lidavidm commented on code in PR #248:
URL: https://github.com/apache/arrow-site/pull/248#discussion_r1050968356
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_posts/2022-12-31-arrow-adbc.md:
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+---
+layout: post
+title: "Introducing ADBC: Database Access for Apache Arrow"
+date: "2022-12-31 00:00:00"
+author: pmc
+categories: [application]
+---
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+
+The Arrow community would like to introduce version 1.0.0 of the [Arrow
Database Connectivity (ADBC)][adbc] specification.
+**ADBC aims to be an columnar, minimal-overhead alternative JDBC/ODBC for
analytical applications**.
+It defines vendor-agnostic and Arrow-based APIs for common database tasks,
like executing queries and getting basic metadata.
+These APIs are available, either directly or via bindings, in C/C++, Go, Java,
Python, Ruby, and soon R.
+
+With ADBC, developers get both the benefits of using columnar Arrow data and
having generic API abstractions.
+Like [JDBC][jdbc]/[ODBC][odbc], ADBC defines database-independent interaction
APIs, and relies on drivers to implement those APIs for particular databases.
+ADBC aims to bring all of these together under a single API:
+
+- Vendor-specific Arrow-native protocols, like [Arrow Flight SQL][flight-sql]
or those offered by ClickHouse or Google BigQuery;
+- Non-columnar protocols, like the PostgreSQL wire format;
+- Non-columnar API abstractions, like JDBC/ODBC.
+
+In other words: **ADBC is a single API for getting Arrow data in and out of
databases**.
+Underneath, ADBC driver implementations take care of bridging the actual
system:
+
+- Databases with Arrow-native protocols can directly pass data through without
conversion.
+- Otherwise, drivers can be built for specific row-based protocols, optimizing
conversions to and from Arrow data as best as possible for particular databases.
+- As a fallback, drivers can be built that convert data from JDBC/ODBC,
bridging existing databases into an Arrow-native API.
+
+In all cases, the application is saved the trouble of wrapping APIs and doing
data conversions.
+
+## Motivation
+
+Applications often use API standards like JDBC and ODBC to work with databases.
+This lets them use the same API regardless of the underlying database, saving
on development time.
+Roughly speaking, when an application executes a query with these APIs:
+
+1. The application submits a SQL query via the JDBC/ODBC APIs.
+2. The query is passed on to the driver.
+3. The driver translates the query to a database-specific protocol and sends
it to the database.
+4. The database executes the query and returns the result set in a
database-specific format.
+5. The driver translates the result format into the JDBC/ODBC API.
+
+<figure style="text-align: center;">
+ <img src="{{ site.baseurl }}/img/ADBCFlow1.svg" width="90%"
class="img-responsive" alt="A diagram showing the query execution flow.">
+ <figcaption>The query execution flow.</figcaption>
+</figure>
+
+When columnar data comes into play, however, problems arise.
+JDBC is a row-oriented API, and while ODBC can support columnar data, the type
system and data representation is not a perfect match with Arrow.
+In both cases, this leads to data conversions around steps 4–5, spending
resources without performing "useful" work.
+
+This mismatch is important for columnar database systems, such as ClickHouse,
Dremio, DuckDB, Google BigQuery, and others.
+Clients, such as Apache Spark and pandas, would like to get columnar data
directly from these systems.
+Meanwhile, traditional database systems aren't going away, and these clients
still want to consume data from them.
+
+In response, we've seen a few solutions:
+
+- *Just provide JDBC/ODBC drivers*.
+ These standards are here to stay, and it makes sense to provide these
interfaces for applications that want them.
+ But if both sides are columnar, that means converting data into rows for
JDBC/ODBC, only for the client to convert them back into columns!
+- *Provide converters from JDBC/ODBC to Arrow*.
+ Some examples include [Turbodbc][turbodbc] and [arrow-jdbc][arrow-jdbc].
+ This approach reduces the burden on client applications, but doesn't
fundamentally solve the problem.
+ Unnecessary data conversions are still required.
+- *Provide special SDKs*.
+ All of the columnar systems listed above do offer ways to get Arrow data,
such as via [Arrow Flight SQL][flight-sql].
+ But client applications need to spend time to integrate with each of them.
+ (Just look at all the
[connectors](https://trino.io/docs/current/connector.html) that Trino
implements.)
+ And not every system offers this option.
+
+ADBC combines the advantages of the latter two solutions under one API.
+In other words, ADBC provides a set of API definitions that client
applications code to.
+These API definitions are Arrow based.
+The application then links to or loads drivers for the actual database, that
implement the API definitions.
+If the database is Arrow-native, the driver can just pass the data through
without conversion.
+Otherwise, the driver converts the data to Arrow format first.
+
+<figure style="text-align: center;">
+ <img src="{{ site.baseurl }}/img/ADBCFlow2.svg" alt="A diagram showing the
query execution flow with ADBC." width="90%" class="img-responsive">
+ <figcaption>The query execution flow with two different ADBC
drivers.</figcaption>
+</figure>
+
+1. The application submits a SQL query via the ADBC APIs.
+2. The query is passed on to the ADBC driver.
+3. The driver translates the query to a database-specific protocol and sends
the query to the database.
+4. The database executes the query and returns the result set in a
database-specific format, which is ideally Arrow data.
+5. The driver translates the result format into Arrow data if needed.
+
+So either way, the client can use a single API and get Arrow data in all cases.
+
+## Examples
+
+*Note: implementations are still under development, separate from the API
standard itself. Examples are subject to change.*
+
+We can get Arrow data out of a Postgres database in a few lines of Python:
+
+```python
+import adbc_driver_postgresql.dbapi
+
+uri = "postgresql://localhost:5432/postgres?user=postgres&password=password"
+with adbc_driver_postgresql.dbapi.connect(uri) as conn:
+ with conn.cursor() as cur:
+ cur.execute("SELECT * FROM customer")
+ table = cur.fetch_arrow_table()
+ # Process the results
+```
+
+The ADBC Python packages offer a familiar [DBAPI 2.0 (PEP 249)][pep-249]-style
API, along with extensions to get Arrow data.
+
+In Java, we can pull Arrow data out of a JDBC connection.
+The ADBC driver takes care of converting the data for the application:
+
+```java
+final Map<String, Object> parameters = new HashMap<>();
+parameters.put(
+ AdbcDriver.PARAM_URL,
+
"jdbc:postgresql://localhost:5432/postgres?user=postgres&password=password");
+try (final AdbcDatabase database = JdbcDriver.INSTANCE.open(parameters);
+ final AdbcConnection connection = database.connect();
+ final AdbcStatement stmt = connection.createStatement()) {
+ stmt.setSqlQuery("SELECT * FROM " + tableName);
+ try (AdbcStatement.QueryResult queryResult = stmt.executeQuery()) {
+ while (queryResult.getReader().loadNextBatch()) {
+ // Process the results
+ }
+ }
+}
+```
+
+## In More Detail
+
+As mentioned, ADBC is an API standard.
+You can read the [C/C++ header][adbc.h], [Go interfaces][adbc-go], and the
[Java interfaces][adbc-core] for yourself.
+The project has built several libraries around this standard:
+
+- Driver implementations implement the API standard on top of a particular
protocol or vendor.
+ See the drivers for [libpq (PostgreSQL)][adbc-libpq] and [JDBC][adbc-jdbc].
+- The driver manager library provides an easy way to use multiple drivers from
a single program, and also load drivers at runtime if desired.
+
+This infrastructure is reusable by other drivers/vendors and accessible to
other languages.
+For example, the Python bindings shown above are actually generic Python
bindings for any ADBC driver, courtesy of the driver manager.
+
+ADBC builds on several existing Arrow projects.
+
+- The [Arrow C Data Interface][c-data-interface] provides a cross-language way
to pass Arrow data between ADBC implementations and ADBC users.
+- [Flight SQL][flight-sql] is used to implement an ADBC driver, making Flight
SQL accessible to languages like Python.
+- [Nanoarrow][nanoarrow] is used to implement some ADBC drivers, making them
lightweight and independent of the core Arrow libraries.
+
+Also, ADBC provides API support for the emerging [Substrait][substrait]
specification.
+Applications can pass queries either as strings (of SQL) or binary blobs (of
serialized Substrait plans).
+
+## What about {Flight RPC, Flight SQL, JDBC, ODBC, …}?
+
+ADBC fills a specific niche that related projects do not address. It is both:
+
+- **Arrow-native**: ADBC can pass through Arrow data with no overhead thanks
to the [C Data Interface][c-data-interface].
+ JDBC is row-oriented, and ODBC has implementation caveats, as discussed,
that make it hard to use with Arrow.
+- **Vendor-agnostic**: ADBC drivers can implement the API using any underlying
protocol, while Flight SQL requires server-side support that may not be easy to
add.
+
+ADBC is both vendor-agnostic and columnar, while Flight SQL is
vendor-specific, and JDBC/ODBC are row-oriented:
Review Comment:
Updated.
Also, tweaked wording below the table to emphasize the API vs protocol
distinction behind ADBC/Flight SQL.
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