NoSQL: No-Tation

This is too good to pass as there is a ton of truth in this simple presentation below. It is cited from”no-tation” without modification:


What Flavor of SQL?

The question now is: what flavor of SQL is the SQL in “No, SQL!”?

One alternative is to start from scratch and invent a SQL grammar and semantics that is not based on the existing SQL standard and SQL implementations. That would not be my choice at all, for the obvious reasons.

Another alternative is to treat JSON ‘just’ as a data structure and embed it into existing SQL. This would be roughly the equivalent of the object/relational model (~NF2) with the variation that there is no global schema and some non-standard SQL types.

The (in my opinion) better alternative is to look at JSON as more than just a data structure and combine JSON with the schema-per-document paradigm as the underlying philosophy.

  • This would be a real game changer as it would cater to the dynamic changing JSON data structures as needed in many projects.
  • As a result, the schema-per-document model would be integrated with the relational model so that a single database management system can support several data models and their paradigms concurrently.

Extending SQL Semantics

Supporting a schema-per-document philosophy requires extending the SQL semantics and its operators to be able to deal with the fact that different JSON structures might have a different schema, even if they are in the same collection or relational table.

Operators like typeOf() come to mind that can test if a path into a JSON structure refers to a specific JSON type or JSON literal name. Another operator is exists() to test if a path into a JSON structure exists. Or array operators that can determine if an array contains a specific JSON structure as an element. containsPath() is an operator that checks if a JSON structure has the path given.

Many more operators can be envisioned to make managing and querying JSON structures in context of SQL expressive and useful.



The views expressed on this blog are my own and do not necessarily reflect the views of Oracle.



Negation and Complement in a Multi-Schema World

Negation and complement are not so straight forward in a multi-schema world where different documents in a collection follow different schemas. Let’s start with an example.

Example: Count()

>{"a":"value", "b":"v1"})
>{"a":"value", "b":null})

Now let’s count how many documents are there with ‘b’ having a value of either ‘null’ or not ‘null’.

>{"$or":[{"b":null}, {"b":{$ne:null}}]}).count()

Is 3 the correct result?

Basing the query on $type instead results into the same count:

>{"$or":[{"b":{$type:10}}, {"b":{$not:{$type:10}}}]}).count()

Is that a correct count?

Interpretation of ‘null’

The query system of MongoDB interprets a query for a property with value ‘null’ in a specific way: if the property is present, then it is checked if the property has the value ‘null’. If the property is not present, then it is interpreted as being present with the value of ‘null’.

In this interpretation, the above query results are correct.

Alternative Interpretation of ‘null’

If only those documents should be considered that actually have the property present, the queries have to be extended to check for that.

>{"$and":[{"b":{$exists:true}}, {"$or":[{"b":null}, {"b":{$ne:null}}]}]}).count()


>{"$and":[{"b":{$exists:true}}, {"$or":[{"b":{"$type":10}}, {"b":{$not:{"$type": 10}}}]}]}).count()

In order to change this interpretation an additional condition is added that checks for the existence of the property being queried. Luckily, the desired interpretation was possible through changing the query without requiring and code.

Looking Beyond ‘null’

Investigating the behavior around ‘null’ was a relatively small effort as it is quite compact. However, with additional operators like ‘$lt’, ‘$gt’, etc., more situations with specific interpretations can be found and have to be carefully planned through when implementing.

There are three basic rules

  • If every document has the same schema (meaning, the same properties with the same names and types), then queries can be written without caveats wrt. multi-schema collections.
  • If documents have different schemas in the sense the some contain a property and some do not contain that property, then queries should be written in such a way that the absence of a property does not cause a wrong result or a wrong interpretation.
  • The same is true for documents that have the same properties, but different types (like the property is sometimes a string and sometimes a number). In this case the queries should be written in such a way that the different types do not lead to a wrong interpretation of the result.

A combination of three cases is possible, meaning, that, for example, all documents have a property ‘p1’ of the same type, a property ‘p2’ that is sometimes not present and a property ‘p3’ that is sometimes not present and sometimes is of different types.

The only alternative to being really careful with the query construction and interpretation is to transform all documents so that they all follow the same schema (global schema for that collection). The particular context and situation determines what the best approach is.

So what’s the Big Deal?

The issues around queries in a multi-schema collection are probably less of a big deal if you are in complete control of your code. In this case you can make sure yourself that the correct interpretation is followed everywhere (by encapsulation, constraint checking, code review or mandate).

However, if you are using libraries as-is and you do not have the ability to change them, you need to make sure that their internal interpretation matches yours. If not, errors can sneak easily into your code base.

On top, sometimes the result determined by code are checked by running queries on the command line or shell. It is not always obvious if e.g. an ‘$exists’ was applied in the code or not. So the ‘checking’ of results using the command-line has its own challenges.

Schema Derivation: Schema-less vs. Schema-based Documents

In the world of document-oriented databases there is the notion that documents are ‘schema-less’ because no schema has to be defined and registered with the database before documents can be stored. Sometimes this is also referred to as ‘schema-free’. Let’s investigate this further: does ‘schema-free’ really exist?


In MongoDB, for example, documents use the JSON ( structure (with an extended set of types as defined in BSON So all document in the database follow the same underlying data type structure. The database will reject a document that is not JSON/BSON compliant.

Schema Enforcement vs. Schema Freedom

In a relational database the notion of schema means that the database knows about the schema (e.g. structure of a table) and all data stored must follow that structure. The database management system is the enforcing entity of the schema. Data not following the schema will be rejected.

In a document-oriented system like MongoDB the notion of schema is not known to the database and therefore not enforced by the database. Any document can have any structure as long as it is valid according to JSON.

Explicit Schema vs. Implicit Schema

In the case of relational databases, the schema is explicit as it is possible to retrieve the schema as a separate data structure from the database. Relational databases have system tables that contain their schema. So the database is aware of the schema in such a way that it can be retrieved.

In a document-oriented database it is not possible to inquire the schema as it has not been defined to the database. However, it could be the case that all documents follow a schema (that is defined outside the database). If this is the case, the schema of the documents can be called an implicit schema. It is not necessary for the database to enforce a schema for the documents to comply to one.

Schema-less, Schema-free

The fact that a document-oriented database does not enforce a schema of its documents does not mean that there is no schema. It only means that the database does not enforce it. However, it could be enforced elsewhere. For example, the software that writes the documents can enforce their schema. Or the software that reads the documents enforces the schema by only accepting those documents that comply to its expectations.

But what if a user manually defines a documents and stores it?

Individual Schema vs. Global Schema

A different viewpoint is possible. If a user stores a manually designed document then it is possible to view this document of having an individual schema. It is the only one with that schema. If the user now adds a second document that has the exact same structure, then it is possible to view these as two documents with the same implicit schema. At this point there are two documents following the same (implicit) schema. It got a bit more global, tough, as now two documents are following it. If all documents follow the same (implicit) schema, then there is an (implicit) global schema.

Different documents can follow different (implicit) schemas. In the extreme case, all documents are different from each other in their structure and each has its own schema.

Schema Derivation

JSON is a construction principle for hierarchical data structures. Each document following the JSON model is hierarchical and finite as JSON does not support (cyclic) references.

Based on the hierarchical and finite structure of JSON it is possible to derive the implicit schema of the JSON documents and make it explicit. On a very high level, the schema derivation algorithm iterates over all documents in the database and determines the schema for each of it. Documents that follow the same schema are grouped together. After all documents have been inspected, there will be one (and possibly more) schema and the associated complying documents.

Details of this algorithm will be discussed in a different blog at some later time.


Documents have a schema, there are not schema-less or schema-free. The schema of a document is implicit (and can be made explicit). Several documents can follow the same schema. While the database is not enforcing the schema, it could be enforced by a separate layer outside the database.

So, not-enforcement of a schema by the database != schema-less/schema-free.