Skip to main content

Which tool for my Big Data?

In order to perform searches and other operations on the big data produced by some systems in my daily usage I had to choose between three different and very popular Open Source technologies:

  1. ElasticSearch
  2. MongoDB
  3. Hadoop

Lets' have a quick look at the features of each one of them first.

Elastic Search
Elastic Search (http://www.elasticsearch.org/) is a search engine based on Lucene (http://lucene.apache.org/). Even if some data visualization and analytics features have been added to it, it is basically a pure search full text search engine into schema free JSON documents.

MongoDB
MongoDB (http://www.mongodb.org/) is a cross-platform NoSQL document-oriented database. Data are stored in JSON-like documents into collection in dynamic schemas. The data interchange format of the documents is BSON (Binary JSON). BSON types are a superset of the JSON types. Like Elastic Search, MongoDB allows full text search in documents, but it provides more useful features like server-side scripts in JavaScript, MapReduce (a data processing paradigm for condensing large volumes of data into useful aggregated results) support, capped collections (the possibility to define a maximum size for a collection: so once it fills its allocated space, it makes room for new documents by overwriting the oldest documents in the collection; this feature could be useful capturing logs or any streaming data for analysis), and an aggregation pipeline framework.

Hadoop
Apache Hadoop (http://hadoop.apache.org/) is a framework for distributed storage and distributed processing of Big Data on clusters of commodity hardware. Its Hadoop Distributed File System (HDFS) splits files into large blocks (default is 64MB or 128MB) and distributes the blocks amongst the nodes in the cluster. For processing the data, the Hadoop Map/Reduce ships code (specifically Jar files) to the nodes that have the required data, and the nodes then process the data in parallel. At present time it provides probably the overall most flexible and powerful environment for processing large amounts of data (covering scenarios that cannot be fulfilled by MongoDB or Elastic Search).

Which one is the best then? It isn't possible to define an absolute winner: it depends by your real needs. In some scenarios you could also choose to work with a combination of more than one of them (there are existing use cases with Elastic Search + MongoDB or Hadoop + Elastic Search).
For the purposes of my current scenario I decided upon MongoDB, but in future scenarios the choice could be different. 
Labels:

Comments

Post a Comment

Popular posts from this blog

Streamsets Data Collector log shipping and analysis using ElasticSearch, Kibana and... the Streamsets Data Collector

One common use case scenario for the Streamsets Data Collector (SDC) is the log shipping to some system, like ElasticSearch, for real-time analysis. To build a pipeline for this particular purpose in SDC is really simple and fast and doesn't require coding at all. For this quick tutorial I will use the SDC logs as example. The log data will be shipped to Elasticsearch and then visualized through a Kibana dashboard. Basic knowledge of SDC, Elasticsearch and Kibana is required for a better understanding of this post. These are the releases I am referring to for each system involved in this tutorial: JDK 8 Streamsets Data Collector 1.4.0 ElasticSearch 2.3.3 Kibana 4.5.1 Elasticsearch and Kibana installation You should have your Elasticsearch cluster installed and configured and a Kibana instance pointing to that cluster in order to go on with this tutorial. Please refer to the official documentation for these two products in order to complete their installation (if you do
Post a Comment
Read more

Exporting InfluxDB data to a CVS file

Sometimes you would need to export a sample of the data from an InfluxDB table to a CSV file (for example to allow a data scientist to do some offline analysis using a tool like Jupyter, Zeppelin or Spark Notebook). It is possible to perform this operation through the influx command line client. This is the general syntax: sudo /usr/bin/influx -database '<database_name>' -host '<hostname>' -username '<username>'  -password '<password>' -execute 'select_statement' -format '<format>' > <file_path>/<file_name>.csv where the format could be csv , json or column . Example: sudo /usr/bin/influx -database 'telegraf' -host 'localhost' -username 'admin'  -password '123456789' -execute 'select * from mem' -format 'csv' > /home/googlielmo/influxdb-export/mem-export.csv
6 comments
Read more

Using Rapids cuDF in a Colab notebook

During last Spark+AI Summit Europe 2019 I had a chance to attend a talk from Miguel Martinez  who was presenting Rapids , the new Open Source framework from NVIDIA for GPU accelerated end-to-end Data Science and Analytics. Fig. 1 - Overview of the Rapids eco-system Rapids is a suite of Open Source libraries: cuDF cuML cuGraph cuXFilter I enjoied the presentation and liked the idea of this initiative, so I wanted to start playing with the Rapids libraries in Python on Colab , starting from cuDF, but the first attempt came with an issue that I eventually solved. So in this post I am going to share how I fixed it, with the hope it would be useful to someone else running into the same blocker. I am assuming here you are already familiar with Google Colab. I am using Python 3.x as Python 2 isn't supported by Rapids. Once you have created a new notebook in Colab, you need to check if the runtime for it is set to use Python 3 and uses a GPU as hardware accelerator. You
5 comments
Read more