solutions) and Keyola (acquired by Lawson Software). Earlier in his career, James was a technology consultant working with large and small firms to deliver the benefits of innovative technology in real-world environments.
Related Posts
– On Big Data Velocity. Interview with Scott Jarr. on January 28, 2013
– The Gaia mission, one year later. Interview with William O’Mullane. on January 16, 2013
– Big Data Analytics– Interview with Duncan Ross on November 12, 2012
– On Big Data, Analytics and Hadoop. Interview with Daniel Abadi. on December 5, 2012
– Managing Big Data. An interview with David Gorbet on July 2, 2012
– Analytics at eBay. An interview with Tom Fastner. on October 6, 2011
Resources
– Big Data: Challenges and Opportunities.
Roberto V. Zicari, October 5, 2012.
Abstract: In this presentation I review three current aspects related to Big Data:
1. The business perspective, 2. The Technology perspective, and 3. Big Data for social good.
Presentation (89 pages) | Intermediate| English | DOWNLOAD (PDF)| October 2012|
– ODBMS.org: Big Data and Analytical Data Platforms.
Blog Posts | Free Software | Articles | PhD and Master Thesis |
You can follow ODBMS.org on Twitter : @odbmsorg.
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“There is only so much static data in the world as of today. The vast majority of new data, the data that is said to explode in volume over the next 5 years, is arriving from a high velocity source. It’s funny how obvious it is when you think about it. The only way to get Big Data in the future is to have it arrive in a high velocity rate ” — Scott Jarr.
One of the key technical challenges of Big Data is (Data) Velocity. On that, I have interviewed Scott Jarr, Co-founder and Chief Strategy Officer of VoltDB.
RVZ
Q1. Marc Geall, past Head of European Technology Research at Deutsche Bank AG/London, writes about the “Big Data myth”, claiming that there is:
1) limited need of petabyte-scale data today,
2) very low proportion of databases in corporate deployment which requires more than tens of TB of data to be handled, and
3) lack of availability and high cost of highly skilled operators (often post-doctoral) to operate highly scalable NoSQL clusters.
What is your take on this?
Scott Jarr: Interestingly I agree with a lot of this for today. However, I also believe we are in the midst of a massive shift in business to what I call data-as-a-priority.
We are just beginning, but you can already see the signs. People are loathed to get rid of anything, sensors are capturing finer resolutions, and people want to make far more, data informed decisions.
I also believe that the value that corporate IT teams were able to extract from data with the advent of data warehouses really whet the appetite of what could be done with data. We are now seeing people ask questions like “why can’t I see this faster,” or “how do we use this incoming data to better serve customers,” or “how can we beat the other guys with our data.”
Data is becoming viewed as a corporate weapon. Add inbound data rates (velocity) combined with the desire to use data for better decisions and you have data sizes that will dwarf what is considered typical today. And almost no industry is excluded. The cost ceiling has collapsed.
Q2: What are the typical problems that are currently holding back many Big Data projects?
Scott Jarr:
1) Spending too much time trying to figure out what solution to use for what problem. We were seeing this so often that we created a graphic and presentation that addresses this topic. We called it the Data Continuum.
2) Putting out fires that the current data environment is causing. Most infrastructures aren’t ready for the volume or velocity of data that is already starting to arrive at their doorsteps. They are spending a ton of time dealing with band-aids on small-data-infrastructure and unable to shake free to focus on the Big Data infrastructure that will be a longer-term fix.
3) Being able to clearly articulate the business value the company expects to achieve from a Big Data project has a way of slowing things down in a radical way.
4) Most of the successes in Big Data projects today are in situations where the company has done a very good job maintaining a reasonable scope to the project.
Q3: Why is it important to solve the Velocity problem when dealing with Big Data projects?
Scott Jarr: There is only so much static data in the world as of today. The vast majority of new data, the data that is said to explode in volume over the next 5 years, is arriving from a high velocity source. It’s funny how obvious it is when you think about it. The only way to get Big Data in the future is to have it arrive in a high velocity rate.
Companies are recognizing the business value they can get by acting on that data as it arrives rather than depositing it in a file to be batch processed at some later data. So much of the context that makes that data is lost when it not acted on quickly.
Q4: What exactly is Big Data Velocity? Is Big Data Velocity the same as stream computing?
Scott Jarr: We think of Big Data Velocity as data that is coming into the organization at a rate that can’t be managed in the traditional database. However, companies want to extract the most value they can from that data as it arrives. We see them doing three specific things:
1) Ingesting relentless feed(s) of data;
2) Making decisioning on each piece of data as it arrives; and
3) Using real-time analytics to derive immediate insights into what is happening with this velocity data.
Making the best possible decision each time data is touched is what velocity is all about. These decisions used to be called transactions in the OLTP world. They involve using other data stored in the database to make decision – approve a transaction, server the ad, authorize the access, etc. These decisions, and the real-time analytics that support them, all require the context of other data. In other words, the database used to perform these decisions must hold some amount of previously processed data – they must hold state. Streaming systems are good at a different set of problems.
Q5: Two other critical factors often mentioned for Big Data projects are: 1) Data discovery: How to find high-quality data from the Web? and 2) Data Veracity: How can we cope with uncertainty, imprecision, missing values, mis-statements or untruths? Any thoughts on these?
Scott Jarr: We have a number of customers who are using VoltDB in ways to improve data quality within their organization. We have one customer who is examining incoming financial events and looking for misses in sequence numbers to determine lost or miss-ordered information. Likewise, a popular use case is to filter out bad data as it comes in by looking at it in its high velocity state against a known set of bad or good characteristics. This keeps much of the bad data from ever entering the data pipeline.
Q6: Scalability has three aspects: data volume, hardware size, and concurrency. Scale and performance requirements for Big Data strain conventional databases. Which database technology is best to scale to petabytes?
Scott Jarr: VoltDB is focused on a very different problem, which is how to process that data prior to it landing in the long-term petabyte system. We see customers deploying VoltDB in front of both MPP OLAP and Hadoop, in roughly the same numbers. It really all depends on what the customer is ultimately trying to do with the data once it settles into its resting state in the petabyte store.
Q7: A/B testing, sessionization, bot detection, and pathing analysis all require powerful analytics on many petabytes of semi-structured Web data. Do you have some customers examples in this area?
Scott Jarr: Absolutely. Taken broadly, this is one of the most common uses of VoltDB. Micro-segmentation and on-the-fly ad content optimization are examples that we see regularly. The ability to design an ad, in real-time, based on five sets of audience meta-data can have a radical impact on performance.
Q8: When would you recommend to store Big Data in a traditional Data Warehouse and when in Hadoop?
Scott Jarr: My experience here is limited. As I said, our customers are using VoltDB in front of both types of stores to do decisioning and real-time analytics before the data moves into the long term store. Often, when the data is highly structured, it goes into a data warehouse and when it is less structured, it goes into Hadoop.
Q9: Instead of stand-alone products for ETL, BI/reporting and analytics wouldn’t it be better to have a seamless integration? In what ways can we open up a data processing platform to enable applications to get closer?
Scott Jarr: This is very much inline with our vision of the world. As Mike (Stonebraker , VoltDB founder) has stated for years, in high performance data systems, you need to have specialized databases. So we see the new world having far more data pipelines than stand alone databases. A data pipeline will have seamless integrations between velocity stores, warehouses, BI tools and exploratory analytics. Standards go a long way to making these integrations easier.
Q10: Anything you wish to add?
Scott Jarr.: Thank you Roberto. Very interesting discussion.
——————–
VoltDB Co-founder and Chief Strategy Officer Scott Jarr. Scott brings more than 20 years of experience building, launching and growing technology companies from inception to market leadership in highly competitive environments.
Prior to joining VoltDB, Scott was VP Product Management and Marketing at on-line backup SaaS leader LiveVault Corporation. While at LiveVault, Scott was key in growing the recurring revenue business to 2,000 customers strong, leading to an acquisition by Iron Mountain. Scott has also served as board member and advisor to other early-stage companies in the search, mobile, security, storage and virtualization markets. Scott has an undergraduate degree in mathematical programming from the University of Tampa and an MBA from the University of South Florida.
Related Posts
– On Big Data, Analytics and Hadoop. Interview with Daniel Abadi. on December 5, 2012
– Two cons against NoSQL. Part II. on November 21, 2012
– Two Cons against NoSQL. Part I. on October 30, 2012
– Interview with Mike Stonebraker. on May 2, 2012
Resources
– ODBMS.org: NewSQL.
Blog Posts | Free Software | Articles and Presentations | Lecture Notes | Tutorials| Journals |
– Big Data: Challenges and Opportunities.
Roberto V. Zicari, October 5, 2012.
Abstract: In this presentation I review three current aspects related to Big Data:
1. The business perspective, 2. The Technology perspective, and 3. Big Data for social good.
Presentation (89 pages) | Intermediate| English | DOWNLOAD (PDF)| October 2012|
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You can follow ODBMS.org on Twitter : @odbmsorg.
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” We will observe at LEAST 1,000,000,000 celestial objects. If we launched today we would cope with difficulty – but we are on track to be ready by September when we actually launch. This is a game changer for astronomy thus very challenging for us, but we have done many large scale tests to gain confidence in our ability to process the complex and voluminous data arriving on ground and turn it into catalogues. I still feel the galaxy has plenty of scope to throw us an unexpected curve ball though and really challenge us in the data processing.” — William O`Mullane.
The Gaia mission is considered by the experts “the biggest data processing challenge to date in astronomy”. I recall here the Objectives and the Mission of the Gaia Project (source ESA Web site):
OBJECTIVES:
“To create the largest and most precise three dimensional chart of our Galaxy by providing unprecedented positional and radial velocity measurements for about one billion stars in our Galaxy and throughout the Local Group.”
THE MISSION:
“Gaia is an ambitious mission to chart a three-dimensional map of our Galaxy, the Milky Way, in the process revealing the composition, formation and evolution of the Galaxy. Gaia will provide unprecedented positional and radial velocity measurements with the accuracies needed to produce a stereoscopic and kinematic census of about one billion stars in our Galaxy and throughout the Local Group. This amounts to about 1 per cent of the Galactic stellar population. Combined with astrophysical information for each star, provided by on-board multi-colour photometry, these data will have the precision necessary to quantify the early formation, and subsequent dynamical, chemical and star formation evolution of the Milky Way Galaxy.
Additional scientific products include detection and orbital classification of tens of thousands of extra-solar planetary systems, a comprehensive survey of objects ranging from huge numbers of minor bodies in our Solar System, through galaxies in the nearby Universe, to some 500 000 distant quasars. It will also provide a number of stringent new tests of general relativity and cosmology.”
Last year in February, I have interviewed William O`Mullane, Science Operations Development Manager, at the European Space Agency, and Vik Nagjee, Product Manager, Core Technologies, at InterSystems Corporation, both deeply involved with the initial Proof-of-Concept of the data management part of the project.
A year later, I have asked William O`Mullane (European Space Agency), and Jose Ruperez (Intersystems Spain), some follow up questions.
RVZ
Q1. The original goal of the Gaia mission was to “observe around 1,000,000,000 celestial objects”. Is this still true? Are you ready for that?
William O’Mullane: YES ! We will have a Ground Segment Readiness Review next Spring and a Flight Acceptance Review before summer. We will observe at LEAST 1,000,000,000 celestial objects. If we launched today we would cope with difficulty – but we are on track to be ready by September when we actually launch. This is a game changer for astronomy thus very challenging for us, but we have done many large scale tests to gain confidence in our ability to process the complex and voluminous data arriving on ground and turn it into catalogues. I still feel the galaxy has plenty of scope to throw us an unexpected curve ball though and really challenge us in the data processing.
Q2. The plan was to launch the Gaia satellite in early 2013. Is this plan confirmed?
William O’Mullane: Currently September 2013 in Q1 is the official launch date.
Q3. Did the data requirements for the project change in the last year? If yes, how?
William O’Mullane: Downlink rate has not changed so we know how much comes into the System still only about 100TB over 5 years. Data processing volumes depend on how many intermediate steps we keep in different locations. Not much change there since last year.
Q4. The sheer volume of data that is expected to be captured by the Gaia satellite poses a technical challenge. What work has been done in the last year to prepare for such a challenge? What did you learn from the Proof-of-Concept of the data management part of this project?
William O’Mullane: I suppose we learned the same lessons as other projects. We have multiple processing centres with different needs met by different systems. We did not try for a single unified approach across these centers.
The CNES have gone completely to Hadoop for their processing. At ESAC we are going to InterSystems Caché. Last year only AGIS was on Caché – now the main daily processing chain is in Caché also [Edit: see also Q.9 ]. There was a significant boost in performance here but it must be said some of this was probably internal to the system, in moving it we looked at some bottlenecks more closely.
Jose Ruperez: We are very pleased to know that last year was only AGIS and now they have several other databases in Caché.
William O’Mullane: The second operations rehearsal is just drawing to a close. This was run completely on Caché (the first rehearsal used Oracle). There were of course some minor problems (also with our software) but in general from Caché perspective it went well.
Q5. Could you please give us some numbers related to performance? Could you also tells us what bottlenecks did you look at, and how did you avoid them?
William O’Mullane: Would take me time to dig out numbers .. we got factor 10 in some places with combination of better queries and removing some code bottle necks. We seem to regularly see factor 10 on “non optimized” systems.
Q6. Is it technically possible to interchange data between Hadoop and Caché ? Does it make sense for the project?
Jose Ruperez: The raw data downloaded from the satellite link every day can be loaded in any database in general. ESAC has chosen InterSystems Caché for performance reasons, but not only. William also explains how cost-effectiveness as well as the support from InterSystems were key. Other centers can try and use other products.
William O’Mullane:
This is a valid point – support is a major reason for our using Caché. InterSystems work with us very well and respond to needs quickly. InterSystems certainly have a very developer oriented culture which matches our team well.
Hadoop is one thing HDFS is another .. but of course they go together. In many ways our DataTrain Whiteboard do “map reduce” with some improvements for our specific problem. There are Hadoop database interfaces so it could work with Caché.
Q7. Could you tell us a bit more what did you learn so far with this project? In particular, what is the implication for Caché, now that also the the main daily processing chain is stored in Caché?
Jose Ruperez: A relevant number, regarding InterSystems Caché performance, is to be able to insert over 100,000 records per second sustained over several days. This also means that InterSystems Caché, in turn, has to write hundreds of MegaBytes per second to disk. To me, this is still mind-boggling.
William O’Mullane:
And done with fairly standard NetApp Storage. Caché and NetApp engineers sat together here at ESAC to align the configuration of both systems to get the max IO for Java through Caché to NetApp. There were several low level page size settings etc. which were modified for this.
Q8. What else still need to be done?
William O’Mullane: Well we have most of the parts but it is not a well oiled machine yet. We need more robustness and a little more automation across the board.
Q9. Your high level architecture a year ago consisted of two databases, a so called Main Database and an AGIS Database.
The Main Database was supposed to hold all data from Gaia and the products of processing. (This was expected to grow from a few TBs to few hundreds of TBs during the mission). AGIS was only required a subset of this data for analytics purpose. Could you tell us how the architecture has evolved in the last year?
William O’Mullane: This remains precisely the same.
Q10. For the AGIS database, were you able to generate realistic data and load on the system?
William O’Mullane: We have run large scale AGIS tests with 50,000,000 sources or about 4,500,000,000 simulated observations. This worked rather nicely and well within requirements. We confirmed going from 2 to 10 to 50 million sources that the problem scales as expected. The final (end of mission 2018) requirement is for 100,000,000 sources, so for now we are quite confident with the load characteristics. The simulation had a realistic source distribution in magnitude and coordinates (i.e. real sky like inhomogeneities are seen).
Q11. What results did you obtain in tuning and configuring the AGIS system in order to meet the strict insert requirements, while still optimizing sufficiently for down-stream querying of the data?
William O’Mullane: We still have bottlenecks in the update servers but the 50 million test still ran inside one month on a small in house cluster. So the 100 million in 3 months (system requirement) will be easily met especially with new hardware.
Q12. What are the next steps planned for the Gaia mission and what are the main technical challenges ahead?
William O’Mullane: AGIS is the critical piece of Gaia software for astrometry but before that the daily data treatment must be run. This so called Initial Data Treatment (IDT) is our main focus right now. It must be robust and smoothly operating for the mission and able to cope with non nominal situations occurring in commissioning the instrument. So some months of consolidation, bug fixing and operational rehearsals for us. The future challenge I expect not to be technical but rather when we see the real data and it is not exactly as we expect/hope it will be.
I may be pleasantly surprised of course. Ask me next year …
———–
William O`Mullane, Science Operations Development Manager, European Space Agency.
William O’Mullane has a PhD in Physics and a background in Computer Science and has worked on space science projects since 1996 when he assisted with the production of the Hipparcos CDROMS. During this period he was also involved with the Planck and Integral science ground segments as well as contemplating the Gaia data processing problem. From 2000-2005 Wil worked on developing the US National Virtual Observatory (NVO) and on the Sloan Digital Sky Survey (SDSS) in Baltimore, USA. In August 2005 he rejoined the European Space Agency as Gaia Science Operations Development Manager to lead the ESAC development effort for the Gaia Data Processing and Analysis Consortium.
José Rupérez, Senior Engineer at InterSystems.
He has been providing technical advise to customers and partners in Spain and Portugal for the last 10 years. In particular, he has been working with the European Space Agency since December 2008. Before InterSystems, José developed his career at eSkye Solutions and A.T. Kearney in the United States, always in Software. He started his career working for Alcatel in 1994 as a Software Engineer. José holds a Bachelor of Science in Physics from Universidad Complutense (Madrid, Spain) and a Master of Science in Computer Science from Ball State University (Indiana, USA). He has also attended courses at the MIT Sloan School of Business.
—————-
Related Posts
– Objects in Space -The biggest data processing challenge to date in astronomy: The Gaia mission. February 14, 2011
– Objects in Space: “Herschel” the largest telescope ever flown. March 18, 2011
– Objects in Space vs. Friends in Facebook. April 13, 2011
Resources
– Gaia Web page at ESA Spacecraft Operations.
– ESA’s web site for the Gaia scientific community.
– “Implementing the Gaia Astrometric Solution”, William O’Mullane, PhD Thesis, 2012
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You can follow ODBMS.org on Twitter : @odbmsorg.
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“Given the recent explosion of NoSQL data stores, we saw the need for a common data access abstraction to simplify development with NoSQL stores. Hence the Spring Data team was created.” –David Turanski.
I wanted to know more about the Spring Data project. I have interviewed David Turanski, Senior Software Engineer with SpringSource, a division of VMWare.
RVZ
Q1. What is the Spring Framework?
David Turanski: Spring is a widely adopted open source application development framework for enterprise Java‚ used by millions of developers. Version 1.0 was released in 2004 as a lightweight alternative to Enterprise Java Beans (EJB). Since, then Spring has expanded into many other areas of enterprise development, such as enterprise integration (Spring Integration), batch processing (Spring Batch), web development (Spring MVC, Spring Webflow), security (Spring Security). Spring continues to push the envelope for mobile applications (Spring Mobile), social media (Spring Social), rich web applications (Spring MVC, s2js Javascript libraries), and NoSQL data access(Spring Data).
Q2. In how many open source Spring projects is VMware actively contributing?
David Turanski: It’s difficult to give an exact number. Spring is very modular by design, so if you look at the SpringSource page on github, there are literally dozens of projects. I would estimate there are about 20 Spring projects actively supported by VMware.
Q3. What is the Spring Data project?
David Turanski: The Spring Data project started in 2010, when Rod Johnson (Spring Framework’s inventor), and Emil Eifrem (founder of Neo Technologies) were trying to integrate Spring with the Neo4j graph database. Spring has always provided excellent support for working with RDBMS and ORM frameworks such as Hibernate. However, given the recent explosion of NoSQL data stores, we saw the need for a common data access abstraction to simplify development with NoSQL stores. Hence the Spring Data team was created with the mission to:
“…provide a familiar and consistent Spring-based programming model for NoSQL and relational stores while retaining store-specific features and capabilities.”
The last bit is significant. It means we don’t take a least common denominator approach. We want to expose a full set of capabilities whether it’s JPA/Hibernate, MongoDB, Neo4j, Redis, Hadoop, GemFire, etc.
Q4. Could you give us an example on how you build Spring-powered applications that use NOSQL data stores (e.g. Redis, MongoDB, Neo4j, HBase)
David Turanski: Spring Data provides an abstraction for the Repository pattern for data access. A Repository is akin to a Data Access Object and provides an interface for managing persistent objects. This includes the standard CRUD operations, but also includes domain specific query operations. For example, if you have a Person object:
Person {
int id;
int age;
String firstName;
String lastName;
}
You may want to perform queries such as findByFirstNameAndLastName, findByLastNameStartsWith, findByFirstNameContains, findByAgeLessThan, etc. Traditionally, you would have to write code to implement each of these methods. With Spring Data, you simply declare a Java interface to define the operations you need. Using method naming conventions, as illustrated above, Spring Data generates a dynamic proxy to implement the interface on top of whatever data store is configured for the application. The Repository interface in this case looks like:
public interface PersonRepository extends CrudRepository{ Person findByFirstNameAndLastName(String firstName, String lastName); Person findByLastNameStartsWith(String lastName); Persion findByAgeLessThan(int age); ... }
In addition, Spring Data Repositories provide declarative support for pagination and sorting.
Then, using Spring’s dependency injection capabilities, you simply wire the repository into your application. For example:
public class PersonApp {
@Autowired
PersonRepository personRepository;
public Person findPerson(String lastName, String firstName) {
return personRepository.findByFirstNameAndLastName(firstName, lastName);
}
}
Essentially, you don’t have to write any data access code! However, you must provide Java annotations on your domain class to configure entity mapping to the data store. For example, if using MongoDB you would associate the domain class to a document:
@Document
Person {
int id;
int age;
String firstName;
String lastName;
}
Note that the entity mapping annotations are store-specific. Also, you need to provide some Spring configuration to tell your application how to connect to the data store, in which package(s) to search for Repository interfaces and the like.
The Spring Data team has written an excellent book, including lots of code examples. Spring Data Modern Data Acces for Enterprise Java recently published by O’Reilly. Also, the project web site includes many resources to help you get started using Spring Data.
Q5 And for map-reduce frameworks?
David Turanski: Spring Data provides excellent support for developing applications with Apache Hadoop along with Pig and/or Hive. However, Hadoop applications typically involve a complex data pipeline which may include loading data from multiple sources, pre-procesing and real-time analysis while loading data into HDFS, data cleansing, implementing a workflow to coordinate several data analysis steps, and finally publishing data from HDFS to on or more application data relational or NoSQL data stores.
The complete pipeline can be implemented using Spring for Apache Hadoop along with Spring Integration and Spring Batch. However, Hadoop has its own set of challenges which the Spring for Apache Hadoop project is designed to address. Like all Spring projects, it leverages the Spring Framework to provide a consistent structure and simplify writing Hadoop applications. For example, Hadoop applications rely heavily on command shell tools. So applications end up being a hodge-podge of Perl, Python, Ruby, and bash scripts. Spring for Apache Hadoop, provides a dedicated XML namespace for configuring Hadoop jobs with embedded scripting features and support for Hive and Pig. In addition, Spring for Apache Hadoop allows you to take advantage of core Spring Framework features such as task scheduling, Quartz integration, and property placeholders to reduce lines of code, improve testability and maintainability, and simplify the development proces.
Q6. What about cloud based data services? and support for relational database technologies or object-relational mappers?
David Turanski: While there are currently no plans to support cloud based services such as Amazon 3S, Spring Data provides a flexible architecture upon which these may be implemented. Relational technologies and ORM are supported via Spring Data JPA. Spring has always provided first class support for Relation database via the JdbcTemplate using a vendor provided JDBC driver. For ORM, Spring supports Hibernate, any JPA provider, and Ibatis. Additionally, Spring provides excellent support for declarative transactions.
With Spring Data, things get even easier. In a traditional Spring application backed by JDBC, you are required to hand code the Repositories or Data Access Objects. With Spring Data JPA, the data access layer is generated by the framework while persistent objects use standard JPA annotations.
Q7. How can use Spring to perform:
– Data ingestion from various data sources into Hadoop,
– Orchestrating Hadoop based analysis workflow,
– Exporting data out of Hadoop into relational and non-relational databases
David Turanski: As previously mentioned, a complete big data processing pipeline involving all of these steps will require Spring for Apache Hadoop in conjunction with Spring Integration and Spring Batch.
Spring Integration greatly simplifies enterprise integration tasks by providing a light weight messaging framework, based on the well known Patterns of Enterprise Integration by Hohpe and Woolf. Sometimes referred to as the “anti ESB”, Spring Integration requires no runtime component other than a Spring container and is embedded in your application process to handle data ingestion from various distributed sources, mediation, transformation, and data distribution.
Spring Batch provides a robust framework for any type of batch processing and is be used to configure and execute scheduled jobs composed of the coarse-grained processing steps. Individual steps may be implemented as Spring Integration message flows or Hadoop jobs.
Q8. What is the Spring Data GemFire project?
David Turanski: Spring Data GemFire began life as a separate project from Spring Data following VMWare’s acquisition of GemStone and it’s commercial GemFire distributed data grid.
Initially, it’s aim was to simplify the development of GemFire applications and the configuration of GemFire caches, data regions, and related components. While this was, and still is, developed independently as an open source Spring project, the GemFire product team recognized the value to its customers of developing with Spring and has increased its commitment to Spring Data GemFire. As of the recent GemFire 7.0 release, Spring Data GemFire is being promoted as the recommended way to develop GemFire applications for Java. At the same time, the project was moved under the Spring Data umbrella. We implemented a GemFire Repository and will continue to provide first class support for GemFire.
Q9. Could you give a technical example on how do you simplify the development of building highly scalable applications?
David Turanski: GemFire is a fairly mature distributed, memory oriented data grid used to build highly scalable applications. As a consequence, there is inherent complexity involved in configuration of cache members and data stores known as regions (a region is roughly analogous to a table in a relational database). GemFire supports peer-to-peer and client-server topologies, and regions may be local, replicated, or partitioned. In addition, GemFire provides a number of advanced features for event processing, remote function execution, and so on.
Prior to Spring Data GemFire, GemFire configuration was done predominantly via its native XML support. This works well but is relatively limited in terms of flexibility. Today, configuration of core components can be done entirely in Spring, making simple things simple and complex things possible.
In a client-server scenario, an application developer may only be concerned with data access. In GemFire, a client application accesses data via a client cache and a client region which act as a proxies to provide access to the grid. Such components are easily configured with Spring and the application code is the same whether data is distributed across one hundred servers or cached locally. This fortunately allows developers to take advantage of Spring’s environment profiles to easily switch to a local cache and region suitable for unit integration tests which are self-contained and may run anwhere, including automated build environments. The cache resources are configured in Spring XML:
<beans>
</beans><beans profile="test">
<gfe:cache />
<gfe:local -region name="Person"/>
</beans>
<beans profile="default">
<context:property-placeholder location="cache.properties"/>
<gfe:client-cache/>
<gfe:client-region name="Person"/>
<gfe:pool>
<gfe:locator host="${locator.host}" port="${locator.port}"/>
</gfe:pool>
</beans>
</beans>
Here we see the deployed application (default profile) depends on a remote GemFire locator process. The client region does not store data locally by default but is connected to an available cache server via the locator. The region is distributed among the cache server and its peers and may be partitioned or replicated. The test profile sets up a self contained region in local memory, suitable for unit integration testing.
Additionally, applications may by further simplified by using a GemFire backed Spring Data Repository. The key difference from the example above is that the entity mapping annotations are replaced with GemFire specific annotations:
@Region
Person {
int id;
int age;
String firstName;
String lastName;
}
The @Region annotation maps the Person type to an existing region of the same name. The Region annotation provides an attribute to specify the name of the region if necessary.
Q10. The project uses GemFire as a distributed data management platform. Why using an In-Memory Data Management platform, and not a NoSQL or NewSQL data store?
David Turanski: Customers choose GemFire primarily for performance. As an in memory grid, data access can be an order of magnitude faster than disk based stores. Many disk based systems also cache data in memory to gain performance. However your mileage may vary depending on the specific operation and when disk I/O is needed. In Contrast, GemFire’s performance is very consistent. This is a major advantage for a certain class of high volume, low latency, distributed systems. Additionally, GemFire is extremely reliable, providing disk-based backup and recovery.
GemFire also builds in advanced features not commonly found in the NoSQL space. This includes a number of advanced tuning parameters to balance performance and reliability, synchronous or asynchronous replication, advanced object serialization features, flexible data partitioning with configurable data colocation, WAN gateway support, continuous queries, .Net interoperability, and remote function execution.
Q11. Is GemFire a full fledged distributed database management system? or else?
David Turanski: Given all its capabilities and proven track record supporting many mission critical systems, I would certainly characterize GemFire as such.
———————————-
David Turanski is a Senior Software Engineer with SpringSource, a division of VMWare. David is a member of the Spring Data team and lead of the Spring Data GemFire project. He is also a committer on the Spring Integration project. David has extensive experience as a developer, architect and consultant serving a variety of industries. In addition he has trained hundreds of developers how to use the Spring Framework effectively.
Related Posts
– On Big Data, Analytics and Hadoop. Interview with Daniel Abadi. December 5, 2012
– Two cons against NoSQL. Part II. November 21, 2012
– Two Cons against NoSQL. Part I. October 30, 2012
– Interview with Mike Stonebraker. May 2, 2012
Resources
– ODBMS.org Lecture Notes: Data Management in the Cloud.
Michael Grossniklaus, David Maier, Portland State University.
Course Description: “Cloud computing has recently seen a lot of attention from research and industry for applications that can be parallelized on shared-nothing architectures and have a need for elastic scalability. As a consequence, new data management requirements have emerged with multiple solutions to address them. This course will look at the principles behind data management in the cloud as well as discuss actual cloud data management systems that are currently in use or being developed. The topics covered in the courserange from novel data processing paradigms (MapReduce, Scope, DryadLINQ), to commercial cloud data management platforms (Google BigTable, Microsoft Azure, Amazon S3 and Dynamo, Yahoo PNUTS) and open-source NoSQL databases (Cassandra, MongoDB, Neo4J). The world of cloud data management is currently very diverse and heterogeneous. Therefore, our course will also report on efforts to classify, compare and benchmark the various approaches and systems. Students in this course will gain broad knowledge about the current state of the art in cloud data management and, through a course project, practical experience with a specific system.”
Lecture Notes | Intermediate/Advanced | English | DOWNLOAD ~280 slides (PDF)| 2011-12|
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“Those who claim they can give you both ACID transactions and linearly scalability at the same time are not telling the truth because it is theoretically proven impossible” –Asa Holmstrom.
I heard about a start up called Starcounter. I wanted to know more. I have interviewed the CEO of the company Asa Holmstrom.
RVZ
Q1. You just launched Starcounter 2.0 public beta. What is it? and who can already use it?
Asa Holmstrom: Starcounter is a high performance in-memory OLTP database. We have partners who built applications on top of Starcounter, e.g. AdServe application, retail application. Today Starcounter has 60+ customers using Starcounter in production.
Q2. You define Starcounter as “memory centric”, using a technique you call “VMDBMS”. What is special about VMDBMS?
Asa Holmstrom: VMDBMS integrates the application runtime virtual machine (VM) with the database management system (DBMS). Data only residees in one single place all the time in RAM, no data is transferred back and forth between the database memory and the temporary storage (object heap) of the application. The VMDBMS makes Starcounter significantly faster than other in-memory databases.
Q3. When you say “the VMDBMS makes Starcounter significantly faster than other in-memory databases”, could you please give some specific benchmarking numbers? Which other in-memory databases did you compare with your benchmark?
Asa Holmstrom: In general we are 100 times faster than any other RDBMS, 10 times comes from being IMDBMS, 10 times comes from VMDBMS.
Q4. How do you handle situations when data in RAM is no more available due to hardware failures?
Asa Holmstrom: In Starcounter the data is just as secure as in any disk-centric database. Image files and transaction log are stored on disk, and before a transaction is regarded committed it has been written to the transaction log.
When restarting Starcounter after a crash, a recovery of the database will automatically be done. To guarantee high availability we recommend our customers to have a hot stand-by machine which subscribes on the transaction log.
Q5. Goetz Graefe, HP fellow, commented in an interview (ref.1) that “disks will continue to have a role and economic value where the database also contains history, including cold history such as transactions that affected the account balances, login & logout events, click streams eventually leading to shopping carts, etc.” What is your take on this?
Asa Holmstrom: As we have hardware limitations on RAM databases in practice about 2TB, therefore there will still be a need for database storage on disk.
Q6. You claim to achieve high performance and consistent data. Do you have any benchmark results to sustain such a claim?
Asa Holmstrom: Yes, we have made internal benchmark tests to compare performance while keeping data consistent.
Q7: Do you have some results of your benchmark tests publically available? If yes, could you please summarize here the main results?
Asa Holmstrom: As TPC tests are not applicable to us, we have done some internal tests. We can’t share them with you.
Q8. What kind of consistency do you implement?
Asa Holmstrom: We support true ACID consistency, implemented using snapshot isolation and fake writes, in a similar way as Oracle.
Q9. How do you achieve scalability?
Asa Holmstrom: ACID transactions are not scalable. All parallel ACID transactions need to be synchronized and the closer the transactions are executed in space, the faster the synchronization becomes. Therefore you get best performance by executing all ACID transactions on one machine. We call it to scale in. When it comes to storage, you scale up a Starcounter database by adding more RAM.
Q10: For which class of applications it is realistic to expect to execute all ACID transactions on one machine?
Asa Holmstrom: For all applications when you want high transactional throughput. When you have parallell ACID transactions you need to synchronize these transactions, and this synchorinization becomes harder when you scale out to several different machines. The benefits of scaling out grows linearly with the number of machines, but the cost of synchronization grows quadratically. Consequently you do not gain anything by scaling out. In fact, you get better total transactional throughput by running all transaction in RAM on one machine, which we call to “scale in”. No other databas can give you the same total ACID transactional throughput as Starcounter. Those who claim they can give you both ACID transactions and linearly scalability at the same time are not telling the truth because it is theoretically proven impossible. Databases which can give you ACID transaction or linearly scalablity
cannot give you both these things at the same time.
Q11. How do you define queries and updates?
Asa Holmstrom: We distinguish between read-only transactions and read-write transactions. You can only write (insert/update) database data using a read-write transaction.
Q12. Are you able to handle Big Data analytics with your system?
Asa Holmstrom: Starcounter is optimized for transactional processing, not for analytical processing.
Q13. How does Starcounter differs from other in-memory databases, such as for example SAP HANA, and McObject?
Asa Holmstrom: In general the primary differentiator between Starcounter and any other in-memory database is the VMDMBS. SAP HANA has primarily an OLAP focus.
Q14. From a user perspective, what is the value proposition of having a VMDMBS as a database engine?
Asa Holmstrom: Uncompetable ACID transactional performance.
Q15. How do you differentiate with respect to VoltDB?
Asa Holmstrom: Better ACID transactional performance. VoltDB gives you either ACID transactions (on one machine) or the possibility to scale out without any guarantees of global database consistency (no ACID). Starcounter has a native .Net object interface which makes it easy to use from any .Net language.
Q16. Is Starcounter 2.0 open source? If not, do you have any plan to make it open source?
Asa Holmstrom: We do not have any current plans of making Starcounter open source.
——————
CEO Asa Holmstrom brings to her role at Starcounter more than 20 years of executive leadership in the IT industry. Previously, she served as the President of Columbitech, where she successfully established its operations in the U.S. Prior to Colmbitech, Asa was CEO of Kvadrat, a technology consultancy firm. Asa also spent time as a management consultant, focusing on sales, business development and leadership within global technology companies such as Ericsson and Siemens. She holds a bachelor’s degree in mathematics and computer science from Stockholm University.
Related Posts
– Interview with Mike Stonebraker. by Roberto V. Zicari on May 2, 2012
Resources
– Cloud Data Stores – Lecture Notes: Data Management in the Cloud.
by Michael Grossniklaus, David Maier, Portland State University.
Course Description: “Cloud computing has recently seen a lot of attention from research and industry for applications that can be parallelized on shared-nothing architectures and have a need for elastic scalability. As a consequence, new data management requirements have emerged with multiple solutions to address them. This course will look at the principles behind data management in the cloud as well as discuss actual cloud data management systems that are currently in use or being developed.
The topics covered in the course range from novel data processing paradigms (MapReduce, Scope, DryadLINQ), to commercial cloud data management platforms (Google BigTable, Microsoft Azure, Amazon S3 and Dynamo, Yahoo PNUTS) and open-source NoSQL databases (Cassandra, MongoDB, Neo4J).
The world of cloud data management is currently very diverse and heterogeneous. Therefore, our course will also report on efforts to classify, compare and benchmark the various approaches and systems.
Students in this course will gain broad knowledge about the current state of the art in cloud data management and, through a course project, practical experience with a specific system.”
Lecture Notes | Intermediate/Advanced | English | DOWNLOAD ~280 slides (PDF)| 2011-12|
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What has coffee to do with managing data? Probably nothing, but at the same time how many developers do I know who drink coffee? Quite a lot.
I myself, decided years ago to reduce my intake of coffee, and I now drink mostly green tea, but not early in the morning, where I need an espresso to get started 🙂
So, once I found out that my database fellow colleague Alon Halevy, very well known for his work on data management, also wrote a (seemly successful) book on coffee, I had to ask him a few questions.
RVZ
Q1. How come did you end up writing a book on coffee?
Alon Halevy: It was a natural outcome of going to many conferences on database management. I realized that anywhere I go in the world, the first thing I look for are nice cafes in town. I also noticed that the coffee culture varies quite a bit from one city to another, and started wondering about how culture affects coffee and vice versa. I think it was during VLDB 2008 in Auckland, New Zealand, that I realized that I should investigate the topic in much more detail.
Q2. When did you start this project?
Alon Halevy: I was toying around with the idea for a while, but did very little. One day in March, 2009, recalling a conversation with a friend, I googled “barista competitions”. I found out that the US Barista Championship was taking place in Portland in a week’s time. I immediately bought a ticket to Portland and a week later I was immersed in world-class barista culture. From that point on, there was no turning back.
Q3. What is the book about?
Alon Halevy: In the book I go to over 25 countries on 6 continents and ask what is special about coffee culture in that country. I typically tell the story of the culture through a fascinating character or a unique cafe. I cover places such as the birthplace of coffee, Ethiopia, where I spend time in a farming community; in Iceland, where the coffee culture developed because of a bored spouse of an engineering Ph.D student at the University of Wisconsin, Madison; and Bosnia, where coffee pervades culture like nowhere else in the world. The book includes many photos and data visualizations (of course!) and makes you want to travel the world to experience some of these great places.
Q4. How did you find the time to visit the main locations you write in your book?
Alon Halevy: Many of the trips were, of course, piggybacked off a database conference or other speaking engagements. After a while, I was so determined to finish the book that I simply got on a plane and covered the regions that don’t attract database conferences (e.g., Central America, Ethiopia). Over time, I became very efficient when I arrived at a destination (and the rumor that I usually bring Google goodies with me started spreading through the coffee community). I developed a rich network of coffee friends, and everywhere I went I was taken directly to the most interesting places.
Q5. So what is the best coffee in the world :-)?
Alon Halevy: That is obviously very subjective, and depends more than anything on who you’re having the coffee with! In terms of sheer quality, I think some of the Scandinavian roasters are doing an amazing job. I was really impressed by the coffee culture in Melbourne, Australia. Japan and Korea were also unexpected highlights. But if you know where to go, you can find amazing coffee almost anywhere. Once you start sensing coffee in “high definition”, tasting the variety of flavors and notes in the brew, coffee becomes a fascinating journey.
Q6. Would you say that coffee is an important part of your work?
Alon Halevy: Yes, even putting aside the fact that no work gets done before coffee is consumed, the coffee community has given me an interesting perspective on computing. Coffee folks are enthusiastic users of technology, as they are a globally distributed community that is constantly on the move (and have a lot to say thanks to their elevated caffeine levels). It is also interesting to think of how technology, and data management techniques in particular, can be used to help this global industry. I hope to investigate these issues more in the future (I’m already being tapped by the coffee community for various database advice often).
Q7. Coffee can be bad, if you drink it in excess. Do you cover this aspect in your book? (I assume not), but what is your take on this?
Alon Halevy: There is a lot of debate on the benefits of coffee in the scientific literature. If you consume coffee moderation (2-3 cups a day), you’re fine (unless you have some abnormal condition). But remember, I am a Doctor of Computer Science, not Medicine.
—————–
The Infinite Emotions of Coffee, by Alon Halevy.
You can download a sample chapter of the book: “COFFEE: A Competitive Sport” (Link to download .pdf)
Details on where to get the book: the book’s page.
(The digital version is available on iTunes, Kindle and the Google bookstore) .
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“Some people even think that “Hadoop” and “Big Data” are synonymous (though this is an over-characterization). Unfortunately, Hadoop was designed based on a paper by Google in 2004 which was focused on use cases involving unstructured data (e.g. extracting words and phrases from Webpages in order to create Google’s Web index). Since it was not originally designed to leverage the structure in relational data in order to take short-cuts in query processing, its performance for processing relational data is therefore suboptimal.”— Daniel Abadi.
On the subject of Big Data, Analytics and Hadoop I have Interviewed Daniel Abadi, associate professor of computer science at Yale University and Chief Scientist and Co-founder of Hadapt.
RVZ
Q1. On the subject of “eventual consistency”, Justin Sheehy of Basho recently said in an interview (1): “I would most certainly include updates to my bank account as applications for which eventual consistency is a good design choice. In fact, bankers have understood and used eventual consistency for far longer than there have been computers in the modern sense”.
What is your opinion on this? Would you wish to have an “eventual consistency” update to your bank account?
Daniel Abadi: It’s definitely true that it’s a common misconception that banks use the ACID features of database systems to perform certain banking transactions (e.g. a transfer from one customer to another customer) in a single atomic transaction. In fact, they use complicated workflows that leverage semantics that reasonably approximate eventual consistency.
Q2. Justin Sheely also said in the same interview that “the use of eventual consistency in well-designed systems does not lead to inconsistency.” How do you ensure that a system is well designed then?
Daniel Abadi: That’s exactly the problem with eventual consistency. If your database only guarantees eventual consistency, you have to make sure your application is well-designed to resolve all consistency conflicts.
For example, eventually consistent systems allow you to update two different replicas to two different values simultaneously. When these updates propagate to the each other, you get a conflict. Application code has to be smart enough to deal with any possible kind of conflict, and resolve them correctly. Sometimes simple policies like “last update wins” is sufficient to handle all cases.
But other applications are far more complicated, and using an eventually consistent database makes the application code far more complex. Often this complexity can lead to errors and security flaws, such as the ATM heist that was written up recently. Having a database that can make stronger guarantees greatly simplifies application design.
Q3. You have created a start up called Hadapt which claims to be the ” first platform to combine Apache Hadoop and relational DBMS technologies”. What is it? Why combining Hadoop with Relational database technologies?
Daniel Abadi: Hadoop is becoming the standard platform for doing large scale processing of data in the enterprise. It’s rate of growth far exceeds any other “Big Data” processing platform. Some people even think that “Hadoop” and “Big Data” are synonymous (though this is an over-characterization). Unfortunately, Hadoop was designed based on a paper by Google in 2004 which was focused on use cases involving unstructured data (e.g. extracting words and phrases from Webpages in order to create Google’s Web index).
Since it was not originally designed to leverage the structure in relational data in order to take short-cuts in query processing, its performance for processing relational data is therefore suboptimal.
At Hadapt, we’re bringing 3 decades of relational database research to Hadoop. We have added features like indexing, co-partitioned joins, broadcast joins, and SQL access (with interactive query response times) to Hadoop, in order to both accelerate its performance for queries over relational data and also provide an interface that third party data processing and business intelligence tools are familiar with.
Therefore we have taken Hadoop, which used to be just a tool for super-smart data scientists, and brought it to the mainstream by providing a high performance SQL interface that business analysts and data analysis tools already know how to use. However, we’ve gone a step further and made it possible to include both relational data and non-relational data in the same query; so what we’ve got now is a platform that people can use to do really new and innovative types of analytics involving both unstructured data like tweets or blog posts and structured data such as traditional transactional data that usually sits in relational databases.
Q4. What is special about tha Hadapt architecture that makes it different from other Hadoop-based products?
Daniel Abadi: The prevalent architecture that people use to analyze structured and unstructured data is a two-system configuration, where Hadoop is used for processing the unstructured data and a relational database system is used for the structured data. However, this is a highly undesirable architecture, since now you have two systems to maintain, two systems where data may be stored, and if you want to do analysis involving data in both systems, you end up having to send data over the network which can be a major bottleneck. What is special about the Hadapt architecture is that we are bringing database technology to Hadoop, so that Hadapt customers only need to deploy a single cluster — a normal Hadoop cluster — that is optimized for both structured and unstructured data, and is capable of pushing the envelope on the type of analytics that can be run over Big Data.
Q5. You claim that Hadapt SQL queries are an order of magnitude faster than Hadoop+Hive? Do you have any evidence for that? How significant is this comparison for an enterprise?
Daniel Abadi: We ran some experiments in my lab at Yale using the queries from the TPC-H benchmark and compared the technology behind Hadapt to both Hive and also traditional database systems.
These experiments were peer-reviewed and published in SIGMOD 2011. You can see the full 12 page paper here. From my point of view, query performance is certainly important for the enterprise, but not as important as the new type of analytics that our platform opens up, and also the enterprise features around availability, security, and SQL compliance that distinguishes our platform from Hive.
Q6. You say that “Hadoop-connected SQL databases” do not eliminate “silos”. What do you mean with silos? And what does it mean in practice to have silos for an enterprise?
Daniel Abadi: A lot of people are using Hadoop as a sort of data refinery. Data starts off unstructured, and Hadoop jobs are run to clean, transform, and structure the data. Once the data is structured, it is shipped to SQL databases where it can be subsequently analyzed. This leads to the raw data being left in Hadoop and the refined data in the SQL databases. But it’s basically the same data — one is just a cleaned (and potentially aggregated) version of the other. Having multiple copies of the data can lead to all kinds of problems. For example, let’s say you want to update the data in one of the two locations — it does not get automatically propagated to the copy in the other silo. Furthermore, let’s say you are doing some analysis in the SQL database and you see something interesting and want to drill down to the raw data — if the raw data is located on a different system, such a drill down becomes highly nontrivial. Furthermore, data provenance is a total nightmare. It’s just a really ugly architecture to have these two systems with a connector between them.
Q7. What is “multi-structured” data analytics?
Daniel Abadi: I would define it as analytics than include both structured data and unstructured data in the same “query” or “job”. For example, Hadapt enables keyword search, advanced analytic functions, and common machine learning algorithms over unstructured data, all of which can be invoked from a SQL query that includes data from relational tables stored inside the same Hadoop cluster. A great example of how this can be used in real life can be seen in the demo given by Data Scientist Mingsheng Hong.
Q8. On the subject of Big Data Analytics, Werner Vogels of Amazon.com said in an interview [2] that “one of the core concepts of Big Data is being able to evolve analytics over time. In the new world of data analysis your questions are going to evolve and change over time and as such you need to be able to collect, store and analyze data without being constrained by resources.”
What is your opinion on this? How Hadapt can help on this?
Daniel Abadi: Yes, I agree. This is precisely why you need a system like Hadapt that has extreme flexibility in terms of the types of data that it can analyze (see above) and types of analysis that can be performed.
Q9. With your research group at Yale you are developing a new data management prototype system called Calvin. What is it? What is special about it?
Daniel Abadi: Calvin is a system designed and built by a large team of researchers in my lab at Yale (Alex Thomson, Thaddeus Diamond, Shu-Chun Weng, Kun Ren, Philip Shao, Anton Petrov, Michael Giuffrida, and Aaron Segal) that scales transactional processing in database systems to millions of transactions per second while still maintaining ACID guarantees.
If you look at what is available in the industry today if you want to scale your transactional workload, you have all these NoSQL systems like HBase or Cassandra or Riak — all of which have to eliminate ACID guarantees in order to achieve scalability. On the other hand, you have NewSQL systems like VoltDB or various sharded MySQL implementations that maintain ACID but can only scale when transactions rarely cross physical server boundaries. Calvin is unique in the way that it uses a deterministic execution model to eliminate the need for commit protocols (such as two phase commit) and allow arbitrary transactions (that can include data on multiple different physical servers) to scale while still maintaining the full set of ACID guarantees. It’s a cool project. I recommend your readers check out the most recent research paper describing the system.
Q10. Looking at three elements: Data, Platform, Analysis, what are the main research challenges ahead?
Daniel Abadi: Here are a few that I think are interesting:
(1) Scalability of non-SQL analytics. How do you parallelize clustering, classification, statistical, and algebraic functions that are not “embarrassingly parallel” (that have traditionally been performed on a single server in main memory) over a large cluster of shared-nothing servers.
(2) Reducing the cognitive complexity of “Big Data” so that it can fit in the working set of the brain of a single analyst who is wrangling with the data.
(3) Incorporating graph data sets and graph algorithms into database management systems.
(4) Enabling platform support for probabilistic data and probabilistic query processing.
———————-
Daniel Abadi is an associate professor of computer science at Yale University where his research focuses on database system architecture and implementation, and cloud computing. He received his PhD from MIT, where his dissertation on column-store database systems led to the founding of Vertica (which was eventually acquired by Hewlett Packard).
His research lab at Yale has produced several scalable database systems, the best known of which is HadoopDB, which was subsequently commercialized by Hadapt, where Professor Abadi serves as Chief Scientist. He is a recipient of a Churchill Scholarship, an NSF CAREER Award, a Sloan Research Fellowship, the 2008 SIGMOD Jim Gray Doctoral Dissertation Award, and the 2007 VLDB best paper award.
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Related Posts
[1] On Eventual Consistency- An interview with Justin Sheehy. by Roberto V. Zicari on August 15, 2012
Resources
– Efficient Processing of Data Warehousing Queries in a Split Execution Environment (.pdf)
– Calvin: Fast Distributed Transactions for Partitioned Database Systems (.pdf)
– Hadoop++: Making a Yellow Elephant Run Like a Cheetah (Without It Even Noticing), 2010 (.pdf)
– MapReduce vs RDBMS [PPT]
– Big Data and Analytical Data Platforms:
Blog Posts | Free Software | Articles | PhD and Master Thesis|
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