![]() GeoJSON is implemented as a set of Geo functions that are natively available to AQL. What this means is, we can query on geospatial data and build things like map views. GeoJSONĪrangoDB out of the box supports GeoJSON which is based on Google’s S2 computation spherical geometry system. I love the aspect of their training because they made it fun but also tangible, beyond a hello world. Without the index the query took ~500mS and with a persistent index (Hash) based on the TailNumber field, the query ran at ~2.4mS, which is an incredible gain, given there are some 280,000+ records. For the tailnumber example in the training material, the speed up was noticeable. The persistent index is actually what they refer to as the Hash index when running on RocksDB.Īdding an Index can speed things up a lot. There are numerous types of index available for you to create and on pay attention to the database engine being used. Naturally, this doesn’t apply if you sort the data and explicitly change the order.ĪrangoDB has default indexes on _key in a collection and on _from and _to on an edge collection. Thankfully, ArangoDB holds true to ordered state between load and extractions! Here’s an example of ordered data in a document in a collection. The order of them absolutely matters and in projects, I’ve gone to great lengths, like encoding data into TLVs with mechanisms to ensure the integrity and order of this data remains intact between ETL operations. An example would be firewall rules on Junos. One of the things I come across regularly, is ordered state held in JSON. Some might have uplink ports, some might not at all. They might have different model numbers and different attributes altogether. In networking, I might assign a group of leaf switches in a given data hall to a collection. So what is similar? Well, it’s whatever you decide. The mental thorn that sprang the question was the ability to group similar documents in a collection. ArangoDB is therefore schemaless just like Neo4J. The important thing is, I can include additional information when required, without having change a schema. Regarding the above, integrity checks will require conditionals to detect the presence of odd fields that vary in their presence before using them in loop or aggregate based queries. Outliers are always present when modelling things and it’s important therefore we have some flexibility in the tools we use for modelling and querying them. RETURN Īnd the projected data from the query. ![]() Note, no authentication is set here so please don’t run this in production. If you wanted to run this on Docker, here’s how to get going super-fast. ![]()
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