Big Data Analytics on Bitcoin‘s first Altcoin: NameCoin

This blog post is about analyzing the Namecoin Blockchain using different Big Data technologies based on the HadoopCryptoLedger library. Currently, this library enables you to analyze the Bitcoin blockchain and Altcoins based on Bitcoin (incl. segregated witness), such as Namecoin, Litecoin, Zcash etc., on Big Data platforms, such as Hadoop, Hive, Flink and Spark. A support for Ethereum is planned for the near future.

However, back to Namecoin and why it is interesting:

  • It was one of the first Altcoin based on Bitcoin
  • It supports a decentralized domain name and identity system based on blockchain technology – no central actor can censor it
  • It is one of the first systems that supports merged mining/AuxPOW. Merged mining enables normal Bitcoin mining pools to mine Altcoins (such as Namecoin) without any additional effort while mining normal Bitcoins. Hence, they can make more revenue and at the same time they support Altcoins, which would be without Bitcoin mining pool support much weaker or not existing. It can be expected that many Altcoins based on Bitcoin will switch to it eventually.

HadoopCryptoLedger supported already from the beginning Altcoins based on Bitcoin. Usually it is expected that Big Data applications based on the HadoopCryptoLedger library implement the analytics they are interested in. However, sometimes we add specific functionality to make it much easier, for instance we provide the Hive UDFs to make certain analysis easier in Hive or we provide certain utility functions for MapReduce, Flink and Spark to make some things which require more detailed know how more easily available to beginners in block chain technology.

We have done this also for Namecoin by providing functionality to:

  • determine the type of name operation (new, firstupdate, update)
  • get more information about available domains, subdomains, ip addresses, identities

With this analysis is rather easy, because Namecoin requires you to update the information at least every 35,999 blocks (roughly between 200 to 250 days) or information are considered as expired. You just have to take simply the most recent information for a given domain or identity – it contains a full update of all information and there is no need to merge it with information from previous transactions. However, sometimes additional information are provided in so-called delegate entries and in this case you need to combine information.

Finally, we provided additional support to read blockchains based on Bitcoin and the merged mining/AuxPOW specification. You can enable it with a single configuration option.
Of course, we provide examples, e.g. for Spark and Hive. Nevertheless all major Big Data platforms (including Flink) are supported.
Find here what we plan next – amongst others:

  • Support analytics on Ethereum
  • Run analytics based on HadoopCryptoLedger in the cloud (e.g. Amazon EMR and/or Microsoft Azure) and provide real time aggregations as a simple HTML page
  • … much more

Let us know via Github issues what you are interested in!

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Templates, low footprint mode, improved integration with Spark for the HadoopOffice library for reading/writing Excel files on Big data platforms

Although it seems to be that it was only a small improvement, version 1.0.4 of the HadoopOffice library has a lot of new features for reading/writing Excel files:

  • Templates, so you can define complex documents with diagrams or other features in MSExcel and fill it with data or formulas from your Big Data platform in Hadoop, Spark & Co
  • Low footprint mode – this mode leverages the Apache POI event and streaming APIs. It saves CPU and memory consumption significantly at the expense of certain features (e.g. evaluation of formulas which is only supported in standard mode). This mode supports reading old MS Excel (.xls)/new MS Excel (.xlsx) and writing new MS Excel (.xlsx) documents
  • New features in the Spark 2 datasource:
    • Inferring of the DataFrame schema consisting of simple Spark SQL DataTypes (Boolean, Date, Byte, Short, Integer, Long, Decimal, String) based on the data in the Excel file
    • Improved writing of a DataFrame based on a schema with simpel Spark SQL DataTypes
    • Interpreting the first row of an Excel file as column names for the DataFrame for reading (“header”)
    • Writing column names of a DataFrame as the first row of an Excel file (“header”)
    • Support for Spark 2.0.1, 2.1, 2.2

 

Of course still other features are still usable, such as metadata reading/writing, encryption/decryption or linked workbooks, support for Hadoop MapReduce, support for Spark2 datasources and  support for Spark 1.

 

What is next?

  • Support for Apache Flink for reading/writing Excel files
  • Support for Apache Hive (Hive SerDe) for reading/writing Excel files
  • Support for digitally signing/verifying signature(s) of Excel files
  • Support for reading access files
  • … many more

Reading/Writing Excel documents with the HadoopOffice library on Hadoop and Spark – First release

Reading/Writing office documents, such as Excel, has been always challenging on Big data platforms. Although many libraries exist for reading/writing office documents, they have never been really integrated in Hadoop or Spark and thus lead to a lot of development efforts.

There are several use cases for using office documents jointly with Big data technologies:

  • Enabling the full customer-centric data science lifecycle: Within your Big Data platform you crunch numbers for complex models. However, you have to make them accessible to your customers. Le us assume you work in the insurance industry. Your Big Data platform calculates various models focused on your customer for insurance products. Your sales staff receives the models in Excel format. They can now play together with the customers on the different parameters, e.g. retirement age, individual risks etc. They may also come up with a different proposal more suitable for your customer and you want to feed it back into your Big Data platform to see if it is feasible.
  • You still have a lot of data in Excel files related to your computation. Let it be code lists, data collected manually or your existing systems simply support this format.

Hence, the HadoopOffice library was created and the first version has just been released!

It features:

Of course, further releases are planned:

  • Support for signing and verification of signature of Excel documents
  • Going beyond Excel with further office formats, such as ODF Calc
  • A Hive Serde for querying and writing Excel documents directly in Hive
  • Further examples including one for Apache Flink