google-cloud-datastore | based Java and Python client libraries | Database library
kandi X-RAY | google-cloud-datastore Summary
kandi X-RAY | google-cloud-datastore Summary
Note: This repository contains low-level Java and Python client libraries for Google Cloud Datastore. For more idiomatic and usable client libraries in these languages, please visit the Google Cloud Client Libraries for Java and Google Cloud Client Libraries for Python repositories. You can also find the full list of supported client libraries in a variety of languages on the Client Libraries page of Cloud Datastore. Cloud Datastore is a highly-scalable NoSQL database for your applications. Cloud Datastore automatically handles sharding and replication, providing you with a highly available and durable database that scales automatically to handle your applications' load. Cloud Datastore provides a myriad of capabilities such as ACID transactions, SQL-like queries, indexes and much more. For more information, see the Cloud Datastore documentation. This repository contains clients that are deliberately low-level and map directly to the underlying Datastore RPC model. They're designed to provide more flexibility to developers and higher level library implementers.
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google-cloud-datastore Key Features
google-cloud-datastore Examples and Code Snippets
Lines of Code : 19License : Strong Copyleft (CC BY-SA 4.0)
Copydef read_header_from_filename(filename):
# note that depending on your newline character/file encoding, this may need to be modified
file_handle = FileSystems.open(filename)
header = file_handle.readline()
return header.split(','from google.cloud.proto.datastore.v1 import entity_pb2
from google.cloud.proto.datastore.v1 import query_pb2
from googledatastore import helper as datastore_helper
import apache_beam as beam
from apache_beam.io.gcp.datastore.v1.datastoreiofrom googledatastore import helper
value_dict = dict((prop_name, helper.get_value(entity.properties.get(prop_name)),) for prop_name in entity.properties)
pipeline_options = {
'project': PROJECT,
'staging_location': STAGING_LOCATION,
'runner': 'DataflowRunner',
'job_name': JOB_NAME,
'temp_location': TEMP_LOCATION,
'streaming': True,
'save_main_session': True} #
<Missing required coder_id on grpc_port for -3; using deprecated fallback.
--requirements_file requirements.txt
import apache_beam as beam
from apache_beam.io.gcp.datastore.v1.datastoreio import WriteToDatastore
from google.cloud.proto.datastore.v1 import entity_pb2
from google.cloud.proto.datastore.v1 import query_pb2
from googledatastore import he%%bash
source activate py2env
conda install -y pytz
pip uninstall -y google-cloud-dataflow
pip install --upgrade apache-beam[gcp]
Flask
gunicorn
apache-beam[gcp]==2.6.0
oauth2client==3.0.0
google-cloud-datastore==1.3.0
google-cloud-pubsub==0.28.0
google-cloud-core==0.27.0
google-cloud==0.34.0
if os.path.exists('requirements.txt'):
with oppip freeze > requirements.txt
--requirements_file requirements.txt
Community Discussions
Trending Discussions on google-cloud-datastore
QUESTION
To call the Datastore.export() API I need to provide a GCS bucket name in the same region as the Datastore I'm exporting.
I checked with node.js' @google-cloud/datastore Datastore instances seem to have no .location property or something similar. Also, the Google provided Datastore libraries in other languages seem to lack this functionality.
With other Google APIs you usually have a way to get the Location of the resource. E.g. in GCS: Storage().bucket('mybucket').getMetadata().location -> 'EU'.
How to view Google Cloud Datastore Region shows to get this information in a manual way, but I'm after programmatic access.
...ANSWER
Answered 2021-Dec-24 at 00:46Firestore has a database get method (in preview as of 2021-12-23), that you can use to lookup the location of your database. This API will also tell you the mode of your database (Firestore native vs Datastore mode).
QUESTION
I'm writing some code for a class project that sends jobs to a dataproc cluster in GCP. I recently ran into an odd error and I'm having trouble wrapping my head around it. The error is as follows:
...ANSWER
Answered 2021-Dec-01 at 19:46Using mvn dependency:tree you can discover there's a mix of grpc-java 1.41.0 and 1.42.1 versions in your dependency tree. google-cloud-datastore:2.2.0 brings in grpc-api:1.42.1 but the other dependencies bring in grpc version 1.40.1.
grpc-java recommends always using requireUpperBoundDeps from maven-enforcer to catch Maven silently downgrading dependencies.
QUESTION
I am trying to create a dataproc cluster that will connect dataproc to pubsub. I need to add multiple jars on cluster creation in the spark.jars flag
...ANSWER
Answered 2021-Nov-27 at 22:40The answer you linked is the correct way to do it: How can I include additional jars when starting a Google DataProc cluster to use with Jupyter notebooks?
If you also post the command you tried with the escaping syntax and the resulting error message then others could more easily verify what you did wrong. It looks like you're specifying an additional spark property in addition to your list of jars spark:spark.driver.memory=3000m, and tried to just space-separate that from your jars flag, which isn't allowed.
Per the linked result, you'd need to use the newly assigned separator character to separate the second spark property:
QUESTION
data source: https://catalog.data.gov/dataset/nyc-transit-subway-entrance-and-exit-data
I tried looking for a similar problem but I can't find an answer and the error does not help much. I'm kinda frustrated at this point. Thanks for the help. I'm calculating the closest distance from a point.
...ANSWER
Answered 2021-Oct-11 at 14:21geopandas 0.10.1
- have noted that your data is on kaggle, so start by sourcing it
- there really is only one issue
shapely.geometry.MultiPoint()constructor does not work with a filtered series. Pass it a numpy array instead and it works. - full code below, have randomly selected a point to serve as
gpdPoint
QUESTION
the replication between Datastore servers. Replication is managed by Cloud Bigtable and Megastore, the underlying technologies for Datastore
Replication for Cloud Bigtable enables you to increase the availability and durability of your data by copying it across multiple regions or multiple zones within the same region
How can I see in the datastore UI if I'm getting any replication? If I am getting replication how can I see if I'm getting cross region or cross zone replication for my datastore entities?
(The entities I'm looking at have been populated since 2017 if that's useful.)
...ANSWER
Answered 2021-Jan-23 at 13:07Cloud Datastore is only a regional service. You can't deploy it in multiple region in the same project.
Its brother (or sister, I don't know), Firestore, can be deployed in multi region.
So, Datastore is only mono region, but multi zonal in this unique region. And the BigTable replication mechanism is used to achieve this replication. You can't see this, it's serverless, transparent.
QUESTION
I have deployed an app in GKE which makes a backend call to datastore to perform crud operations.Added cloud datastore owner role to the Service account on which gke is hosted.
when i request any of the endpoint which makes call to the backend datastore i am getting below excpetion:
...ANSWER
Answered 2021-Jan-15 at 20:44It's a problem at node level permission. When you create your cluster, and your node pool, you can choose the security of your node. And you can explicitly allow or deny the access to some API, such as Datastore. And by default Datastore is disabled.
You can check this in the Compute Engine that compose your GKE cluster:
If you don't want to delete your Cluster, you need to create a new node pool, to migrate your pod and to remove the old node pool.
Side remark. Don't you Service account key files. I recommend you to use workload identity for a better security
QUESTION
I am trying to use BigQuery in AI-Platform-Notebooks, but I am running into a ContextualVersionConflict. In this toy example, I am trying to pull two columns worth of data from the BigQuery database entitled bgt_all, in the project job2vec.
...ANSWER
Answered 2021-Jan-05 at 10:19In order to further contribute to the community I am posting the answer based on my comment above.
Firstly, you should try to upgrade the packages using the command:
pip install --upgrade pandas-gbq 'google-cloud-bigquery[bqstorage,pandas]'
Then, instead of using the to_dataframe() method you can use the read_gbq(), which loads data from BigQuery using the environment's default project, as follows:
QUESTION
I am working on a Google App and have an issue when I deploy it. The project contains two services website and worker. The website acts as a front page and the worker is run through a cron and has its page hit every 5 minutes.
The issue I'm having is that when running them locally both services run fine. Once I deploy with gcloud the application returns an error in the Logs Console.
The error is: ImportError: cannot import name 'vision' from 'google.cloud' (unknown location)
It is the worker service that is not working. It's requirements.txt reads
...ANSWER
Answered 2020-Dec-07 at 18:09The issue seemed to be caused by the order of the components in the requirements file, similar to this issue. The solution is to explicitly include the grpcio module in the requirements.txt file, e.g. :
QUESTION
tl;dr: How can I run this project locally, in a way that Datastore will work? (Zip download link here.)
I'm migrating a Java 8 project that used App Engine and Datastore over to Java 11.
With Java 8, I used the Cloud SDK-based App Engine plugin to run the server locally using mvn appengine:run and to deploy to the live server using mvn appengine:deploy.
I followed this guide which told me to delete the appengine-web.xml file and use app.yaml instead..
To deploy to the live server, I can still use mvn appengine:deploy and this works fine, with and without Datastore.
To deploy locally, I run mvn package exec:java. This works fine for running a basic server without Datastore, but if I add some example Datastore code, then I get this error:
ANSWER
Answered 2020-Aug-22 at 21:30Based on guillaume blaquiere's suggestion in their comment, I tried following this guide for manually running Datastore locally.
I ran gcloud beta emulators datastore start in one command line, which seemed to run fine, and then I ran $(gcloud beta emulators datastore env-init) in another command line, and I got this error:
QUESTION
I have a number of Python 3.7 apps on Google App Engine standard, all building and deploying fine. I'm trying to upgrade some of them to the new Python 3.8 runtime, but when I try to deploy, they fail in Cloud Build.
It looks like they're hitting this open pip bug (more background). Odd that only the Python 3.8 runtime triggers this bug, though, and 3.7 builds fine.
Full log below. (Note that it's happening in Cloud Build, not my local machine, so I can't upgrade pip or otherwise change any of the commands or environment.) Anyone know how I can fix or work around this?
...ANSWER
Answered 2020-Aug-22 at 16:54I checked pypi page of oauth-dropins (at which it is failing) and they're mentioning there exactly this issue being caused by -e
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install google-cloud-datastore
You can use google-cloud-datastore like any standard Java library. Please include the the jar files in your classpath. You can also use any IDE and you can run and debug the google-cloud-datastore component as you would do with any other Java program. Best practice is to use a build tool that supports dependency management such as Maven or Gradle. For Maven installation, please refer maven.apache.org. For Gradle installation, please refer gradle.org .
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