prosto VS spyql

> Joins are what makes relational modeling interesting!

It is the central part of RM which is difficult to model using other methods and which requires high expertise in non-trivial use cases. One alternative to how multiple tables can be analyzed without joins is proposed in the concept-oriented model [1] which relies on two equal modeling constructs: sets (like RM) and functions. In particular, it is implemented in the Prosto data processing toolkit [2] and its Column-SQL language. The idea is that links between tables are used instead of joins. A link is formally a function from one set to another set.

[1] Joins vs. Links or Relational Join Considered Harmful https://www.researchgate.net/publication/301764816_Joins_vs_...

[2] https://github.com/asavinov/prosto data processing toolkit radically changing how data is processed by heavily relying on functions and operations with functions - an alternative to map-reduce and join-groupby

One idea is to use columns instead of cells. Each column has a definition in terms of other columns which might also be defined in terms of other columns. If you change value(s) in some source column then these changes will propagate through the graph of these column definitions. Some fragments of this general idea were implemented in different systems, for example, Power BI or Airtable.

This approach was formalized in the concept-oriented model of data which relies on two basic elements: mathematical functions and mathematical sets. In contrast, most traditional data models rely on only sets. Functions are implemented as columns. The main difficulty in any formalization is how to deal with columns in multiple tables.

This approach was implemented in the Prosto data processing toolkit: https://github.com/asavinov/prosto

Having Python expressions within a declarative language is a really good idea because we can combine low level logic of computations of values with high level logic of set processing.

A similar approach is implemented in the Prosto data processing toolkit:

https://github.com/asavinov/prosto

Although Prosto is viewed as an alternative to Map-Reduce by relying on functions, it also supports Python User-Defined Functions in its Column-SQL:

Hamilton is more similar to the Prosto data processing toolkit which also relies on column operations defined via Python functions:

https://github.com/asavinov/prosto

However, Prosto allows for data processing via column operations in many tables (implemented as pandas data frames) by providing a column-oriented equivalents for joins and groupby (hence it has no joins and no groupbys which are known to be quite difficult and require high expertise).

Prosto also provides Column-SQL which might be simpler and more natural in many use cases.

The whole approach is based on the concept-oriented model of data which makes functions first-class elements of the model as opposed to having only sets in the relational model.

One alternative to SQL (type of thinking) is Column-SQL [1] which is based on a new data model. This model is relies on two equal constructs: sets (tables) and functions (columns). It is opposed to the relational algebra which is based on only sets and set operations. One benefit of Column-SQL is that it does not use joins and group-by for connectivity and aggregation, respectively, which are known to be quite difficult to understand and error prone in use. Instead, many typical data processing patterns are implemented by defining new columns: link columns instead of join, and aggregate columns instead of group-by.

More details about "Why functions and column-orientation" (as opposed to sets) can be found in [2]. Shortly, problems with set-orientation and SQL are because producing sets is not what we frequently need - we need new columns and not new table. And hence applying set operations is a kind of workaround due the absence of column operations.

This approach is implemented in the Prosto data processing toolkit [0] and Column-SQL[1] is a syntactic way to define its operations.

[0] https://github.com/asavinov/prosto Prosto is a data processing toolkit - an alternative to map-reduce and join-groupby

[1] https://prosto.readthedocs.io/en/latest/text/column-sql.html Column-SQL (work in progress)

[2] https://prosto.readthedocs.io/en/latest/text/why.html Why functions and column-orientation?

> This is closer to the way that humans perceive the world — mapping between whatever aspect of external reality you are interested in and the data model is an order of magnitude easier than with relational databases.

One approach to modeling data based on mappings (mathematical functions) is the concept-oriented model [1] implemented in [2]. Its main feature is that it gets rid of joins, groupby and map-reduce by manipulating data using operations with functions (mappings).

> Everything is pre-joined — you don’t have to disassemble objects into normalised tables and reassemble them with joins.

One old related general idea is to assume the existence of universal relation. Such an approach is referred to as the universal relation model (URM) [3, 4].

[1] A. Savinov, Concept-oriented model: Modeling and processing data using functions, Eprint: arXiv:1911.07225 [cs.DB], 2019 https://www.researchgate.net/publication/337336089_Concept-o...

[2] https://github.com/asavinov/prosto Prosto Data Processing Toolkit: No join-groupby, No map-reduce

[3] https://en.wikipedia.org/wiki/Universal_relation_assumption

[4] R. Fagin, A.O. Mendelzon and J.D. Ullman, A Simplified Universal Relation Assumption and Its Properties. ACM Trans. Database Syst., 7(3), 343-360 (1982).

(Currently, it is not actively developed and the focus is moved to a similar project - https://github.com/asavinov/prosto - also focused on data preprocessing and feature engineering)

I prefer a SQL-like format. It’s not as complete but it cover most of the day-to-day use cases. Take a look at https://github.com/dcmoura/spyql (I am the author). Congrats on fq!

SPyQL documentation: spyql.readthedocs.io

spyql – Query data on the command line with SQL-like SELECTs powered by Python expressions

This is great!

I am the author of SPyQL [1]. Combining JC with SPyQL you can easily query the json output and run python commands on top of it from the command-line :-) You can do aggregations and so forth in a much simpler and intuitive way than with jq.

I just wrote a blogpost [2] that illustrates it. It is more focused on CSV, but the commands would be the same if you were working with JSON.

[1] https://github.com/dcmoura/spyql

You can import the data into a PostgreSQL/MySQL/SQLite/... database and then query the database. However, even with the right choice of indexes, it might take a while to run queries on a table with hundreds of millions of records. You can easily import your data to these databases with SpyQL: $ spyql "SELECT * FROM csv TO sql(table=my_table_name) | sqlite3 my.db" (you would need to create the table my_table_name before running the command).

https://github.com/dcmoura/spyql/blob/master/notebooks/json_...

And ClickHouse looks like a normal relational database - there is no need for multiple components for different tiers (like in Druid), no need for manual partitioning into "daily", "hourly" tables (like you do in Spark and Bigquery), no need for lambda architecture... It's refreshing how something can be both simple and fast.

I thought this library was pretty nifty for json. It's also relatively fast compared to most json parsers: https://github.com/dcmoura/spyql