company:mapr

#mapr-DB Spark Connector with Secondary Indexes
▻https://hackernoon.com/mapr-db-spark-connector-with-secondary-indexes-df41909f28ea?source=rss--

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MapR Data Platform offers significant advantages over any other tool on the big data space. MapR-DB is one of the core components of the platform and it offers state of the art capabilities that blow away most of the NoSQL databases out there.An important add-on to MapR-DB is the ability to use, for writing and querying, Apache Spark through the Connector for Apache Spark. Using this connector comes very handy since it can read and write from spark to MapR-DB using the different Spark APIs such as RDDs, DataFrames, and Streams.Using the connector we can issue queries like the following one.▻https://medium.com/media/eaf9aff1c6b796823d99a0b32e11c030/hrefThe resulting type is a Dataframethat we can use as any other Dataframe from any other source, as we normally do in Spark.If we then filter (...)

#programming #big-data #apache-spark #scala

#scala Object Serialization for #mapr-DB
▻https://hackernoon.com/scala-object-serialization-for-mapr-db-792817d3962d?source=rss----3a8144

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Previously, we have discussed some of the advantages and features accompanying MapR-DB. However, this time we are going to get our hand dirty while using this enterprise-grade database.When using MapR-DB, it is a common practice to serialize and deserialize our business objects (commonly known as POJO) from/to JSON all the time since MapR-DB data is stored using JSON format. These operations are very common and frequent so we will take a look at them from Scala’s point of view.MapR-DB Document APIThere are a series of steps to be followed to create new objects and to insert them into MapR-DB. Let’s look at the typical workflow.▻https://medium.com/media/e33ad01ea3ab9948ecff3c2b4e26e11b/hrefThis is the basic steps to insert into MapR-DB. This snippet can be extended for more complex use (...)

#coding #programming #big-data

Interacting with MapR-DB
▻https://hackernoon.com/interacting-with-mapr-db-58c4f482efa1?source=rss----3a8144eabfe3---4

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MapR Platform is an excellent choice for solving many of the problems associated with having humongous, continuously increasing dataset of nowadays businesses.The distributed, highly efficient file system along with the powerful yet simple and standard streaming API, are key components of the success of the platform. However, one of its most celebrated pieces is its distributed, non-SQL, highly available, JSON database.MapR-DB supports the HBase API for retro-compatibility, but the newest OJAI API is the core of it.Let’s look at an example of a document we could store in MapR-DB.▻https://medium.com/media/2378b9d6c9eec97a2ebe0e4fc31d0349/hrefThis is a pure JSON that MapR-DB can store. The document could be as complicated as we want. There is virtually no limit on the document size, the (...)

MapR-DB Atomic Document Updates
▻https://hackernoon.com/mapr-db-atomic-document-updates-d54f535b589?source=rss----3a8144eabfe3--

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In a previous post, we have discussed some of the features of MapR-DB that make this distributed database especially interesting. In this post, we intend to continue that effort by presenting a specific use case.The ProblemThe problem to be solved can be described as follows.A serie of message are coming through a stream. Each value has an idand a count. For eachid, we must update the existing count in the data base by incrementing its value using the count coming in the stream.The following image shows an example of the problem.The problem in an image.There are various ways to solve this problem. One could be by reading the current state of a given idstored on the database, then update its count using the values on the stream to finally save the updated values back to the database.Let’s (...)

#big-data #scala #coding #programming #spark