| Name | pydgraph JSON |
| Version |
23.0.2
JSON |
| download |
| home_page | |
| Summary | Official Dgraph client implementation for Python |
| upload_time | 2023-11-08 17:19:45 |
| maintainer | |
| docs_url | None |
| author | |
| requires_python | >=3.7 |
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# pydgraph
This is the official Dgraph database client implementation for Python (Python >= v3.7), using [gRPC][grpc].
[grpc]: https://grpc.io/
This client follows the [Dgraph Go client][goclient] closely.
[goclient]: https://github.com/dgraph-io/dgo
Before using this client, we highly recommend that you read the
the product documentation at [dgraph.io/docs].
[dgraph.io/docs]:https://dgraph.io/docs
## Table of contents
- [pydgraph](#pydgraph)
- [Table of contents](#table-of-contents)
- [Install](#install)
- [Supported Versions](#supported-versions)
- [Quickstart](#quickstart)
- [Using a client](#using-a-client)
- [Creating a Client](#creating-a-client)
- [Login into a Namespace](#login-into-a-namespace)
- [Connecting To Dgraph Cloud](#connecting-to-dgraph-cloud)
- [Altering the Database](#altering-the-database)
- [Creating a Transaction](#creating-a-transaction)
- [Running a Mutation](#running-a-mutation)
- [Running a Query](#running-a-query)
- [Query with RDF response](#query-with-rdf-response)
- [Running an Upsert: Query + Mutation](#running-an-upsert-query--mutation)
- [Running a Conditional Upsert](#running-a-conditional-upsert)
- [Committing a Transaction](#committing-a-transaction)
- [Cleaning Up Resources](#cleaning-up-resources)
- [Setting Metadata Headers](#setting-metadata-headers)
- [Setting a timeout](#setting-a-timeout)
- [Async methods](#async-methods)
- [Examples](#examples)
- [Development](#development)
- [Setting up environment](#setting-up-environment)
- [Build from source](#build-from-source)
- [Running tests](#running-tests)
## Install
Install using pip:
```sh
pip install pydgraph
```
## Supported Versions
Depending on the version of Dgraph that you are connecting to, you will have to
use a different version of this client.
| Dgraph version | pydgraph version |
|:--------------:|:--------------------:|
| 20.3.X | *20.3.0* |
| 20.7.X | *20.7.0* |
| 20.11.X | *20.7.0* |
| 21.03.X | *21.3.0* |
| 22.0.X | *21.3.0* |
| 23.0.X | *23.0.0* |
## Quickstart
Build and run the [simple project][simple] in the `examples` folder, which
contains an end-to-end example of using the Dgraph python client. For additional details, follow the
instructions in the project's [README](./examples/simple/README.md).
[simple]: ./examples/simple
## Using a client
### Creating a Client
You can initialize a `DgraphClient` object by passing it a list of
`DgraphClientStub` clients as variadic arguments. Connecting to multiple Dgraph
servers in the same cluster allows for better distribution of workload.
The following code snippet shows just one connection.
```python3
import pydgraph
client_stub = pydgraph.DgraphClientStub('localhost:9080')
client = pydgraph.DgraphClient(client_stub)
```
### Login into a Namespace
If your server has Access Control Lists enabled (Dgraph v1.1 or above), the client must be
logged in for accessing data. Use `login` endpoint:
Calling login will obtain and remember the access and refresh JWT tokens. All subsequent operations
via the logged in client will send along the stored access token.
```python3
client.login("groot", "password")
```
If your server additionally has namespaces (Dgraph v21.03 or above), use the
`login_into_namespace` API.
```python3
client.login_into_namespace("groot", "password", "123")
```
### Connecting To Dgraph Cloud
If you want to connect to Dgraph running on [Dgraph Cloud](https://cloud.dgraph.io) instance, then
get the gRPC endpoint of your cluster that you can find in the
[Settings section](https://cloud.dgraph.io/_/settings) of Dgraph Cloud console and obtain a Client
or Admin API key (created in the [API key tab](https://cloud.dgraph.io/_/settings?tab=api-keys)
of the Setting section). Create the `client_stub` using the gRPC endpoint and the API key:
```python3
client_stub = pydgraph.DgraphClientStub.from_cloud(
"https://morning-glade.grpc.us-east-1.aws.cloud.dgraph.io:443", "<api-key>")
client = pydgraph.DgraphClient(client_stub)
```
The `DgraphClientStub.from_slash_endpoint()` method has been removed v23.0.
Please use `DgraphClientStub.from_cloud()` instead.
### Altering the Database
To set the schema, create an `Operation` object, set the schema and pass it to
`DgraphClient#alter(Operation)` method.
```python3
schema = 'name: string @index(exact) .'
op = pydgraph.Operation(schema=schema)
client.alter(op)
```
`Operation` contains other fields as well, including `DropAttr` and `DropAll`. `DropAll` is
useful if you wish to discard all the data, and start from a clean slate, without bringing
the instance down. `DropAttr` is used to drop all the data related to a predicate.
```python3
# Drop all data including schema from the Dgraph instance. This is a useful
# for small examples such as this since it puts Dgraph into a clean state.
op = pydgraph.Operation(drop_all=True)
client.alter(op)
```
Indexes can be computed in the background.
You can set the `run_in_background` field of `pydgraph.Operation` to `True`
before passing it to the `Alter` function. You can find more details
[here](https://docs.dgraph.io/master/query-language/#indexes-in-background).
```python3
schema = 'name: string @index(exact) .'
op = pydgraph.Operation(schema=schema, run_in_background=True)
client.alter(op)
```
### Creating a Transaction
To create a transaction, call the `DgraphClient#txn()` method, which returns a
new `Txn` object. This operation incurs no network overhead.
It is good practice to call `Txn#discard()` in a `finally` block after running
the transaction. Calling `Txn#discard()` after `Txn#commit()` is a no-op
and you can call `Txn#discard()` multiple times with no additional side-effects.
```python3
txn = client.txn()
try:
# Do something here
# ...
finally:
txn.discard()
# ...
```
To create a read-only transaction, call `DgraphClient#txn(read_only=True)`.
Read-only transactions are ideal for transactions which only involve queries.
Mutations and commits are not allowed.
```python3
txn = client.txn(read_only=True)
try:
# Do some queries here
# ...
finally:
txn.discard()
# ...
```
To create a read-only transaction that executes best-effort queries, call
`DgraphClient#txn(read_only=True, best_effort=True)`. Best-effort queries are
faster than normal queries because they bypass the normal consensus protocol.
For this same reason, best-effort queries cannot guarantee to return the latest
data. Best-effort queries are only supported by read-only transactions.
### Running a Mutation
`Txn#mutate(mu=Mutation)` runs a mutation. It takes in a `Mutation` object,
which provides two main ways to set data: JSON and RDF N-Quad. You can choose
whichever way is convenient.
`Txn#mutate()` provides convenience keyword arguments `set_obj` and `del_obj`
for setting JSON values and `set_nquads` and `del_nquads` for setting N-Quad
values. See examples below for usage.
We define a person object to represent a person and use it in a transaction.
```python3
# Create data.
p = { 'name': 'Alice' }
# Run mutation.
txn.mutate(set_obj=p)
# If you want to use a mutation object, use this instead:
# mu = pydgraph.Mutation(set_json=json.dumps(p).encode('utf8'))
# txn.mutate(mu)
# If you want to use N-Quads, use this instead:
# txn.mutate(set_nquads='_:alice <name> "Alice" .')
```
```python3
# Delete data
query = """query all($a: string)
{
all(func: eq(name, $a))
{
uid
}
}"""
variables = {'$a': 'Bob'}
res = txn.query(query, variables=variables)
ppl = json.loads(res.json)
# For a mutation to delete a node, use this:
txn.mutate(del_obj=person)
```
For a complete example with multiple fields and relationships, look at the
[simple project][simple] in the `examples` folder.
Sometimes, you only want to commit a mutation, without querying anything further.
In such cases, you can set the keyword argument `commit_now=True` to indicate
that the mutation must be immediately committed.
A mutation can be executed using `txn.do_request` as well.
```python3
mutation = txn.create_mutation(set_nquads='_:alice <name> "Alice" .')
request = txn.create_request(mutations=[mutation], commit_now=True)
txn.do_request(request)
```
### Running a Query
You can run a query by calling `Txn#query(string)`. You will need to pass in a
[DQL](https://dgraph.io/docs/query-language/) query string. If you want to pass
an additional dictionary of any variables that you might want to set in the query,
call `Txn#query(string, variables=d)` with the variables dictionary `d`.
The query response contains the `json` field, which returns the JSON response.
Let’s run a query with a variable `$a`, deserialize the result from JSON and
print it out:
```python3
# Run query.
query = """query all($a: string) {
all(func: eq(name, $a))
{
name
}
}"""
variables = {'$a': 'Alice'}
res = txn.query(query, variables=variables)
# If not doing a mutation in the same transaction, simply use:
# res = client.txn(read_only=True).query(query, variables=variables)
ppl = json.loads(res.json)
# Print results.
print('Number of people named "Alice": {}'.format(len(ppl['all'])))
for person in ppl['all']:
print(person)
```
This should print:
```console
Number of people named "Alice": 1
Alice
```
You can also use `txn.do_request` function to run the query.
```python3
request = txn.create_request(query=query)
txn.do_request(request)
```
### Query with RDF response
You can get query result as a RDF response by calling `Txn#query(string)` with `resp_format` set
to `RDF`. The response would contain a `rdf` field, which has the RDF encoded result.
**Note:** If you are querying only for `uid` values, use a JSON format response.
```python3
res = txn.query(query, variables=variables, resp_format="RDF")
print(res.rdf)
```
### Running an Upsert: Query + Mutation
The `txn.do_request` function allows you to use upsert blocks. An upsert block
contains one query block and one or more mutation blocks, so it lets you perform
queries and mutations in a single request. Variables defined in the query block
can be used in the mutation blocks using the `uid` and `val` functions
implemented by DQL.
To learn more about upsert blocks, see the
[Upsert Block documentation](https://dgraph.io/docs/mutations/upsert-block/).
```python3
query = """{
u as var(func: eq(name, "Alice"))
}"""
nquad = """
uid(u) <name> "Alice" .
uid(u) <age> "25" .
"""
mutation = txn.create_mutation(set_nquads=nquad)
request = txn.create_request(query=query, mutations=[mutation], commit_now=True)
txn.do_request(request)
```
### Running a Conditional Upsert
The upsert block also allows specifying a conditional mutation block using an `@if` directive.
The mutation is executed only when the specified condition is true. If the condition is false,
the mutation is silently ignored.
See more about Conditional Upserts [here](https://docs.dgraph.io/mutations/#conditional-upsert).
```python3
query = """
{
user as var(func: eq(email, "wrong_email@dgraph.io"))
}
"""
cond = "@if(eq(len(user), 1))"
nquads = """
uid(user) <email> "correct_email@dgraph.io" .
"""
mutation = txn.create_mutation(cond=cond, set_nquads=nquads)
request = txn.create_request(mutations=[mutation], query=query, commit_now=True)
txn.do_request(request)
```
### Committing a Transaction
A transaction can be committed using the `Txn#commit()` method. If your transaction
consist solely of `Txn#query` or `Txn#queryWithVars` calls, and no calls to
`Txn#mutate`, then calling `Txn#commit()` is not necessary.
An error is raised if another transaction(s) modify the same data concurrently that was
modified in the current transaction. It is up to the user to retry transactions
when they fail.
```python3
txn = client.txn()
try:
# ...
# Perform any number of queries and mutations
# ...
# and finally...
txn.commit()
except pydgraph.AbortedError:
# Retry or handle exception.
finally:
# Clean up. Calling this after txn.commit() is a no-op
# and hence safe.
txn.discard()
```
### Cleaning Up Resources
To clean up resources, you have to call `DgraphClientStub#close()` individually for
all the instances of `DgraphClientStub`.
```python3
SERVER_ADDR1 = "localhost:9080"
SERVER_ADDR2 = "localhost:9080"
# Create instances of DgraphClientStub.
stub1 = pydgraph.DgraphClientStub(SERVER_ADDR1)
stub2 = pydgraph.DgraphClientStub(SERVER_ADDR2)
# Create an instance of DgraphClient.
client = pydgraph.DgraphClient(stub1, stub2)
# Use client
...
# Clean up resources by closing all client stubs.
stub1.close()
stub2.close()
```
### Setting Metadata Headers
Metadata headers such as authentication tokens can be set through the metadata of gRPC methods.
Below is an example of how to set a header named "auth-token".
```python3
# The following piece of code shows how one can set metadata with
# auth-token, to allow Alter operation, if the server requires it.
# metadata is a list of arbitrary key-value pairs.
metadata = [("auth-token", "the-auth-token-value")]
dg.alter(op, metadata=metadata)
```
### Setting a timeout
A timeout value representing the number of seconds can be passed to the `login`,
`alter`, `query`, and `mutate` methods using the `timeout` keyword argument.
For example, the following alters the schema with a timeout of ten seconds:
`dg.alter(op, timeout=10)`
### Async methods
The `alter` method in the client has an asynchronous version called
`async_alter`. The async methods return a future. You can directly call the
`result` method on the future. However. The DgraphClient class provides a static
method `handle_alter_future` to handle any possible exception.
```python3
alter_future = self.client.async_alter(pydgraph.Operation(
schema="name: string @index(term) ."))
response = pydgraph.DgraphClient.handle_alter_future(alter_future)
```
The `query` and `mutate` methods int the `Txn` class also have async versions
called `async_query` and `async_mutation` respectively. These functions work
just like `async_alter`.
You can use the `handle_query_future` and `handle_mutate_future` static methods
in the `Txn` class to retrieve the result. A short example is given below:
```python3
txn = client.txn()
query = "query body here"
future = txn.async_query()
response = pydgraph.Txn.handle_query_future(future)
```
Keep in mind that due to the nature of async calls, the async functions cannot
retry the request if the login is invalid. You will have to check for this error
and retry the login (with the function `retry_login` in both the `Txn` and
`Client` classes). A short example is given below:
```python3
client = DgraphClient(client_stubs) # client_stubs is a list of gRPC stubs.
alter_future = client.async_alter()
try:
response = alter_future.result()
except Exception as e:
# You can use this function in the util package to check for JWT
# expired errors.
if pydgraph.util.is_jwt_expired(e):
# retry your request here.
```
## Examples
[tls]: ./examples/tls
[parse_datetime]: ./examples/parse_datetime
- [simple][]: Quickstart example of using pydgraph.
- [tls][]: Quickstart example that uses TLS.
- [parse_datetime]: Demonstration of converting Dgraph's DateTime strings to native python datetime.
## Development
### Setting up environment
There are many ways to set up your local Python environment. We suggest some sane defaults here.
- Use [pyenv](https://github.com/pyenv/pyenv) to manage your Python installations.
- Most recent versions of Python should work, but the version of Python officially supported is located in
`.python-version`
- Create a Python virtual environment using `python -m venv .venv`
- Activate virtual environment via `source .venv/bin/activate`
### Build from source
To build and install pydgraph locally, run
```sh
pip install -e ".[dev]"
```
If you have made changes to the `pydgraph/proto/api.proto` file, you need need
to regenerate the source files generated by Protocol Buffer tools. To do that,
install the [grpcio-tools][grpcio-tools] library and then run the following
command:
[grpcio-tools]: https://pypi.python.org/pypi/grpcio-tools
```sh
python scripts/protogen.py
```
### Running tests
To run the tests in your local machine, run:
```bash
bash scripts/local-test.sh
```
This script assumes dgraph is located on your path. Dgraph release binaries can
be found [here](https://github.com/dgraph-io/dgraph/releases).
The test script also requires that `docker` and `docker compose` are installed on
your machine.
The script will take care of bringing up a Dgraph cluster and bringing it down
after the tests are executed. The script connects to randomly selected ports for
HTTP and gRPC requests to prevent interference with clusters running on the
default port. Docker and docker-compose need to be installed before running the
script. Refer to the official [Docker documentation](https://docs.docker.com/)
for instructions on how to install those packages.
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"description": "# pydgraph\n\nThis is the official Dgraph database client implementation for Python (Python >= v3.7), using [gRPC][grpc].\n\n[grpc]: https://grpc.io/\n\nThis client follows the [Dgraph Go client][goclient] closely.\n\n[goclient]: https://github.com/dgraph-io/dgo\n\nBefore using this client, we highly recommend that you read the\nthe product documentation at [dgraph.io/docs].\n\n[dgraph.io/docs]:https://dgraph.io/docs\n\n## Table of contents\n- [pydgraph](#pydgraph)\n - [Table of contents](#table-of-contents)\n - [Install](#install)\n - [Supported Versions](#supported-versions)\n - [Quickstart](#quickstart)\n - [Using a client](#using-a-client)\n - [Creating a Client](#creating-a-client)\n - [Login into a Namespace](#login-into-a-namespace)\n - [Connecting To Dgraph Cloud](#connecting-to-dgraph-cloud)\n - [Altering the Database](#altering-the-database)\n - [Creating a Transaction](#creating-a-transaction)\n - [Running a Mutation](#running-a-mutation)\n - [Running a Query](#running-a-query)\n - [Query with RDF response](#query-with-rdf-response)\n - [Running an Upsert: Query + Mutation](#running-an-upsert-query--mutation)\n - [Running a Conditional Upsert](#running-a-conditional-upsert)\n - [Committing a Transaction](#committing-a-transaction)\n - [Cleaning Up Resources](#cleaning-up-resources)\n - [Setting Metadata Headers](#setting-metadata-headers)\n - [Setting a timeout](#setting-a-timeout)\n - [Async methods](#async-methods)\n - [Examples](#examples)\n - [Development](#development)\n - [Setting up environment](#setting-up-environment)\n - [Build from source](#build-from-source)\n - [Running tests](#running-tests)\n\n## Install\n\nInstall using pip:\n\n```sh\npip install pydgraph\n```\n\n## Supported Versions\n\nDepending on the version of Dgraph that you are connecting to, you will have to\nuse a different version of this client.\n\n| Dgraph version | pydgraph version |\n|:--------------:|:--------------------:|\n| 20.3.X | *20.3.0* |\n| 20.7.X | *20.7.0* |\n| 20.11.X | *20.7.0* |\n| 21.03.X | *21.3.0* |\n| 22.0.X | *21.3.0* |\n| 23.0.X | *23.0.0* |\n\n## Quickstart\n\nBuild and run the [simple project][simple] in the `examples` folder, which\ncontains an end-to-end example of using the Dgraph python client. For additional details, follow the\ninstructions in the project's [README](./examples/simple/README.md).\n\n[simple]: ./examples/simple\n\n## Using a client\n\n### Creating a Client\n\nYou can initialize a `DgraphClient` object by passing it a list of\n`DgraphClientStub` clients as variadic arguments. Connecting to multiple Dgraph\nservers in the same cluster allows for better distribution of workload.\n\nThe following code snippet shows just one connection.\n\n```python3\nimport pydgraph\n\nclient_stub = pydgraph.DgraphClientStub('localhost:9080')\nclient = pydgraph.DgraphClient(client_stub)\n```\n\n### Login into a Namespace\n\nIf your server has Access Control Lists enabled (Dgraph v1.1 or above), the client must be\nlogged in for accessing data. Use `login` endpoint:\n\nCalling login will obtain and remember the access and refresh JWT tokens. All subsequent operations\nvia the logged in client will send along the stored access token.\n\n```python3\nclient.login(\"groot\", \"password\")\n```\n\nIf your server additionally has namespaces (Dgraph v21.03 or above), use the\n`login_into_namespace` API.\n\n```python3\nclient.login_into_namespace(\"groot\", \"password\", \"123\")\n```\n\n### Connecting To Dgraph Cloud\n\nIf you want to connect to Dgraph running on [Dgraph Cloud](https://cloud.dgraph.io) instance, then\nget the gRPC endpoint of your cluster that you can find in the\n[Settings section](https://cloud.dgraph.io/_/settings) of Dgraph Cloud console and obtain a Client\nor Admin API key (created in the [API key tab](https://cloud.dgraph.io/_/settings?tab=api-keys)\nof the Setting section). Create the `client_stub` using the gRPC endpoint and the API key:\n\n```python3\nclient_stub = pydgraph.DgraphClientStub.from_cloud(\n \"https://morning-glade.grpc.us-east-1.aws.cloud.dgraph.io:443\", \"<api-key>\")\nclient = pydgraph.DgraphClient(client_stub)\n```\n\nThe `DgraphClientStub.from_slash_endpoint()` method has been removed v23.0.\nPlease use `DgraphClientStub.from_cloud()` instead.\n\n### Altering the Database\n\nTo set the schema, create an `Operation` object, set the schema and pass it to\n`DgraphClient#alter(Operation)` method.\n\n```python3\nschema = 'name: string @index(exact) .'\nop = pydgraph.Operation(schema=schema)\nclient.alter(op)\n```\n\n`Operation` contains other fields as well, including `DropAttr` and `DropAll`. `DropAll` is\nuseful if you wish to discard all the data, and start from a clean slate, without bringing\nthe instance down. `DropAttr` is used to drop all the data related to a predicate.\n\n```python3\n# Drop all data including schema from the Dgraph instance. This is a useful\n# for small examples such as this since it puts Dgraph into a clean state.\nop = pydgraph.Operation(drop_all=True)\nclient.alter(op)\n```\n\nIndexes can be computed in the background.\nYou can set the `run_in_background` field of `pydgraph.Operation` to `True`\nbefore passing it to the `Alter` function. You can find more details\n[here](https://docs.dgraph.io/master/query-language/#indexes-in-background).\n\n```python3\nschema = 'name: string @index(exact) .'\nop = pydgraph.Operation(schema=schema, run_in_background=True)\nclient.alter(op)\n```\n\n### Creating a Transaction\n\nTo create a transaction, call the `DgraphClient#txn()` method, which returns a\nnew `Txn` object. This operation incurs no network overhead.\n\nIt is good practice to call `Txn#discard()` in a `finally` block after running\nthe transaction. Calling `Txn#discard()` after `Txn#commit()` is a no-op\nand you can call `Txn#discard()` multiple times with no additional side-effects.\n\n```python3\ntxn = client.txn()\ntry:\n # Do something here\n # ...\nfinally:\n txn.discard()\n # ...\n```\n\nTo create a read-only transaction, call `DgraphClient#txn(read_only=True)`.\nRead-only transactions are ideal for transactions which only involve queries.\nMutations and commits are not allowed.\n\n```python3\ntxn = client.txn(read_only=True)\ntry:\n # Do some queries here\n # ...\nfinally:\n txn.discard()\n # ...\n```\n\nTo create a read-only transaction that executes best-effort queries, call\n`DgraphClient#txn(read_only=True, best_effort=True)`. Best-effort queries are\nfaster than normal queries because they bypass the normal consensus protocol.\nFor this same reason, best-effort queries cannot guarantee to return the latest\ndata. Best-effort queries are only supported by read-only transactions.\n\n### Running a Mutation\n\n`Txn#mutate(mu=Mutation)` runs a mutation. It takes in a `Mutation` object,\nwhich provides two main ways to set data: JSON and RDF N-Quad. You can choose\nwhichever way is convenient.\n\n`Txn#mutate()` provides convenience keyword arguments `set_obj` and `del_obj`\nfor setting JSON values and `set_nquads` and `del_nquads` for setting N-Quad\nvalues. See examples below for usage.\n\nWe define a person object to represent a person and use it in a transaction.\n\n```python3\n# Create data.\np = { 'name': 'Alice' }\n\n# Run mutation.\ntxn.mutate(set_obj=p)\n\n# If you want to use a mutation object, use this instead:\n# mu = pydgraph.Mutation(set_json=json.dumps(p).encode('utf8'))\n# txn.mutate(mu)\n\n# If you want to use N-Quads, use this instead:\n# txn.mutate(set_nquads='_:alice <name> \"Alice\" .')\n```\n\n```python3\n# Delete data\n\nquery = \"\"\"query all($a: string)\n {\n all(func: eq(name, $a))\n {\n uid\n }\n }\"\"\"\nvariables = {'$a': 'Bob'}\n\nres = txn.query(query, variables=variables)\nppl = json.loads(res.json)\n\n# For a mutation to delete a node, use this:\ntxn.mutate(del_obj=person)\n```\n\nFor a complete example with multiple fields and relationships, look at the\n[simple project][simple] in the `examples` folder.\n\nSometimes, you only want to commit a mutation, without querying anything further.\nIn such cases, you can set the keyword argument `commit_now=True` to indicate\nthat the mutation must be immediately committed.\n\nA mutation can be executed using `txn.do_request` as well.\n\n```python3\nmutation = txn.create_mutation(set_nquads='_:alice <name> \"Alice\" .')\nrequest = txn.create_request(mutations=[mutation], commit_now=True)\ntxn.do_request(request)\n```\n\n### Running a Query\n\nYou can run a query by calling `Txn#query(string)`. You will need to pass in a\n[DQL](https://dgraph.io/docs/query-language/) query string. If you want to pass\nan additional dictionary of any variables that you might want to set in the query,\ncall `Txn#query(string, variables=d)` with the variables dictionary `d`.\n\nThe query response contains the `json` field, which returns the JSON response.\nLet\u2019s run a query with a variable `$a`, deserialize the result from JSON and\nprint it out:\n\n```python3\n# Run query.\nquery = \"\"\"query all($a: string) {\n all(func: eq(name, $a))\n {\n name\n }\n}\"\"\"\nvariables = {'$a': 'Alice'}\n\nres = txn.query(query, variables=variables)\n\n# If not doing a mutation in the same transaction, simply use:\n# res = client.txn(read_only=True).query(query, variables=variables)\n\nppl = json.loads(res.json)\n\n# Print results.\nprint('Number of people named \"Alice\": {}'.format(len(ppl['all'])))\nfor person in ppl['all']:\n print(person)\n```\n\nThis should print:\n\n```console\nNumber of people named \"Alice\": 1\nAlice\n```\n\nYou can also use `txn.do_request` function to run the query.\n\n```python3\nrequest = txn.create_request(query=query)\ntxn.do_request(request)\n```\n\n### Query with RDF response\n\nYou can get query result as a RDF response by calling `Txn#query(string)` with `resp_format` set\nto `RDF`. The response would contain a `rdf` field, which has the RDF encoded result.\n\n**Note:** If you are querying only for `uid` values, use a JSON format response.\n\n```python3\nres = txn.query(query, variables=variables, resp_format=\"RDF\")\nprint(res.rdf)\n```\n\n### Running an Upsert: Query + Mutation\n\nThe `txn.do_request` function allows you to use upsert blocks. An upsert block\ncontains one query block and one or more mutation blocks, so it lets you perform\nqueries and mutations in a single request. Variables defined in the query block\ncan be used in the mutation blocks using the `uid` and `val` functions\nimplemented by DQL.\n\nTo learn more about upsert blocks, see the\n[Upsert Block documentation](https://dgraph.io/docs/mutations/upsert-block/).\n\n```python3\nquery = \"\"\"{\n u as var(func: eq(name, \"Alice\"))\n}\"\"\"\n\nnquad = \"\"\"\n uid(u) <name> \"Alice\" .\n uid(u) <age> \"25\" .\n\"\"\"\n\nmutation = txn.create_mutation(set_nquads=nquad)\nrequest = txn.create_request(query=query, mutations=[mutation], commit_now=True)\ntxn.do_request(request)\n```\n\n### Running a Conditional Upsert\n\nThe upsert block also allows specifying a conditional mutation block using an `@if` directive.\nThe mutation is executed only when the specified condition is true. If the condition is false,\nthe mutation is silently ignored.\n\nSee more about Conditional Upserts [here](https://docs.dgraph.io/mutations/#conditional-upsert).\n\n```python3\nquery = \"\"\"\n {\n user as var(func: eq(email, \"wrong_email@dgraph.io\"))\n }\n\"\"\"\n\ncond = \"@if(eq(len(user), 1))\"\nnquads = \"\"\"\n uid(user) <email> \"correct_email@dgraph.io\" .\n\"\"\"\n\nmutation = txn.create_mutation(cond=cond, set_nquads=nquads)\nrequest = txn.create_request(mutations=[mutation], query=query, commit_now=True)\ntxn.do_request(request)\n```\n\n### Committing a Transaction\n\nA transaction can be committed using the `Txn#commit()` method. If your transaction\nconsist solely of `Txn#query` or `Txn#queryWithVars` calls, and no calls to\n`Txn#mutate`, then calling `Txn#commit()` is not necessary.\n\nAn error is raised if another transaction(s) modify the same data concurrently that was\nmodified in the current transaction. It is up to the user to retry transactions\nwhen they fail.\n\n```python3\ntxn = client.txn()\ntry:\n # ...\n # Perform any number of queries and mutations\n # ...\n # and finally...\n txn.commit()\nexcept pydgraph.AbortedError:\n # Retry or handle exception.\nfinally:\n # Clean up. Calling this after txn.commit() is a no-op\n # and hence safe.\n txn.discard()\n```\n\n### Cleaning Up Resources\n\nTo clean up resources, you have to call `DgraphClientStub#close()` individually for\nall the instances of `DgraphClientStub`.\n\n```python3\nSERVER_ADDR1 = \"localhost:9080\"\nSERVER_ADDR2 = \"localhost:9080\"\n\n# Create instances of DgraphClientStub.\nstub1 = pydgraph.DgraphClientStub(SERVER_ADDR1)\nstub2 = pydgraph.DgraphClientStub(SERVER_ADDR2)\n\n# Create an instance of DgraphClient.\nclient = pydgraph.DgraphClient(stub1, stub2)\n\n# Use client\n...\n\n# Clean up resources by closing all client stubs.\nstub1.close()\nstub2.close()\n```\n\n### Setting Metadata Headers\n\nMetadata headers such as authentication tokens can be set through the metadata of gRPC methods.\nBelow is an example of how to set a header named \"auth-token\".\n\n```python3\n# The following piece of code shows how one can set metadata with\n# auth-token, to allow Alter operation, if the server requires it.\n# metadata is a list of arbitrary key-value pairs.\nmetadata = [(\"auth-token\", \"the-auth-token-value\")]\ndg.alter(op, metadata=metadata)\n```\n\n### Setting a timeout\n\nA timeout value representing the number of seconds can be passed to the `login`,\n`alter`, `query`, and `mutate` methods using the `timeout` keyword argument.\n\nFor example, the following alters the schema with a timeout of ten seconds:\n`dg.alter(op, timeout=10)`\n\n### Async methods\n\nThe `alter` method in the client has an asynchronous version called\n`async_alter`. The async methods return a future. You can directly call the\n`result` method on the future. However. The DgraphClient class provides a static\nmethod `handle_alter_future` to handle any possible exception.\n\n```python3\nalter_future = self.client.async_alter(pydgraph.Operation(\n\tschema=\"name: string @index(term) .\"))\nresponse = pydgraph.DgraphClient.handle_alter_future(alter_future)\n```\n\nThe `query` and `mutate` methods int the `Txn` class also have async versions\ncalled `async_query` and `async_mutation` respectively. These functions work\njust like `async_alter`.\n\nYou can use the `handle_query_future` and `handle_mutate_future` static methods\nin the `Txn` class to retrieve the result. A short example is given below:\n\n```python3\ntxn = client.txn()\nquery = \"query body here\"\nfuture = txn.async_query()\nresponse = pydgraph.Txn.handle_query_future(future)\n```\n\nKeep in mind that due to the nature of async calls, the async functions cannot\nretry the request if the login is invalid. You will have to check for this error\nand retry the login (with the function `retry_login` in both the `Txn` and\n`Client` classes). A short example is given below:\n\n```python3\nclient = DgraphClient(client_stubs) # client_stubs is a list of gRPC stubs.\nalter_future = client.async_alter()\ntry:\n response = alter_future.result()\nexcept Exception as e:\n\t# You can use this function in the util package to check for JWT\n # expired errors.\n if pydgraph.util.is_jwt_expired(e):\n # retry your request here.\n```\n\n## Examples\n\n[tls]: ./examples/tls\n[parse_datetime]: ./examples/parse_datetime\n\n- [simple][]: Quickstart example of using pydgraph.\n- [tls][]: Quickstart example that uses TLS.\n- [parse_datetime]: Demonstration of converting Dgraph's DateTime strings to native python datetime.\n\n## Development\n\n### Setting up environment\n\nThere are many ways to set up your local Python environment. We suggest some sane defaults here.\n\n- Use [pyenv](https://github.com/pyenv/pyenv) to manage your Python installations.\n- Most recent versions of Python should work, but the version of Python officially supported is located in\n`.python-version`\n- Create a Python virtual environment using `python -m venv .venv`\n- Activate virtual environment via `source .venv/bin/activate`\n\n### Build from source\n\nTo build and install pydgraph locally, run\n\n```sh\npip install -e \".[dev]\"\n```\n\nIf you have made changes to the `pydgraph/proto/api.proto` file, you need need\nto regenerate the source files generated by Protocol Buffer tools. To do that,\ninstall the [grpcio-tools][grpcio-tools] library and then run the following\ncommand:\n\n[grpcio-tools]: https://pypi.python.org/pypi/grpcio-tools\n\n```sh\npython scripts/protogen.py\n```\n\n### Running tests\n\nTo run the tests in your local machine, run:\n\n```bash\nbash scripts/local-test.sh\n```\n\nThis script assumes dgraph is located on your path. Dgraph release binaries can\nbe found [here](https://github.com/dgraph-io/dgraph/releases).\nThe test script also requires that `docker` and `docker compose` are installed on\nyour machine.\n\nThe script will take care of bringing up a Dgraph cluster and bringing it down\nafter the tests are executed. The script connects to randomly selected ports for\nHTTP and gRPC requests to prevent interference with clusters running on the\ndefault port. Docker and docker-compose need to be installed before running the\nscript. Refer to the official [Docker documentation](https://docs.docker.com/)\nfor instructions on how to install those packages.\n",
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