IllegalStateException from Mongo storage plugin in Apache Drill

Apache Drill (https://drill.apache.org/) is an Open Source framework that supports data-intensive distributed applications for interactive analysis of large scale datasets. It supports several kinds of filesystems and NoSQL databases, including HDFS and MongoDB. In this short post I want to show an exception you could deal with if your Drill installation has the MongoDB storage plugin enabled.
If suddenly, after more than 24 of uptime, any SQL query (not only those against a MongoDB database) issued through Drill throw the following exception:

SYSTEM ERROR: IllegalStateException: state should be: open [Error Id: 57a02508-1920-4360-a111-c2a55a7af15c on hostname:31010]]

this should be related to a connection cache that expires and isn't automatically reset. This issue affects in particular the latest release (1.1.0) of Drill and it is still marked as unresolved at the time of this post writing (https://issues.apache.org/jira/browse/DRILL-3522), but there is a patch available for it (https://issues.apache.org/jira/secure/attachment/12746251/DRILL-3522.1.patch.txt) that requires the recompilation of Drill from the source code. If you don't feel confident rebuilding it from scratch there is a quick and dirty trick to restore the correct execution of the queries: go to the Drill Web UI and move to the Storage page, disable the mongo storage plugin and then enabled it again.

Comments

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

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

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

Googlielmo's blog

Search This Blog