Node administration¶

Logging¶

By default the node log files are stored to the logs subdirectory of the working directory and are rotated from time to time. You can have logging printed to the console as well by passing the --log-to-console command line flag. The default logging level is INFO which can be adjusted by the --logging-level command line argument. For more custom logging, the logger settings can be completely overridden with a Log4j 2 configuration file assigned to the log4j.configurationFile system property.

Database access¶

The node exposes its internal database over a socket which can be browsed using any tool that can use JDBC drivers. The JDBC URL is printed during node startup to the log and will typically look like this:

jdbc:h2:tcp://192.168.0.31:31339/node

The username and password can be altered in the Node configuration but default to username “sa” and a blank password.

Any database browsing tool that supports JDBC can be used, but if you have IntelliJ Ultimate edition then there is a tool integrated with your IDE. Just open the database window and add an H2 data source with the above details. You will now be able to browse the tables and row data within them.

Monitoring your node¶

Like most Java servers, the node exports various useful metrics and management operations via the industry-standard JMX infrastructure. JMX is a standard API for registering so-called MBeans ... objects whose properties and methods are intended for server management. It does not require any particular network protocol for export. So this data can be exported from the node in various ways: some monitoring systems provide a “Java Agent”, which is essentially a JVM plugin that finds all the MBeans and sends them out to a statistics collector over the network. For those systems, follow the instructions provided by the vendor.

Sometimes though, you just want raw access to the data and operations itself. So nodes export them over HTTP on the /monitoring/json HTTP endpoint, using a program called Jolokia. Jolokia defines the JSON and REST formats for accessing MBeans, and provides client libraries to work with that protocol as well.

Here are a few ways to build dashboards and extract monitoring data for a node:

• JMX2Graphite is a tool that can be pointed to /monitoring/json and will scrape the statistics found there, then insert them into the Graphite monitoring tool on a regular basis. It runs in Docker and can be started with a single command.
• JMXTrans is another tool for Graphite, this time, it’s got its own agent (JVM plugin) which reads a custom config file and exports only the named data. It’s more configurable than JMX2Graphite and doesn’t require a separate process, as the JVM will write directly to Graphite.
• Java Mission Control is a desktop app that can connect to a target JVM that has the right command line flags set (or always, if running locally). You can explore what data is available, create graphs of those metrics, and invoke management operations like forcing a garbage collection.
• VisualVM is another desktop app that can do fine grained JVM monitoring and sampling. Very useful during development.
• Cloud metrics services like New Relic also understand JMX, typically, by providing their own agent that uploads the data to their service on a regular schedule.

Memory usage and tuning¶

All garbage collected programs can run faster if you give them more memory, as they need to collect less frequently. As a default JVM will happily consume all the memory on your system if you let it, Corda is configured with a relatively small 200mb Java heap by default. When other overheads are added, this yields a total memory usage of about 500mb for a node (the overheads come from things like compiled code, metadata, off-heap buffers, thread stacks, etc).

If you want to make your node go faster and profiling suggests excessive GC overhead is the cause, or if your node is running out of memory, you can give it more by running the node like this:

java -Xmx1024m -jar corda.jar

The example command above would give a 1 gigabyte Java heap.

Uploading and downloading attachments¶

Attachments are files that add context to and influence the behaviour of transactions. They are always identified by hash and they are public, in that they propagate through the network to wherever they are needed.

All attachments are zip files. Thus to upload a file to the ledger you must first wrap it into a zip (or jar) file. Then you can upload it by running this command from a UNIX terminal:

curl -F myfile=@path/to/my/file.zip http://localhost:31338/upload/attachment

The attachment will be identified by the SHA-256 hash of the contents, which you can get by doing:

shasum -a 256 file.zip

on a Mac or by using sha256sum on Linux. Alternatively, the hash will be returned to you when you upload the attachment.

An attachment may be downloaded by fetching:

http://localhost:31338/attachments/DECD098666B9657314870E192CED0C3519C2C9D395507A238338F8D003929DE9

where DECD... is of course replaced with the hash identifier of your own attachment. Because attachments are always containers, you can also fetch a specific file within the attachment by appending its path, like this:

http://localhost:31338/attachments/DECD098666B9657314870E192CED0C3519C2C9D395507A238338F8D003929DE9/path/within/zip.txt

Uploading interest rate fixes¶

If you would like to operate an interest rate fixing service (oracle), you can upload fix data by uploading data in a simple text format to the /upload/interest-rates path on the web server.

The file looks like this:

# Some pretend noddy rate fixes, for the interest rate oracles.

LIBOR 2016-03-16 1M = 0.678
LIBOR 2016-03-16 2M = 0.655
EURIBOR 2016-03-15 1M = 0.123
EURIBOR 2016-03-15 2M = 0.111

The columns are:

• Name of the fix
• Date of the fix
• The tenor / time to maturity in days
• The interest rate itself