NoSQL for the SQL Server Pro Lynn Langit Feb 2013 – SDC, Sweden

Is NoSQL just Hadoop? HUGE Hype factor over last few years http://hadoop.apache.org/ http://en.wikipedia.org/wiki/Apache_Hadoop Apache Hadoop is a software framework that supports data-intensive distributed applications under a free license enables applications to work with thousands of nodes and petabytes of data was inspired by Google's MapReduce and Google File System (GFS) papers

Hadoop in the Enterprise http://www.oracle.com/technetwork/bdc/hadoop-loader/overview/index.html http://www.microsoft.com/download/en/details.aspx?id=27584

Working with Hadoop Common Tools / Languages Java (JDK) / Eclipse MapReduce Map (query/format) Reduce (aggregate) plug-in for Eclipse (Java) Pig (ETL -- Java) Hive (HQL Query) HBase tables Others Mahout (analyze) Karmasphere (analyze) R (analyze) http://hortonworks.com/technology/hortonworksdataplatform/ More about Hbase, from the O’Reilly ‘Getting Ready for BigData’ report “Enter HBase, a column-oriented database that runs on top of HDFS. Modeled after Google’s BigTable, the project’s goal is to host billions of rows of data for rapid access. MapReduce can use HBase as both a source and a destination for its computations, and Hive and Pig can be used in combination with HBase. In order to grant random access to the data, HBase does impose a few restrictions: performance with Hive is 4-5 times slower than plain HDFS, and the maximum amount of data you can store is approximately a petabyte, versus HDFS’ limit of over 30PB.” http://www.cloudera.com/

Demo -HDInsight– Cluster Allocation https://www.hadooponazure.com/Account Demo - http://www.youtube.com/watch?v=ugi9C6s_sH4

What is the relationship? NoSQL BigData

BigData = Exponentially More Data Retail Example -> ‘Feedback Economy’ Number of transactions Number of behaviors (collected every minute) From the O’Reilly / Strata “Getting Ready for Big Data” Report… “the three Vs of volume, velocity and variety are commonly used to characterize different aspects of big data”

BigData = ‘Next State’ Questions What could happen? Why didn’t this happen? When will the next new thing happen? What will the next new thing be? What happens? Collecting Behavioral data

Demo - HDInsight - MapReduce https://www.hadooponazure.com/Account Demo - http://www.youtube.com/watch?v=XcHz8aUDDN8 and http://www.youtube.com/watch?v=c7oHntP8HBI

Hitting (Relational) Walls CA Highly-available consistency CP Enforced consistency AP Eventual consistency http://berb.github.com/diploma-thesis/original/061_challenge.html http://nosqltips.blogspot.com/2011/04/cap-diagram-for-distribution.html http://blog.mccrory.me/2010/11/03/cap-theorem-and-the-clouds/ ACID VS BASE ACID RDBMS are the predominant database systems currently in use for most applications, including web applications. Their data model and their internals are strongly connected to transactional behaviour when operating on data. However, transactional behaviour is not solely related to RDBMS, but is also used for other systems. A set of properties describes the guarantees that database transactions generally provide in order to maintain the validity of data [Hae83]. Atomicity This property determines that a transaction executes with a "all or nothing" manner. A transaction can either be a single operation, or a sequence of operations resp. sub-transactions. As a result, a transaction either succeeds and the database state is changed, or the database remains unchanged, and all operations are dismissed. Consistency In context of transactions, we define consistency as the transition from one valid state to another, never leaving behind an invalid state when executing transactions. Isolation The concept of isolation guarantees that no transaction sees premature operations of other running transactions. In essence, this prevents conflicts between concurrent transactions. Durability The durability property assures persistence of executed transactions. Once a transaction has committed, the outcome of the transaction such as state changes are kept, even in case of a crash or other failures. Strongly adhering to the principles of ACID results in an execution order that has the same effect as a purely serial execution. In other words, there is always a serially equivalent order of transactions that represents the exact same state [Dol05]. It is obvious that ensuring a serializable order negatively affects performance and concurrency, even when a single machine is used. In fact, some of the properties are often relaxed to a certain extent in order to improve performance. A weaker isolation level between transactions is the most used mechanism to speed up transactions and their throughput. Stepwise, a transactional system can leave serializablity and fall back to the weaker isolation levels repeatable reads, read committed and read uncommitted. These levels gradually remove range locks, read locks and resp. write locks (in that order). As a result, concurrent transactions are less isolated and can see partial results of other transactions, yielding so called read phenomena. Some implementations also weaken the durability property by not guaranteeing to write directly to disk. Instead, committed states are buffered in memory and eventually flushed to disk. This heavily decreases latencies at the cost of data integrity. Consistency is still a core property of the ACID model, that cannot be relaxed easily. The mutual dependencies of the properties make it impossible to remove a single property without affecting the others. Referring back to the CAP theorem, we have seen the trade-off between consistency and availability regarding distributed database systems that must tolerate partitions. In case we choose a database system that follows the ACID paradigm, we cannot guarantee high availability anymore. The usage of ACID as part of a distributed systems yields the need of distributed transactions or similar mechanisms for preserving the transactional properties when state is shared and sharded onto multiple nodes. Now let us reconsider what would happen if we evict the burden of distributed transactions. As we are talking about distributed systems, we have no global shared state by default. The only knowledge we have is a per-node knowledge of its own past. Having no global time, no global now, we cannot inherently have atomic operations on system level, as operations occur at different times on different machines. This softens isolation and we must leave the notion of global state for now. Having no immediate, global state of the system in turn endangers durability. In conclusion, building distributed systems adhering to the ACID paradigm is a demanding challenge. It requires complex coordination and synchronization efforts between all involved nodes, and generates considerable communication overhead within the entire system. It is not for nothing that distributed transactions are feared by many architects [Hel09,Alv11]. Although it is possible to build such systems, some of the original motivations for using a distributed database system have been mitigated on this path. Isolation and serializablity contradict scalability and concurrency. Therefore, we will now consider an alternative model for consistency that sacrifices consistency for other properties that are interesting for certain systems. BASE This alternative consistency model is basically the subsumption of properties resulting from a system that provides availability and partition tolerance, but no strong consistency [Pri08]. While a strong consistency model as provided by ACID implies that all subsequent reads after a write yield the new, updated state for all clients and on all nodes of the system, this is weakened for BASE. Instead, the weak consistency of BASE comes up with an inconsistency window, a period in which the propagation oft the update is not yet guaranteed. Basically available The availability of the system even in case of failures represents a strong feature of the BASE model. Soft state No strong consistency is provided and clients must accept stale state under certain circumstances. Eventually consistent Consistency is provided in a "best effort" manner, yielding a consistent state as soon as possible. The optimistic behaviour of BASE represents a best effort approach towards consistency, but is also said to be simpler and faster. Availability and scaling capacities are primary objectives at the expense of consistency. This has an impact on the application logic, as the developer must be aware of the possibility of stale data. On the other hand, favoring availability over consistency has also benefits for the application logic in some scenarios. For instance, a partial system failure of an ACID system might reject write operations, forcing the application to handle the data to be written somehow. A BASE system in turn might always accept writes, even in case of network failures, but they might not be visible for other nodes immediately. The applicability of relaxed consistency models depends very much on the application requirements. Strict constraints of balanced accounts for a banking application do not fit eventual consistency naturally. But many web applications built around social objects and user interactions can actually accept slightly stale data. When the inconsistency window is on average smaller than the time between request/response cycles of user interactions, a user might not even realize any kind of inconsistency at all. http://www.julianbrowne.com/article/viewer/brewers-cap-theorem

So many NoSQL options More than just the Elephant in the room Over 120+ types of NoSQL databases http://nosql-database.org/ http://hadoop.apache.org/ & http://www.mongodb.org/ Wikipedia - http://en.wikipedia.org/wiki/NoSQL List of noSQL databases – http://nosql-database.org/ The good, the bad - http://www.techrepublic.com/blog/10things/10-things-you-should-know-about-nosql-databases/1772

Flavors of NoSQL http://bigdatanerd.wordpress.com/2012/01/04/why-nosql-part-2-overview-of-data-modelrelational-nosql/ http://docs.jboss.org/hibernate/ogm/3.0/reference/en-US/html_single/

Key / Value Database Schema-less State (Persistent or Volatile) Examples AWS Dynamo DB Riak http://en.wikipedia.org/wiki/Project_Voldemort http://aws.amazon.com/ http://docs.amazonwebservices.com/amazondynamodb/latest/developerguide/Introduction.html http://www.allthingsdistributed.com/2012/01/amazon-dynamodb.html

Column Database Wide, sparse column sets Examples: Cassandra HBase BigTable GAE HR DS Azure Tables SQL 2012 Tabular Model http://stage.hypertable.com/index.php/documentation/architecture/ http://code.google.com/appengine/ http://code.google.com/appengine/articles/datastore/overview.html

More about Column Databases Type A Column-families Non-relational Sparse Examples: HBase, Cassandra, xVelocity (SQL 2012 Tabular) Type B Column-stores Relational Dense Example: SQL Server 2012 Columnstore index http://cwebbbi.wordpress.com/2012/02/14/so-what-is-the-bi-semantic-model/ http://www.databasejournal.com/features/mssql/understanding-new-column-store-index-of-sql-server-2012.html http://dbmsmusings.blogspot.com/2010/03/distinguishing-two-major-types-of_29.html http://ayende.com/blog/4500/that-no-sql-thing-column-family-databases

Demo - Document Database (Mongo DB) document-oriented (collection of JSON documents) w/semi structured data Encodings include BSON, JSON, XML… binary forms PDF, Microsoft Office documents -- Word, Excel…) http://en.wikipedia.org/wiki/MongoDB http://www.mongodb.org/downloads http://www.mongodb.org/display/DOCS/Drivers

Demo - Graph Database (Neo4j) a lot of many-to-many relationships recursive self-joins when your primary objective is quickly finding connections, patterns and relationships between the objects within lots of data http://www.infinitegraph.com/what-is-a-graph-database.html http://en.wikipedia.org/wiki/Graph_database http://www.freebase.com/

So which type of NoSQL? Back to CAP… Consistency Availability Partitioning CP = NoSQL/column Hadoop Big Table H-base MemCacheDB CA = SQL/RDBMS SQL Sever / Oracle MySQL http://amitpiplani.blogspot.com/2010/05/u-pick-2-selection-for-nosql-providers.html AP = NoSQL/document or key/value DynamoDB CouchDB Cassandra Voldemort

Which type of NoSQL for which type of data? Type of NoSQL solution Example Log files Wide Column HBase Product Catalogs Key Value on disk DynamoDB User profiles Key Value in memory Redis Startups Document MongoDB Social media connections Graph Neo4j LOB w/Transactions NONE! Use RDBMS SQL Server

Cloud-hosted NoSQL up to 50x CHEAPER http://lynnlangit.wordpress.com/2011/11/09/relational-cloud-storage-is-50x-more-expensive-than-nosql/

The reality…two pivots Storage Methods SQL (RDBMS) NoSQL Storage Locations On premises Cloud-hosted

NoSQL (Cloud) BLOB Storage Buckets Amazon – S3 or Glacier The gold standard Google – Cloud Storage Free for developers Microsoft Azure BLOBS DropBox, Box… http://code.google.com Access via REST APIs Very Cheap, but not much functionality included Lots of code to write for application development But…can be a good backup solution

Cloud-hosted RDBMS AWS RDS – SQL Server, mySQL, Oracle Google – mySQL Medium cost Solid feature set, i.e. backup, snapshot Use existing tooling Google – mySQL Lowest cost Most limited RDBMS functionality Microsoft – SQLAzure Highest cost http://code.google.com

Demo - AWS RDS SQL Server, MySQL or Oracle Essential to understand pricing models

Cloud Offerings– RDBMS AND NoSQL AWS Google Microsoft Cloud RDBMS RDS – all major mySQL SQL Azure NoSQL buckets S3 or Glacier Cloud Storage Azure Blobs NoSQL databases DynamoDB H/R Data on GAE Azure Tables Streaming ML or (Mahout) Custom EC2 Prospective Search & Prediction API StreamInsight Document or Graph MongoDB on EC2 Freebase MongoDB on Windows Azure Hadoop Elastic MapReduce using S3 & EC2 none HDInsight Dremel/Warehousing RedShift BigQuery Hadoop on AWS - http://wiki.apache.org/hadoop/AmazonEC2

Data Scientists… About Data Science -- http://www.romymisra.com/the-new-job-market-rulers-data-scientists/ R language - http://www.r-project.org/ Infer.NET - http://research.microsoft.com/en-us/um/cambridge/projects/infernet/ There are a plethora of languages to access, manipulate and process bigData. These languages fall into a couple of categories: RESTful – simple, standards ETL – Pig (Hadoop) is an example Query – Hive (again Hadoop), lots of *QL Analyze – R, Mahout, Infer.NET, DMX, etc.. Applying statistical (data-mining) algorithms to the data output

Comparing… http://rickosborne.org/download/SQL-to-MongoDB.pdf

Karmasphere Studio for AWS http://www.youtube.com/watch?v=gjsMDAcI1Mo - analyst http://www.youtube.com/watch?v=_MT04szKlyo http://aws.amazon.com/articles/9574327584309154

Hadoop Connector to Excel http://www.microsoft.com/en-us/bi/default.aspx http://dennyglee.com/ Demos -   http://www.youtube.com/watch?v=djfpPsGwm6A and http://www.youtube.com/watch?v=uh9bKWO1K7U

Google BigQuery Hadoop-like (Dremel) based service For massive amounts of data SQL-like query language http://research.google.com/pubs/pub36632.html

Dremel Realized => Impala Interactive Hadoop? http://blog.cloudera.com/blog/2012/10/cloudera-impala-real-time-queries-in-apache-hadoop-for-real/

Other types of cloud data services Hosting public datasets Pay to read Earn revenue by offering for read Cleaning / matching (your) data ETL – Microsoft Data Explorer, Google Refine Data Quality – Windows Azure Data Market, InfoChimps, DataMarket.com DataMarkets – InfoChimps, Factual, DataMarket, Windows Azure Data Marketplace, Wolfram Alpha, Datasift http://www.microsoft.com/en-us/sqlazurelabs/default.aspx and http://www.microsoft.com/en-us/sqlazurelabs/labs/dataexplorer.aspx https://datamarket.azure.com/ http://www.freebase.com/ http://code.google.com/p/google-refine/

NoSQL To-Do List Understand CAP & types of NoSQL databases Use NoSQL when business needs designate Use the right type of NoSQL for your business problem Try out NoSQL on the cloud Quick and cheap for behavioral data Mashup cloud datasets Good for specialized use cases, i.e. dev, test , training environments Learn noSQL access technologies New query languages, i.e. MapReduce, R, Infer.NET New query tools (vendor-specific) – Google Refine, Amazon Karmasphere, Microsoft Excel connectors, etc…

The Changing Data Landscape Course Title: Module Title 1-Oct-2011 The Changing Data Landscape NoSQL RDBMS Other Services ©2011 DevelopMentor

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Toward Data Craftsmanship… Follow me @LynnLangit RSS my blog www.LynnLangit.com Hire me To help build your BI/Big Data solution To teach your team next gen BI To learn more about using NoSQL solutions