insights2csv.py allows for easy creation of CSV files from data pulled from New Relic Insights via their API.
New Relic Insights provides basic querying ability via NRQL. However, NRQL does not allow joins, self-joins, sub-queries and numerous other advanced features that are standard in more powerful relational databases. Thus, Insights users may often see a need to dump data from their Insights tables into a relational DB to run various single-use queries or data mining exercises. insights2csv.py allows easy extraction of Insights data into a CSV file, from where it can be imported into the DB of your choice. You can also use this to get Insights data into Excel.
If you're on OS-X, I'd strongly recommend looking at Postgres.app for analyzing your New Relic data. It lets you get a Postgres DB installed, configured and running in about 3 minutes.
- Edit config.py and enter your New Relic credentials and specify the columns, table and timeframe you wish to extract.
- Run the script: /path/to/folder/insights2csv.py
- Connect to your database
- Issue the CREATE TABLE statement to make an empty table with all required columns. I recommend BIGINT for timestamps and TEXT types for most other columns.
- CREATE INDEX statements for all needed indexes.
- Import the insights data into the database from the CSV. In Postgres
the command to do this is as follows:
COPY tablename FROM '/path/to/insights-data.csv' (delimiter ',');
- Start slicing and dicing your insights data with all the power of a relational DB or data warehouse!!!
Advanced Stuff in config.py
Hopefully, the comments in config.py are self-explanatory but here are some extra info on each, if you need it.
INSIGHTS_ACCOUNT_ID and INSIGHTS_QUERY_KEY
These can be found inside your New Relic account. If you fork insights2csv don't commit your credentials to a public repo!
This is the query you want to run to extract the Insights data. You should be explicit about which columns you want to retrieve and do not use SELECT *. The reason for this is that SELECT * may return different columns in each result set and you'll end up with a completely broken CSV. Also, you must leave the last line (SINCE '%s' UNTIL '%s' LIMIT 1000) unchanged so the Insights data can be paginated.
START_TIME and END_TIME
insights2csv works by paginating the data based on the SINCE/UNTIL
timestamps. For this reason, if your data exceeds 1000 data points per
second, you won't be able to use insights2csv to get at all your data.
You should use a very small interval between
when you first start testing the script (say, 1 minute) to ensure everything works and
that you are using the best possible value for
These values are ordered like a timestamp: year, month, day, hour, minute,
This step time must be set to determine how the data will be paginated when querying Insights. Since the query will only return 1000 records at a time, this value should be as large as possible without triggering the 1000 item threshold. It defaults to 1, which will run very slow but is the most widely compatible value. You may want to start at a higher value and keep tabs on the script to see if it dies to find the perfect balance.
Default behavior is for the script to die when it sees 1000 records.
This lets you fine-tune the
STEP_AMOUNT_IN_SECONDS to find the right
value for your data. If you don't care about missing data, you can
change this to False.
Default behavior is to create a new CSV everytime the program is run.
If the program died (such as from a
STEP_AMOUNT_IN_SECONDS value that
was too high) you can change
APPEND_TO_CSV to True and adjust
START_TIME to pick up where the program died.
This is the name of the CSV that will be produced
The name of the logfile. %s will be the timestamp
If you like insights2csv please star this repo so I'll know folks are using it. Also, be sure to +1 any of the github issues you'd like me to work on, or add your own suggestions. Feel free to email me directly as well with any feedback.
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