How Zerve Works

Execution order:

Zerve canvas follows DAG (Directed Acyclic Graphs) where the values are passed between left to right between blocks.

Running multiple blocks at once:

If blocks are not interdependent - then multiple blocks can be run at same time by using the run all mode.

Execution modes:

Canvas Level:

Run All:

This mode runs all the block from left to right starting from the first unfinished block. (Refer the image above for more info)

Force Run All:

This mode will rerun all the blocks from the first block irrespective of it's run status and replace data outputs. (Refer the image above for more info)

Block Level:

Run this block:

Runs the particular block. This is active only if the block is not run or if there is a code change

Run up to here:

Runs all the blocks left of this particular block including the selected block.

Force run this block:

Force executes the block regardless of code state or block state.

Force run up to here:

Force executes all the blocks up to the particular block regardless of current state.

How code blocks manage data:

Zerve is designed to be serverless and to abstract compute from storage. Once the code execution is done the data - all the variables, functions, dataframes, charts etc are cached, serialized and stored on disk (s3 in this case). This is then passed on to the next block as input for futher code execution.

This stored data is separated from the compute and won't be affected by code execution in downstream blocks.

Block states:

When is a block's data replaced?

When the block execution has been successfully completed. It is inheriting an integer and a dataframe from upstream, and its code creates an additional integer and makes some changes to the dataframe. If the user makes changes to the code in the block and re-runs it, the following are the various scenarios on what data will be passed downstream.

Scenario 1: Code changed and run, resulting in an error.

• Old data remains unless explicitly modified or deleted.

Scenario 2: Code changed but not run.

• Old data remains unaffected. (Check Output section and highlighted run status on top right in the screenshots below)

Scenario 3: Code changed and run, still running.

• Old data remains unless explicitly modified or deleted. (Check Output section and highlighted run status on top right in the screenshots below)

Scenario 4: Code changed, run, and completes successfully.

• Old data may or may not be replaced by new results, depending on the code's design. (Check Output section and highlighted run status on top right in the screenshots below)