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//! Our "interchange" format for database table schemas. //! //! To convert table schemas between different databases, we have a choice: //! //! 1. We can convert between each pair of schema formats directly, which would //! require `2*n*(n-1)` conversions for `n` databases. //! 2. We can define an "interchange" format, and then build `n` input //! conversions and `n` output conversions. This is much simpler. //! //! A good interchange format should be rich enough to include the most common //! database types, including not just obvious things like text and integers, //! but also things like timestamps and geodata. But a good interchange format //! should also be as simple as possible, omitting details that generally don't //! translate well. //! //! Inevitably, this means that we're going to wind up with a subjective and //! opinionated design. //! //! We define our format using Rust data structures, which are serialized and //! deserialized using [`serde`](https://serde.rs/). //! //! ``` //! use dbcrossbarlib::schema::Table; //! use serde_json; //! //! let json = r#" //! { //! "name": "example", //! "columns": [ //! { "name": "a", "is_nullable": true, "data_type": "text" }, //! { "name": "b", "is_nullable": true, "data_type": "int32" }, //! { "name": "c", "is_nullable": false, "data_type": "uuid" }, //! { "name": "d", "is_nullable": true, "data_type": "date" }, //! { "name": "e", "is_nullable": true, "data_type": "float64" }, //! { "name": "f", "is_nullable": true, "data_type": { "array": "text" } }, //! { "name": "h", "is_nullable": true, "data_type": { "geo_json": 4326 } } //! ] //! } //! "#; //! //! let table: Table = serde_json::from_str(json).expect("could not parse JSON"); //! ``` use serde_derive::{Deserialize, Serialize}; #[cfg(test)] use serde_json::json; use std::fmt; /// Information about a table. /// /// This is the "top level" of our JSON schema format. #[derive(Clone, Debug, Deserialize, Eq, PartialEq, Serialize)] pub struct Table { /// The name of the table. pub name: String, /// Information about the table's columns. pub columns: Vec<Column>, } /// Information about a column. #[derive(Clone, Debug, Deserialize, Eq, PartialEq, Serialize)] pub struct Column { /// The name of the column. pub name: String, /// Can this column be `NULL`? pub is_nullable: bool, /// The data type of this column. pub data_type: DataType, /// An optional comment associated with this column. #[serde(default, skip_serializing_if = "Option::is_none")] pub comment: Option<String>, } /// The data type of a column. /// /// This is a rather interesting type: It only exists to provide a reasonable /// set of "interchange" types, that we might want to preserve when moving from /// on database to another. So it's less precise than PostgreSQL's built-in /// types, but more precise than BigQuery's built-in types. It exists to be a /// "happy medium"--every output driver should be able to understand every one /// of these types meaningfully, and it should almost always be able to map it /// to something in the local database. /// /// Essentially, this fulfills a similar role to the standard JSON types /// (number, string, array, map, boolean, etc.). It's an interchange format. /// It's not supposed to cover every imaginable type. But it should at least /// cover common, generic types that make sense to many database backends. /// /// We represent this as a Rust `enum`, and not a class hierarchy, because: /// /// 1. Class hierarchies provide an extensible set of _types_ (subclasses), but /// a closed set of _operations_ (instance methods on the root class). /// 2. Rust `enum`s provide a closed set of _types_ (`enum` variants), but an /// open set of operations (`match` statements matching each possible /// variant). /// /// In this case, we will extend and change our set of _operations_ regularly, /// as we add new input and output filters. But we will only change the possible /// data types after careful deliberation. So `enum` is the better choice here. #[derive(Clone, Debug, Deserialize, Eq, PartialEq, Serialize)] #[serde(rename_all = "snake_case")] pub enum DataType { /// An array of another data type. For many output formats, it may not be /// possible to nest arrays. Array(Box<DataType>), /// A boolean value. Bool, /// A date, with no associated time value. Date, /// A decimal integer (can represent currency, etc., without rounding /// errors). Decimal, /// 4-byte float. Float32, /// 8-byte float. Float64, /// Geodata in GeoJSON format, using the specified SRID. GeoJson(Srid), /// 2-byte int. Int16, /// 4-byte integer. Int32, /// 8-byte integer. Int64, /// JSON data. This includes both Postgres `json` and `jsonb` types, the /// differences between which don't usually matter when converting schemas. Json, /// A data type which isn't in this list. Other(String), /// A text type. Text, /// A timestamp with no timezone. Ideally, this will would be in UTC, and /// some systems like BigQuery may automatically assume that. TimestampWithoutTimeZone, /// A timestamp with a timezone. TimestampWithTimeZone, /// A UUID. Uuid, } #[test] fn data_type_serialization_examples() { // Our serialization format is an external format, so let's write some tests // to make sure we don't change it accidentally. let examples = &[ ( DataType::Array(Box::new(DataType::Text)), json!({"array":"text"}), ), (DataType::Bool, json!("bool")), (DataType::Date, json!("date")), (DataType::Decimal, json!("decimal")), (DataType::Float32, json!("float32")), (DataType::Float64, json!("float64")), (DataType::Int16, json!("int16")), (DataType::Int32, json!("int32")), (DataType::Int64, json!("int64")), (DataType::Json, json!("json")), ( DataType::Other("custom".to_owned()), json!({"other":"custom"}), ), (DataType::Text, json!("text")), ( DataType::TimestampWithoutTimeZone, json!("timestamp_without_time_zone"), ), ( DataType::TimestampWithTimeZone, json!("timestamp_with_time_zone"), ), (DataType::Uuid, json!("uuid")), ]; for (data_type, serialized) in examples { assert_eq!(&json!(data_type), serialized); } } #[test] fn data_type_roundtrip() { use serde_json; let data_types = vec![ DataType::Array(Box::new(DataType::Text)), DataType::Bool, DataType::Date, DataType::Decimal, DataType::Float32, DataType::Float64, DataType::Int16, DataType::Int32, DataType::Int64, DataType::Json, DataType::Other("custom".to_owned()), DataType::Text, DataType::TimestampWithoutTimeZone, DataType::TimestampWithTimeZone, DataType::Uuid, ]; for data_type in &data_types { let serialized = serde_json::to_string(data_type).unwrap(); println!("{:?}: {}", data_type, serialized); let parsed: DataType = serde_json::from_str(&serialized).unwrap(); assert_eq!(&parsed, data_type); } } /// An SRID number specifying how to intepret geographical coordinates. #[derive(Clone, Copy, Debug, Deserialize, Eq, PartialEq, Serialize)] #[serde(transparent)] pub struct Srid(u32); impl Srid { /// Return the one true SRID (WGS84), according to our GIS folks and Google BigQuery. pub fn wgs84() -> Srid { Srid(4326) } /// Create a new `Srid` from a numeric code. pub fn new(srid: u32) -> Srid { Srid(srid) } /// Return our `Srid` as a `u32`. pub fn to_u32(self) -> u32 { self.0 } } impl Default for Srid { /// Default to WGS84. fn default() -> Self { Self::wgs84() } } impl fmt::Display for Srid { fn fmt(&self, f: &mut fmt::Formatter) -> fmt::Result { self.0.fmt(f) } }