Hotfix v0.1a1

.gitignore

@@ -112,3 +112,4 @@ venv.bak/
 _catalogs/
 _old/
 
+.DS_Store

api/Dockerfile

@@ -1,9 +1,19 @@
 ARG REGISTRY=rg.fr-par.scw.cloud/geolake
 ARG TAG=latest
 FROM $REGISTRY/geolake-datastore:$TAG
+
+RUN apt update && apt install -y cron curl
+
 WORKDIR /app
 COPY requirements.txt /code/requirements.txt
 RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt
 COPY app /app
 EXPOSE 80
+
+COPY ./healtcheck.* /opt/
+
+RUN chmod +x /opt/healtcheck.sh
+RUN crontab -u root /opt/healtcheck.cron
+
 CMD ["uvicorn", "app.main:app", "--proxy-headers", "--host", "0.0.0.0", "--port", "80"]
+

api/healtcheck.cron

@@ -0,0 +1 @@
+*/10 * * * * bash -c '/opt/healtcheck.sh'

api/healtcheck.sh

@@ -0,0 +1 @@
+curl https://hc-ping.com/$HEALTCHECKS

datastore/datastore/datastore.py

@@ -50,7 +50,7 @@ def __init__(self) -> None:
 
     @log_execution_time(_LOG)
     def get_cached_product_or_read(
-        self, dataset_id: str, product_id: str, query: GeoQuery | None = None
+        self, dataset_id: str, product_id: str,
     ) -> DataCube | Dataset:
         """Get product from the cache instead of loading files indicated in
         the catalog if `metadata_caching` set to `True`.
@@ -81,7 +81,7 @@ def get_cached_product_or_read(
             )
             return self.catalog(CACHE_DIR=self.cache_dir)[dataset_id][
                 product_id
-            ].get(geoquery=query, compute=False).read_chunked()
+            ].read_chunked()
         return self.cache[dataset_id][product_id]
 
     @log_execution_time(_LOG)
@@ -389,7 +389,7 @@ def estimate(
         # NOTE: we always use catalog directly and single product cache
         self._LOG.debug("loading product...")
         # NOTE: for estimation we use cached products
-        kube = self.get_cached_product_or_read(dataset_id, product_id, query=query)
+        kube = self.get_cached_product_or_read(dataset_id, product_id)
         self._LOG.debug("original kube len: %s", len(kube))
         return Datastore._process_query(kube, geoquery, False).nbytes
 

drivers/Dockerfile

@@ -1,6 +1,6 @@
 ARG REGISTRY=rg.fr-par.scw.cloud/geokube
-# ARG TAG=v0.2.6b1
-ARG TAG=latest
+#ARG TAG=v0.2.6b2
+ARG TAG=2024.05.03.10.36
 FROM $REGISTRY/geokube:$TAG
 
 COPY dist/intake_geokube-0.1a0-py3-none-any.whl /

drivers/intake_geokube/afm.py

@@ -0,0 +1,99 @@
+"""geokube driver for intake."""
+
+from typing import Mapping, Optional
+import geokube
+import numpy as np
+import xarray as xr
+from .base import GeokubeSource
+from geokube import open_datacube, open_dataset
+from geokube.core.datacube import DataCube
+
+_PROJECTION = {"grid_mapping_name": "latitude_longitude"}
+
+def postprocess_afm(ds: xr.Dataset, **post_process_chunks):
+    if isinstance(ds, geokube.core.datacube.DataCube):
+        ds = ds.to_xarray()
+    latitude = ds['lat'].values
+    longitude = ds['lon'].values
+    # ds = ds.expand_dims(dim={"latitude": latitude, "longitude": longitude}, axis=(1,0))
+    ds = ds.drop('lat')
+    ds = ds.drop('lon')
+    ds = ds.drop('certainty')
+    deduplicated = ds.expand_dims(dim={"latitude": latitude, "longitude": longitude}, axis=(1, 0))
+    # print(deduplicated.dims)
+    for dim in deduplicated.dims:
+        indexes = {dim: ~deduplicated.get_index(dim).duplicated(keep='first')}
+        deduplicated = deduplicated.isel(indexes)
+    return DataCube.from_xarray(
+        deduplicated.sortby('time').sortby('latitude').sortby('longitude').chunk(post_process_chunks))
+
+def add_projection(dset: xr.Dataset, **kwargs) -> xr.Dataset:
+    """Add projection information to the dataset"""
+    coords = dset.coords
+    coords["crs"] = xr.DataArray(data=np.array(1), attrs=_PROJECTION)
+    for var in dset.data_vars.values():
+        enc = var.encoding
+        enc["grid_mapping"] = "crs"
+    return dset
+
+
+class CMCCAFMSource(GeokubeSource):
+    name = "cmcc_afm_geokube"
+
+    def __init__(
+            self,
+            path: str,
+            pattern: str = None,
+            field_id: str = None,
+            delay_read_cubes: bool = False,
+            metadata_caching: bool = False,
+            metadata_cache_path: str = None,
+            storage_options: dict = None,
+            xarray_kwargs: dict = None,
+            metadata=None,
+            mapping: Optional[Mapping[str, Mapping[str, str]]] = None,
+            load_files_on_persistance: Optional[bool] = True,
+            postprocess_chunk: Optional = None
+    ):
+        self._kube = None
+        self.path = path
+        self.pattern = pattern
+        self.field_id = field_id
+        self.delay_read_cubes = delay_read_cubes
+        self.metadata_caching = metadata_caching
+        self.metadata_cache_path = metadata_cache_path
+        self.storage_options = storage_options
+        self.mapping = mapping
+        self.xarray_kwargs = {} if xarray_kwargs is None else xarray_kwargs
+        self.load_files_on_persistance = load_files_on_persistance
+        self.postprocess_chunk = postprocess_chunk
+        # self.preprocess = preprocess_afm
+        super(CMCCAFMSource, self).__init__(metadata=metadata)
+
+    def _open_dataset(self):
+        if self.pattern is None:
+            self._kube =\
+                postprocess_afm(
+                    open_datacube(
+                        path=self.path,
+                        id_pattern=self.field_id,
+                        metadata_caching=self.metadata_caching,
+                        metadata_cache_path=self.metadata_cache_path,
+                        mapping=self.mapping,
+                        **self.xarray_kwargs,
+                        # preprocess=self.preprocess
+                    ),
+                    **self.postprocess_chunk
+                ).resample('maximum', frequency='1H')
+        else:
+            self._kube = open_dataset(
+                        path=self.path,
+                        pattern=self.pattern,
+                        id_pattern=self.field_id,
+                        metadata_caching=self.metadata_caching,
+                        metadata_cache_path=self.metadata_cache_path,
+                        mapping=self.mapping,
+                        **self.xarray_kwargs,
+                        # preprocess=self.preprocess
+                    ).apply(postprocess_afm,**self.postprocess_chunk).resample('maximum', frequency='1H')
+        return self._kube

drivers/setup.py

@@ -18,6 +18,7 @@
         "intake.drivers": [
             "geokube_netcdf = intake_geokube.netcdf:NetCDFSource",
             "cmcc_wrf_geokube = intake_geokube.wrf:CMCCWRFSource",
+            "cmcc_afm_geokube = intake_geokube.afm:CMCCAFMSource",
         ]
     },
     classifiers=[

executor/Dockerfile

@@ -1,8 +1,18 @@
 ARG REGISTRY=rg.fr-par.scw.cloud/geolake
 ARG TAG=latest
 FROM $REGISTRY/geolake-datastore:$TAG
+
+RUN apt update && apt install -y cron curl
+
+
 WORKDIR /app
 COPY requirements.txt /code/requirements.txt
 RUN pip install --no-cache-dir -r /code/requirements.txt
 COPY app /app
+
+COPY ./healtcheck.* /opt/
+
+RUN chmod +x /opt/healtcheck.sh
+RUN crontab -u root /opt/healtcheck.cron
+
 CMD [ "python", "main.py" ]

executor/app/main.py

@@ -231,26 +231,20 @@ def process(message: Message, compute: bool):
 class Executor(metaclass=LoggableMeta):
     _LOG = logging.getLogger("geokube.Executor")
 
-    def __init__(self, broker, store_path):
+    def __init__(self, broker, store_path, dask_cluster_opts):
         self._store = store_path
         broker_conn = pika.BlockingConnection(
             pika.ConnectionParameters(host=broker, heartbeat=10),
         )
         self._conn = broker_conn
         self._channel = broker_conn.channel()
         self._db = DBManager()
+        self.dask_cluster_opts = dask_cluster_opts
 
     def create_dask_cluster(self, dask_cluster_opts: dict = None):
         if dask_cluster_opts is None:
-            dask_cluster_opts = {}
-            dask_cluster_opts["scheduler_port"] = int(
-                os.getenv("DASK_SCHEDULER_PORT", 8188)
-            )
-            dask_cluster_opts["processes"] = True
-            port = int(os.getenv("DASK_DASHBOARD_PORT", 8787))
-            dask_cluster_opts["dashboard_address"] = f":{port}"
-            dask_cluster_opts["n_workers"] = None
-            dask_cluster_opts["memory_limit"] = "auto"
+            dask_cluster_opts = self.dask_cluster_opts
+
         self._worker_id = self._db.create_worker(
             status="enabled",
             dask_scheduler_port=dask_cluster_opts["scheduler_port"],
@@ -262,10 +256,11 @@ def create_dask_cluster(self, dask_cluster_opts: dict = None):
             extra={"track_id": self._worker_id},
         )
         dask_cluster = LocalCluster(
-            n_workers=dask_cluster_opts["n_workers"],
+            n_workers=dask_cluster_opts['n_workers'],
             scheduler_port=dask_cluster_opts["scheduler_port"],
             dashboard_address=dask_cluster_opts["dashboard_address"],
             memory_limit=dask_cluster_opts["memory_limit"],
+            threads_per_worker=dask_cluster_opts['thread_per_worker'],
         )
         self._LOG.info(
             "creating Dask Client...", extra={"track_id": self._worker_id}
@@ -356,7 +351,7 @@ def retry_until_timeout(
             )
             status = RequestStatus.FAILED
             fail_reason = f"{type(e).__name__}: {str(e)}"
-        return (location_path, status, fail_reason)
+        return location_path, status, fail_reason
 
     def handle_message(self, connection, channel, delivery_tag, body):
         message: Message = Message(body)
@@ -382,6 +377,9 @@ def handle_message(self, connection, channel, delivery_tag, body):
             message=message,
             compute=False,
         )
+
+        #future = asyncio.run(process(message,compute=False))
+
         location_path, status, fail_reason = self.retry_until_timeout(
             future,
             message=message,
@@ -447,7 +445,19 @@ def get_size(self, location_path):
     executor_types = os.getenv("EXECUTOR_TYPES", "query").split(",")
     store_path = os.getenv("STORE_PATH", ".")
 
-    executor = Executor(broker=broker, store_path=store_path)
+    dask_cluster_opts = {}
+    dask_cluster_opts["scheduler_port"] = int(
+        os.getenv("DASK_SCHEDULER_PORT", 8188)
+    )
+    dask_cluster_opts["processes"] = True
+    port = int(os.getenv("DASK_DASHBOARD_PORT", 8787))
+    dask_cluster_opts["dashboard_address"] = f":{port}"
+    dask_cluster_opts["n_workers"] = int(os.getenv("DASK_N_WORKERS", 1))
+    dask_cluster_opts["memory_limit"] = os.getenv("DASK_MEMORY_LIMIT", "auto")
+    dask_cluster_opts['thread_per_worker'] = int(os.getenv("DASK_THREADS_PER_WORKER", 8))
+
+
+    executor = Executor(broker=broker, store_path=store_path, dask_cluster_opts=dask_cluster_opts)
     print("channel subscribe")
     for etype in executor_types:
         if etype == "query":

executor/healtcheck.cron

@@ -0,0 +1 @@
+*/10 * * * * bash -c '/opt/healtcheck.sh'

executor/healtcheck.sh

@@ -0,0 +1 @@
+curl https://hc-ping.com/$HEALTCHECKS