Getting your data connected is usually the first step in working with the CARTO platform. That data might be in files, databases, URLs, services, etc. The options are numerous, so we offer a wide range of connectivity capabilities to different sources—each coming with its own benefits and tradeoffs.
At the end of the day, every dataset connected to CARTO gets represented in the form of a table, but these come in distinct flavors:
This process makes CARTO very performant with our internal database. But what happens when you can’t or you don’t want to cache the data in CARTO?
Technically, a Federated Table is a PostgreSQL Foreign Data Wrapper to a remote server, allowing us to perform live queries to a remote database. Think of it as a virtual table that looks like a regular sync table in CARTO — but when used, makes the queries you perform in CARTO travel to the remote database and get executed there.
Use cases for federated tables include:
So technically this means that when you do a query to make a choropleth map:
SELECT geog_data.the_geom_webmercator, fdw_data.lotarea FROM fdw.mappluto_18v2_1 as fdw_data INNER JOIN public.mappluto_18v2_1 as geog_data ON fdw_data.cartodb_id = geog_data.cartodb_id WHERE fdw_data.cartodb_id <20
CARTO will push the filters to the federated database, executing for example:
SELECT cartodb_id, lotarea FROM public.mappluto_18v2_1 WHERE ((cartodb_id < 20))
And then join locally the data inside CARTO and execute any spatial operations that could not be sent to the federated database.
But wait, there’s more! We have extended the capabilities of Foreign Data Wrapper to push spatial aggregations and filters down to the remote server. That of course requires the usage of a remote server that has PostGIS capabilities (which Aurora, Citus, AWS RDS, and Google Cloud SQL does!). In that case when you run a query like the following to retrieve a vector tile:
SELECT ST_AsMVT(rows.*) mvt FROM ( SELECT ST_AsMVTGeom(the_geom_webmercator,TileBBox(13,2412,3079)) as geom, numfloors FROM fdw.mappluto_18v2_1 WHERE the_geom_webmercator && TileBBox(13,2412,3079) ) rows;
The remote server will run the following SQL:
SELECT the_geom_webmercator, numfloors FROM public.mappluto_18v2_1 WHERE ((the_geom_webmercator OPERATOR(public.&&) $1::public.geometry))
Which means that we have pushed the spatial filter down to the remote server, and the query took ~15 seconds to run, which is too slow for a tile request.
We can break down the time the query took into 3 steps:
In total, 3200 KB were transferred from the remote to the local server.
With our implementation running the same query, the remote server will run ALSO the spatial aggregation defined by ST_AsMVT:
SELECT public.st_asmvt(q) FROM ( SELECT public.st_asmvtgeom(the_geom_webmercator, 'BOX(-8238077.15931641 4970241.32652345,-8233185.18950684 4975133.29633302)'::public.box2d, 4096, 256, true), numfloors FROM carto_lite.mappluto_18v2_1 WHERE ((the_geom_webmercator OPERATOR(public.&&) $1::public.geometry)) ) q
And that query now runs in 840 miliseconds. That’s 17x faster!
In this scenario, 285 KB were transferred between both servers.
As you can see, by pushing down the filter and the aggregation to the remote server, we only send the minimum data between servers, speeding up the federated queries tremendously. Of course there will always be latency for running a query between different cloud providers (Google Cloud and AWS in this case), and locations, but if servers are close by, and ideally on the same cloud provider, latency shouldn’t be an issue.
We have worked hard to ensure that the maximum possible set of filters and aggregations get passed through to the original database, to reduce the payload between servers and take advantage of the capabilities of the remote database. Still, there are some cases where tuning will be necessary, and we will be there to help you with non performant queries and keep pushing the limits of federated tables. Talk to us!
Right now we have implemented, and heavily optimized, this functionality to connect to the rich PostgreSQL ecosystem, like:
Depending on the destination database, the performance of spatial operations might be different because of their version of PostGIS (the spatial extension of PostgreSQL). We will follow with more specific measurements in follow-ups blog posts.
We have plans to add support to other databases including SAP Hana, Oracle, SQL Server, and Cloud Spanner, so if you are interested in any of those please let us know!
We are glad you are interested! We would love to talk to you about activating this functionality on your CARTO account, or discuss your connectivity needs to other databases and warehouses.
Get in touch!Let us know what your connectivity needs are
Exciting news for Snowflake users! As part of our commitment to enabling geospatial analysis natively within the cloud, today we are announcing the launch of our Spatial Ex...News
The demands on Spatial Data Scientists and Developers to produce ever more complex data visualizations and geospatial apps within shorter timeframes are constantly increasi...News
Last year, Google Cloud announced a new initiative bring partner solutions to the network edge, enabling a wide range of clients in industries such as CPG, Retail, and Manu...News
Please fill out the below form and we'll be in touch real soon.