html_url,issue_url,id,node_id,user,created_at,updated_at,author_association,body,reactions,performed_via_github_app,issue
https://github.com/pydata/xarray/issues/2329#issuecomment-408928221,https://api.github.com/repos/pydata/xarray/issues/2329,408928221,MDEyOklzc3VlQ29tbWVudDQwODkyODIyMQ==,1197350,2018-07-30T16:37:05Z,2018-07-30T16:37:23Z,MEMBER,"Can you forget about zarr for a moment and just do a reduction on your dataset? For example:
```python
ds.sum().load()
```

Keep the same chunk arguments you are currently using. This will help us understand if the problem is with reading the files.

Is it your intention to chunk the files contiguously in time? Depending on the underlying structure of the data within the netCDF file, this could amount to a complete transposition of the data, which could be very slow / expensive. This could have some parallels with #2004.","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,345715825
https://github.com/pydata/xarray/issues/2329#issuecomment-408925488,https://api.github.com/repos/pydata/xarray/issues/2329,408925488,MDEyOklzc3VlQ29tbWVudDQwODkyNTQ4OA==,1197350,2018-07-30T16:28:31Z,2018-07-30T16:28:31Z,MEMBER,"> I was somehow expecting that each worker will read a chunk and then write it to zarr, streamlined.

Yes, this is what we want!","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,345715825
https://github.com/pydata/xarray/issues/2329#issuecomment-408860643,https://api.github.com/repos/pydata/xarray/issues/2329,408860643,MDEyOklzc3VlQ29tbWVudDQwODg2MDY0Mw==,1197350,2018-07-30T13:20:59Z,2018-07-30T13:20:59Z,MEMBER,"@lrntct - this sounds like a reasonable way to use zarr. We routinely do this sort of transcoding and it works reasonable well. Unfortunately something clearly isn't working right in your case. These things can be hard to debug, but we will try to help you.

You might want to start by reviewing the [guide I wrote for Pangeo on preparing zarr datasets](http://pangeo-data.org/data.html#guide-to-preparing-cloud-optimized-data).

It would also be good to see a bit more detail. You posted a function `netcdf2zarr` that converts a single netcdf file to a single zarr file. How are you invoking that function? Are you trying to create one zarr store for each netCDF file? How many netCDF files are there? If there are many (e.g. one per timmestep), my recommendation is to create only one zarr store for the whole dataset. Open the netcdf files using `open_mfdataset`.

If instead you have just **one big netCDF file** as in the example you posted above, I think I see you problem: you are calling `.chunk()` after calling `open_dataset()`, rather calling `open_dataset(nc_path, chunks=chunks)`. This probably means that you are loading the whole dataset in a single task and then re-chunking. That could be the source of the inefficiency.

More ideas:
- explicitly specify the chunks (rather than using  `'auto'`)
- eliminate the negative number in your chunk sizes
- make sure you really need `clevel=9`


Another useful piece of advice would be to use the [dask distributed dashboard](http://distributed.readthedocs.io/en/latest/web.html) to monitor what is happening under the hood. You can do this by running
```python
from dask.distributed import Client
client = Client()
client
```
In a notebook, this should provide you a link to the scheduler dashboard. Once you call `ds.to_zarr()`, watch the task stream in the dashboard to see what is happening.

Hopefully these ideas can help you move forward.","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,345715825