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NDREAN

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Testing SurrealDB

A word on SurrealDB

When an application runs on embedded devices, with the model "off-line first", you want to use your embedded (local) database first. When the user has access to the internet, you can sync with a remote database. One can use SQLite, a single file relational database, but it may be hard to synchronise with a backend. Here comes SurrealDB into play. It is a lightweight cloud-native database that claims to synchronise easily to a backend.
SurrealDB is essentially a key/value ACID-compliant NoSQL database. It can run in memory or with local persistence, or with a remote connection. Redis is this kind of in memory key/value NoSQL database mainly used for caching, for PubSub and streams (queue-like). SurrealDB is different: it offers a DSL very close to SQL and allows you to write Javascript functions.

By default, it is schemaless; you can insert any key/value. When you want more control, you can turn a table into a schemafull table.

If you run the server locally, you need to start a server. It can also be run "serverless", meaning that you reach a service in the cloud. By default, it points to https://cloud.surrealdb.com. HTTP is all you need: the server provides a REST API for CRUD queries, and a unique endpoint at "/sql" where you POST a query.

Websockets aren't documented at the moment, nor how to secure the data.

It is also worth noting that there are some functions to deal with GeoJSON data. It is an important topic as many apps that deal with geolocation are embedded apps. However, spatial indexing doesn't seem to be implemented (yet?) (cf PostGis using GIST).

SurrealDB still seems pretty new. I still needs to be battle tested, and how would one migrate to SurrealDB, as well as performance metrics.
We made a quick demo on how to work with SurrealDB in Elixir with the SurrealEX package and with Node.js. Note that the connection upgrades to websockets with Node.js. A quick comparison is made when serializing a small 6MB CSV file of 57.000 lines into a SurrealDB table vs an SQLite table with Node.js and Elixir, and then querying the tables.
The upload and serialization into SurrealDB with Elixir is slow (7-8s) whilst it takes 1-2s with Node.js. This is because we have to JSON parse the data with Elixir. For SQLite, both Elixir and Node.js take the same time, 1-2s.
The aggregation query (count the 57.000 lines) is slower with SurrealDB (around 300ms) than with SQLite (around 50ms), and Elixir and Node.js perform the same. A line selection is also slower with SurrealDB (around 150ms) versus 10ms with SQLite.

A word on SurrealDB
Start a local SurrealDB server
Interact with the server
Schemaless tables

Graph connections

Schemafull Tables

Events
Register User
Elixir package SurrealEx setup
Insert Elixir maps into a SurrealDB table
Download a CSV file and serialize in a SurrealDB table
Schemaless vs indexed Schemafull
SQLite with Elixir
SurrealDB with Node.js
SQLite with Node.js

Start a local SurrealDB server

You can run a Docker image. We pass the super user credentials and run an in-memory database with:

docker run --rm -p 8000:8000 surrealdb/surrealdb:latest start  \
 --log debug --user root --pass root memory
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Alternatively, you install SurrealDB and run:

surreal start --log debug --user root --pass root memory
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You should get a fancy prompt:


 .d8888b.                                             888 8888888b.  888888b.
d88P  Y88b                                            888 888  'Y88b 888  '88b
Y88b.                                                 888 888    888 888  .88P
 'Y888b.   888  888 888d888 888d888  .d88b.   8888b.  888 888    888 8888888K.
    'Y88b. 888  888 888P'   888P'   d8P  Y8b     '88b 888 888    888 888  'Y88b
      '888 888  888 888     888     88888888 .d888888 888 888    888 888    888
Y88b  d88P Y88b 888 888     888     Y8b.     888  888 888 888  .d88P 888   d88P
 'Y8888P'   'Y88888 888     888      'Y8888  'Y888888 888 8888888P'  8888888P'


[2023-02-18 20:12:12] INFO  surrealdb::iam Root authentication is enabled
[2023-02-18 20:12:12] INFO  surrealdb::iam Root username is 'root'
[2023-02-18 20:12:12] INFO  surrealdb::dbs Database strict mode is disabled
[2023-02-18 20:12:12] INFO  surrealdb::kvs Starting kvs store in memory
[2023-02-18 20:12:12] INFO  surrealdb::kvs Started kvs store in memory
[2023-02-18 20:12:12] INFO  surrealdb::net Starting web server on 0.0.0.0:8000
[2023-02-18 20:12:12] INFO  surrealdb::net Started web server on 0.0.0.0:8000
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Interact with the server

cURL
You can send POST requests via cURL with a payload, the two headers (NS for namespace, DB for database) and a BASIC Authentication to the endpoint:

data = "create tab:john set name='john'"

curl -k -L -s --compressed POST \
    --header "Accept: application/json" \
    --header "NS: test" \
    --header "DB: test" \
    --user "root:root" \
    --data "${DATA}" \
    http://localhost:8000/sql
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SurrealDB CLI
If you installed SurrealDB, you can use CLI where you set the namespace and the database name, and the basic authentication of the super-user. You can then type directly your queries:

$ surreal sql --conn http://localhost:8000  \
--user root --pass root --ns testns --db testdb

> INFO FOR tb;

[{"time": "1.47255ms", "status": "OK", ...]

> CREATE ...
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HTTP client
You can use any HTTP client to POST queries, again with the correct headers and basic authentication.

For example, in Elixir with the HTTP client HTTPoison:

url = "http://localhost:8000/sql"

headers = [
  {"accept", "application/json"},
  {"Content-Type", "application/json"},
  {"NS", "testns"},
  {"DB", "testdb"}
]

auth = [hackney: [basic_auth: {"root", "root"}]]

query = "create table:test set name='hello world';"

HTTPoison.post!(url, query, headers, auth)
|> Map.get(:status_code)
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You can get the response:

HTTPoison.post!(url, "select * from test;", headers, auth)
|> Map.get(:body)
|> Jason.decode!()
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Schemaless tables

First CREATE

Each individual statement within SurrealDB is run within its own transaction.

Every row is identified relatively to a table. To CREATE a row with the "SurQL" DSL, we pass a table name and possibly a unique id in the format <table>:<id> and SET the body with a list of <key>=<value>.

You can pass the SurQL query into the CLI or to the HTTP client or package.

CREATE dev:nelson SET name = 'nelson', status= 'founder';
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[{"time":"4.1355ms","status":"OK","result":[{"id":"dev:nelson","name":"nelson","status":"founder"}]}]
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When we pass an id (the :nelson), the transaction can be run only once. When you don't specify the id, SurrealDB will assign one for you. Note that you can now run the CREATE query below multiple times, but you maybe don't want this.

  CREATE dev SET name = 'again', status='founder'
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[{"id":"dev:kdofs33s4izrt04djc8s","name":"again","status":"founder"}]}]
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You can also use INSERT INTO and pass a JSON object (where you can specify the id), or use a more SQL like with INSERT INTO ... VALUES:

INSERT INTO dev {id: 'bob_id', name: 'bob', status: 'trainee' };
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INSERT INTO dev (id, name, status, id) VALUES ('mike', 'founder', 'mike');
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We check the table:

SELECT * FROM dev;
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[{"id":"dev:bob_id","name":"bob","status":"trainee"},
{"id":"dev:kdofs33s4izrt04djc8s","name":"again","status":"founder"},
{"id":"dev:mike","name":"founder","status":"mike"},
{"id":"dev:nelson","name":"nelson","status":"founder"},
{"id":"dev:ssd3lt188dndo0a2afne","name":"again","status":"founder"}]
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Transaction

You can use a transaction for a set of changes. You can use CREATE and pass params, or run an INSERT INTO and pass a JSON object.

 BEGIN TRANSACTION;
 CREATE dev:lucio SET name = 'lucio', status = 'dev';
 INSERT INTO dev {name: 'nd', id: 'nd', status: 'trainee'};
 COMMIT TRANSACTION;
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Multiple inserts

You pass an array of tuples. You can pass an id, or let Surreal do it for you if you don't need to retrieve them immediately.

INSERT INTO dev [{name: 'Amy'}, {name: 'Mary', id: 'Mary'}];
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The first row has an auto-assigned id:

[{"id":"dev:mdfeausb4gata00vcxav","name":"Amy"},
{"id":"dev:Mary","name":"Mary"}]
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An example of inserting a CSV file into a SurrealDB table is given further down with Elixir.

We can also use the form:

INSERT INTO users (name, location) VALUES ('La Cantine', 'Nantes'),('Work In Excess', 'Nantes'),...
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Timestamps

We can use futures for this. It is a value that will be dynamically computed on every access to the record.

For example, we create a record with an id, a (fixed) "created_at" field, and a future field "updated_at":

CREATE dev:1 SET 
  name='one', 
  created_at=time::now(), 
  updated_at=<future>{time::now()};
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 UPDATE dev:1 SET name='new one';
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[{"created_at":"2023-02-21T20:42:07.978776Z",
"id":"dev:1","name":"new one",
"updated_at":"2023-02-21T20:42:36.468812Z"}]}]
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Record links

You can pass a nested object with the dot "." format. Note how we can pass record links of the "developper" table into the "webapp" table.

CREATE app_table:surreal SET 
  app_name = 'surrealistic.com',
  agency.team = [dev:nelson, dev:lucio, dev:nd],
  agency.name = 'dwyl.com';
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You can equivalently pass the data in JSON format:

  INSERT INTO app_table {
    id: 'unreal', 
    app_name: 'unreal',
    agency: {
      name: 'dwyl.com', 
      team: [dev:nelson, dev:lucio]
    }
  };
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UPDATE app_table:unreal SET app_name='unreal.com';
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Query nested records without "join"

We can get linked data without joins with the dot . notation:

SELECT agency.team.*.name AS team, agency.team.*.status AS status
  FROM app_table:surreal;
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[{"team":["nelson","lucio","nd"],
"status":["founder","dev","trainee"]}}}]
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We can get the name for the devs with status 'dev' per agency by using a nested WHERE:

SELECT app_name,agency.team[WHERE status='dev'].name AS dev 
FROM app_table 
WHERE agency.name = 'dwyl.com';
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[{"app_name": "surrealistic.com", "dev": "lucio"},
{"app_name": "unreal.com", "dev": "lucio"}]
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Aggregation query

Return the number of devs in the team for the app_table row "unreal" with array functions.

 SELECT * from array::len(app_table:unreal.agency.team);
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[{"time":"218.125µs","status":"OK","result":[2]}]
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You can also use count(). Return the number of developpers and their names per project:

SELECT 
  app_name,
  agency.name AS agency, 
  agency.team.*.name AS members, 
  count(agency.team) AS team_count 
  FROM app_table;
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[{"agency":"dwyl.com","app_name":"surrealistic.com","members":["nelson","lucio","nd"],"team_count":3},
{"agency":"dwyl.com","app_name":"unreal.com","members":["nelson","lucio"],"team_count":2}]
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Type functions

You can use type functions that converts a string into the desired type:

SELECT count() AS total, app_name
  FROM type::table('app_table') 
  GROUP BY app_table;
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[{"app_name":"surrealistic.com","total":2}]
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You can use the generic type:thing and pass the object and identifier:

SELECT * FROM type::thing('app_table', 'unreal');
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[{"agency":{"name":"dwyl.com","team":["dev:nelson","dev:lucio"]},
"app_name":"unreal.com","id":"app_table:unreal"}]
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Parameters and subqueries

We can run queries with parameters. Given a webapp, get the names of the team members with status "dev":

LET $status='dev'
LET $dev_team = (SELECT agency.team[WHERE status=$status] FROM app_table:unreal);
SELECT name, status from $dev_team;
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[{"name": "lucio", "status": "dev"}]
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Graph connections

Suppose we have a 1-n relation between agencies and webapps, and a 1-n relation between agencies and devs. In a conventional DB, we would have a foreign key "agency_id" in the table Webapp (Webapp belongs_to Agency), and a foreign key "agency_id" in the Dev table (Dev belongs_to Agency).

ERD with foreign keys

With SurrealDB, we can use 2 approaches:

  • set an array of [webapp_ids] and an array of [dev_ids] as fields of the table Agency,
  • or set connections between the nodes.

Array of linked records

The ERD of the first approach is shown below:

ERD with arrays

We added a field "team" and webapps which respectively stores all the references to devs employed and to webapps developed by an agency.

BEGIN TRANSACTION;
  create webapp:app1 set name = 'app1';
  create webapp:app2 set name = 'app2';
  create webapp:app3 set name = 'app3';

  create developper:nelson set name = 'nelson', status = 'founder';
  create developper:nd set name = 'nd', status = 'trainee';
  create developper:lucio set name = 'lucio', status = 'dev';

  create agency:dwyl1 set name = 'dwyl', project = [], team = [];
  create agency:unreal1 set name = 'unreal', project = [], team = [];

  update agency:dwyl1 set projects += [webapp:1, webapp:3];
  update agency:unreal1 set projects += [webapp:2];
  update agency:dwyl1 set team += [developper:nelson, developper:lucio];
  update agency:unreal1 set team += [developper:nd];
COMMIT TRANSACTION;
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We can get the team members name per agency (by omitting the id) since we have a relation 1-n with record links:

SELECT name AS company, team.*.name AS employees FROM agency:dwyl1;
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[{"company":"dwyl","employees":["nelson","lucio"]}]}]
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Conversely, find the agency for which a dev works. We used the CONTAINS operator.

SELECT name AS company FROM agency WHERE team CONTAINS developper:nd;
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[{"company":"unreal"}]}
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Connections

The second approach with connections is shown below. We setup 2 connections with the 2 verbs "works_for" and "developped". This will generate 2 other tables.

  • [dev:id]->works_for->[agency:id]
  • [agency:id]->developped->[webapp:id]

The order is not important as in the first case, it is a 1-1, and a 1-n in the second case. It turns out that we can reverse the links are we will see. A connection is coded with RELATE @from->verb->@to

ERD edges

BEGIN TRANSACTION;
  CREATE agency:dwyl2 SET name = 'dwyl';
  CREATE agency:unreal2 SET name = 'unreal';

  RELATE developper:lucio->works_for->agency:dwyl2 CONTENT {owner: false, created_at: time::now()};
  RELATE developper:nelson-> works_for->agency:dwyl2 CONTENT {owner: true, created_at: time::now()};
  RELATE developper:nd->works_for->agency:unreal2 CONTENT {owner: true, created_at: time::now()};

  RELATE agency:dwyl2->developped->webapp:app1;
  RELATE agency:unreal2->developped->webapp:app2;
  RELATE agency:dwyl2->developped->webapp:app3;
COMMIT TRANSACTION;
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We can take a peek at the association-table "works_for":

SELECT * FROM developped;
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We have an association [dev:id]->works_for->[agency:id]. We can query for the agency.name given a dev:id:

SELECT name, ->works_for->agency.name AS employer FROM developper:nelson;
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[{"employer":["dwyl"],"name":"nelson"}]
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We now want all the devs working for a particular agency. We just revert the relation: get all dev:id from agency:id:

SELECT name, <-works_for<-developper.name AS employees FROM agency:dwyl2;
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[{"employees":["nelson","lucio"],"name":"dwyl"}]
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Similarly, we can check the webapps name developped by an agency with the association with [agency:id]->developped->[webapp:id].

SELECT ->developped->webapp.name AS agency FROM agency:dwyl2;
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[{"agency":["app1","app3"]}]
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To query the agency which developped a given webapp, we reverse the query:

SELECT <-developped<-agency.name AS agency FROM webapp:app2;
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[{"employees":["nelson","lucio"],"name":"dwyl"}]
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We can run subqueries if we want the devs that worked on a given webapp:

  LET $agency=(SELECT <-developped<-agency.id AS id FROM webapp:app1);
  SELECT  <-works_for<-developper.name AS employees FROM $agency;
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[{"employees":[["nelson","lucio"]]}]
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Schemafull tables

If we want to enforce a fixed struct, we can define a schema with DEFINE TABLE ... SCHEMAFULL.

We will check this enforcement below.

The ERD shows a one_to_many_through relation with 3 tables. Apps has many users, and uses has one details. We set a 1-N relation between app and users, and 1-1 between users and details. We elaborate ith 2 examples:

  • one with 3 tables where we pass an array of references (apps<-[users:id]) and a reference to another table (users <- details:id),
  • and one with 2 tables where we pass an array of references (apps<-[users:id]) and an object (users.details{}) that mirrors the thrid table.

How to pass an array of references?
Since we will have nested data in each table, we create a field that should be an array of references to the other table. For this:

  • create a field say team of type array in the table "apps",
  • and declare the team.* of type record(users) which are references to rows of the table "users".

For the table users, we have a 1-1 relation with the table details. In the first case mentionned above, we add a field of type record(details). In the second case, we declare a field of TYPE object and declare the nested fields in the table "users" that mirror the "details" table that is not used.

We also use INDEX to enforce uniqueness. We also showcased field constraints and default values (with VALUE $value)

3 tables

First case 3 tables 

Conn.sql("
BEGIN TRANSACTION;

  DEFINE TABLE details SCHEMAFULL;
  DEFINE FIELD email ON details TYPE string;
  DEFINE FIELD age ON details type int ASSERT is::numeric($value) and $value>18;
  DEFINE FIELD birthdate ON details TYPE string;

  DEFINE TABLE users SCHEMAFUL;
  DEFINE FIELD name ON users type string;
  DEFINE FIELD details ON users TYPE record(details);
  DEFINE INDEX users ON TABLE users COLUMNS name UNIQUE;

  DEFINE TABLE apps SCHEMAFULL;
  DEFINE FIELD name ON apps TYPE string;
  DEFINE FIELD agency ON apps TYPE string VALUE $value OR 'dwyl';
  DEFINE FIELD team ON apps TYPE array;
  DEFINE FIELD team.* ON apps TYPE record(users);

  DEFINE INDEX name ON TABLE apps COLUMNS name UNIQUE;

COMMIT TRANSACTION;
")
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We can INSERT INTO (and set the :id), or CREATE SET. We can pass nested links. SurrealDB understands dates.

BEGIN TRANSACTION;
  INSERT INTO details {id: 'john', email: 'john@com', age: 20, birthdate: '2023-03-01'};
  INSERT INTO users {name: 'john', id: 'john', details: details:john};

  CREATE details:lucio SET email='lucio@com', age = 20, birthdate = '2023-03-01';
  INSERT INTO users { id: 'lucio', name: 'lucio', details: details:lucio};

  INSERT INTO users {name: 'nelson', id: 'nelson'};

  INSERT INTO apps {id: 'test', agency: 'dwyl', team: []};
COMMIT TRANSACTION;
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SELECT * FROM apps;
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Prevent bad insertions

Let's insert a "bad" record into the "details" table: we get an error:

INSERT  INTO details {gender: 'f', occupation: 'eng', age: 'twenty', id: 'wrong'};
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[{"time":"427.541µs","status":"ERR",
"detail":"Found 0 for field `age`, with record `details:wrong`, 
but field must conform to: is::numeric($value) AND $value > 18"}]
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Insert into a nested array

Let's create a new "dev" and add him as a team member: we use array:concat (doc) or simply +=.

Before:

SELECT team from apps:test;
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Let's update and check if only filtered data of type "users" is accepted:

  UPDATE apps:test SET team += [users:nelson, users:lucio, 'ok'], bad = 'input';
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After:

SELECT team FROM apps:test;
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Nested query without "join"

Let's select the names of the users for all apps developped by the agency "dwyl":

SELECT team.*.name FROM apps WHERE agency = 'dwyl'
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[{"team":{"name":["nelson","lucio"]}}]
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Second case: 2 tables

Instead of defining a third table "details", we pass an object of TYPE object as a field of the table "users", and define the fields as nested attributes (details.owner for example). Since we defined a SCHEMAFULL table, the input is still filtered. For example, we can't pass an extra attribute in the "details" object on the talbe "users".

BEGIN TRANSACTION;

  DEFINE TABLE users1 SCHEMAFUL;
  DEFINE FIELD name ON users1 type string;
  DEFINE FIELD details ON users1 TYPE object;
  DEFINE FIELD details.age ON users1 TYPE int;
  DEFINE FIELD details.owner ON users1 TYPE bool;
  DEFINE INDEX users1 ON TABLE users1 COLUMNS name UNIQUE;

  DEFINE TABLE apps1 SCHEMAFULL;
  DEFINE FIELD name ON apps1 TYPE string;
  DEFINE FIELD agency ON apps1 TYPE string;
  DEFINE FIELD team ON apps1 TYPE array;
  DEFINE FIELD team.* ON apps1 TYPE record(users1);

  DEFINE INDEX name ON TABLE apps1 COLUMNS name UNIQUE;

COMMIT TRANSACTION;
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  INSERT INTO users1 {
    id: 1,
    name: 1,
    details: {
      age: 20,
      owner: false,
      test1: 'bad'
    }
  };
  INSERT INTO users1 {
    id: 2,
    name: 2,
    details: {
      age: 20,
      owner: true
    }
  };

  INSERT INTO apps1 {
    name: 'app1',
    agency: 'surreal',
    team: [users1:1, users1:2, 'toto']
  };
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Events

Let's create an event query example. When we change a field, say the birthdate of the table details, we want to create a new table that records this new date. We can do this with DEFINE EVENT:

  DEFINE EVENT passed_birthdates ON TABLE details 
  WHEN $before.birthdate < $after.birthdate 
  THEN (
  CREATE passed_birthdates SET birthdate = $after.birthdate
);
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We update 2 rows of the table "details" where we change the birthdate:

BEGIN TRANSACTION;
UPDATE details:john SET birthdate='2023-03-02';
UPDATE details:lucio SET birthdate='2023-03-02';
COMMIT TRANSACTION;
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We check that the change in the field triggered the action to create a new table where the record has the field with value the new date.

SELECT * FROM passed_birthdates;
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We have two events:

[{"time":"213.625µs","status":"OK","result":
[{"birthdate":"\"2023-03-02T00:00:00Z\"","id":"passed_birthdates:6i4cdsmue06v2rbfiwdt"},
{"birthdate":"\"2023-03-02T00:00:00Z\"","id":"passed_birthdates:i5ly5nxb1y3lo4knzkms"}]}]
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Register User

Provides a registration via JWT. Authorization can be bound to records in the database, and can be scoped to tables, rows and fields, and actions (like updates or deletions).

Source tutorial

You may use the helpful package SurrealEx (see Elixir setup)

Elixir package SurrealEx setup

A Livebook to experiment:
Run in Livebook

You have two ways to setup the package SurrealEx. Firstly, you setup a config file as below.

#config.exs
import Config

config :surreal_ex, Conn,
  interface: :http,
  uri: "http://localhost:8000",
  ns: "testns",
  db: "testdb",
  user: "root",
  pass: "root"
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For Livebook, put the file below in your root directory, and add the key config_path: "config.exs" in the Mix.install of the Livebook.

Mix.install(
  [{:surreal_ex, "~> 0.2.0"}], 
  config_path: "config.exs"
)
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You can alternatively use the module SurrealEx.Config. Let's define a module Conn to wrap the config.

defmodule Conn do
  @config [
    interface: :http,
    uri: "http://localhost:8000",
    ns: "test",
    db: "test",
    user: "root",
    pass: "root"
  ]
  use SurrealEx.Conn

  def setup do
    @config
    |> SurrealEx.Config.for_http()
    |> SurrealEx.Config.set_config_pid()
  end
end
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You describe the config: uri, (namespace, database) and basic auth, and pass it to the Conn module which uses the SurrealEx.Conn behaviour.

You run the setup:

Conn.setup()
cfg = SurrealEx.Config.get_config(Conn)
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We can now use SurrealDB. Every query above can be run when wrapped with Conn.sql(query).

IF you register users, you get a token and can secure the query with Conn.sql(query, token).

There are other interesting routines with SurrealEx, in particular the SurrealEx.HTTP that allows to pipe queries.

Insert Elixir maps into a SurrealDB table

We define a module with the behaviour use SurrealEx.HTTP.Table. It will reach the REST API supplied by SurrealDB.

defmodule FromAirportMap do
  use SurrealEx.HTTP.Table,
      conn: Conn,
      table: "airports"
end
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We can insert a map into the "airports" table:

FromAirportMap.create(%{country: "FR", IATA: "CDG"})
Conn.sql("SELECT * FROM airports")
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{:ok,
 %SurrealEx.Response{
   time: "1.650583ms",
   status: "OK",
   result: %{"IATA" => "CDG", "country" => "FR", "id" => "airports:nnjki1k2bhcxrxlibmuw"},
   detail: nil
 }}
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Download a CSV file and serialize in a SurrealDB table

Let's download a CSV file, parse it into a map and insert into a SurrealDB table. We wil reach the "/sql" endpoint supplied by SurrealDB with the Conn.sql routine.

Firstly, since SurrealEx seems to use HTTPoison to POST to the server, we build a wrapper around it to stream down a file (source).

defmodule HTTPoisonStream do
  @doc """
  Builds a custom stream from `HTTPoison.AsyncChunk`.
  """
  def download(url) do
    Stream.resource(
      # start_fun: stream line by line with `async: :once`
      fn -> HTTPoison.get!(url, %{}, stream_to: self(), async: :once) end,

      # next_fun,
      fn %HTTPoison.AsyncResponse{id: id} = resp ->
        receive do
          %HTTPoison.AsyncStatus{id: ^id, code: _code} ->
            HTTPoison.stream_next(resp)
            {[], resp}

          %HTTPoison.AsyncHeaders{id: ^id, headers: _headers} ->
            HTTPoison.stream_next(resp)
            {[], resp}

          %HTTPoison.AsyncChunk{id: ^id, chunk: chunk} ->
            HTTPoison.stream_next(resp)
            {[chunk], resp}

          %HTTPoison.AsyncEnd{id: ^id} ->
            {:halt, resp}
        after
          5000 ->
            raise "timeout"
        end
      end,
      # end_fun
      fn %HTTPoison.AsyncResponse{id: id} = _resp ->
        :hackney.stop_async(id)
      end
    )
  end
end
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We can get a CSV dataset from this endpoint:

unzipped_airports =
  "https://pkgstore.datahub.io/core/airport-codes/airport-codes_csv/data/e07739e49300d125989ee543d5598c4b/airport-codes_csv.csv"
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We will parse a CSV line into an Elixir map ith the helper:

defmodule AirportDataset do
  @moduledoc """
  Provides mapping for the Airport dataset: <https://datahub.io/core/airport-codes>
  """
  def map(row) do
    %{
      ident: Enum.at(row, 0),
      type: Enum.at(row, 1),
      name: Enum.at(row, 2),
      elevation_ft: Enum.at(row, 3),
      continent: Enum.at(row, 4),
      iso_country: Enum.at(row, 5),
      iso_region: Enum.at(row, 6),
      municipality: Enum.at(row, 7),
      gps_code: Enum.at(row, 8),
      iata_code: Enum.at(row, 9),
      local_code: Enum.at(row, 10),
      coordinates: Enum.at(row, 11)
    }
  end
end
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We can now download, CSV parse, parse into a map and serialize into a SurrealDB table:

list = 
HTTPoisonStream.download(unzipped_airports)
|> Stream.map(&IO.chardata_to_string/1)
|> CSV.decode!(headers: false, separator: ?,)
|> Stream.map(&AirportDataset.map/1)
|> Stream.chunk_every(1000)
|> Stream.map(fn chunk -> 
  data = Jason.encode!(chunk)
  Conn.sql("
    BEGIN TRANSACTION;
    INSERT INTO airports #{data};
    COMMIT TRANSACTION;
  ") 
end)
|> Stream.run()
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Check it:

Conn.sql("SELECT * FROM airports limit 2;")
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It takes around 6-7s to process this small file. The same operation with the relational database SQLite takes less than 2 second.

The SELECT query is still fast (2ms)

Conn.sql("SELECT * FROM airports LIMIT 2;")
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The query below took 330ms to count 57423 rows:

Conn.sql("SELECT count() from airports GROUP BY all ;")
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Let's select the airports listed in Valledupar, Colombia. It takes around 200ms.

Conn.sql("SELECT * FROM airports WHERE municipality = 'Valledupa';")
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Let's select one, "SK-151". It takes approx 180ms.

Conn.sql("Select * FROM airports WHERE ident = 'SK-151';")
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Schemaless vs indexed Schemafull

Let's build a schemafull table 'airport" from the schemaless table "airports" and add an INDEX on the column "ident" to evaluate the performance.

Conn.sql("
BEGIN TRANSACTION;
DEFINE TABLE airport SCHEMAFULL;
DEFINE INDEX ident ON TABLE airport COLUMNS ident UNIQUE;
DEFINE FIELD ident ON TABLE airport TYPE string;
DEFINE FIELD elevation_ft ON TABLE airport TYPE string;
DEFINE FIELD name ON TABLE airport TYPE string;
DEFINE FIELD continent ON TABLE airport TYPE string;
DEFINE FIELD iso_country ON TABLE airport TYPE string;
DEFINE FIELD iso_region ON TABLE airport TYPE string;
DEFINE FIELD municipality ON TABLE airport TYPE string;
DEFINE FIELD itata_code ON TABLE airport TYPE string;
DEFINE FIELD local_code ON TABLE airport TYPE string;
DEFINE FIELD gps_code ON TABLE airport TYPE string;
DEFINE FIELD coordinates ON TABLE airport TYPE string;
COMMIT TRANSACTION;
")
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We copy the schemaless table "airports" into the schemafull table "airport". It takes 1.2s.

Conn.sql("
BEGIN TRANSACTION;
LET $data = (SELECT * FROM airports);
INSERT INTO airport $data;
COMMIT TRANSACTION;
")
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Let's query on the indexed column "ident". The same query as above takes 150ms. The performance gain seems modest.

Conn.sql("SELECT * FROM airport WHERE ident = 'SK-151';")
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SQLite with Elixir

We setup the SQLite connection, database and create a table to mirror the CSV file we got. The setup is much heavier than SurrealDB.

Our dependencies are:

Mix.install([
  {:surreal_ex, "~> 0.2.0"},
  {:csv, "~> 3.0"},
  {:ecto_sql, "~> 3.9"},
  {:ecto_sqlite3, "~> 0.9.1"}
])
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defmodule SqilteConn do
  def start do
    Exqlite.Sqlite3.open(":memory")
  end

  def create_table(conn, table) do
    Exqlite.Sqlite3.execute(conn, "DROP TABLE IF EXISTS #{table}")

    Exqlite.Sqlite3.execute(
      conn,
      "CREATE TABLE IF NOT EXISTS #{table} (
        id integer primary key,
        ident text,
        elevation_ft text,
        type text,
        name text,
        continent text,
        iso_country text,
        iso_region text,
        municipality text,
        iata_code text,
        local_code text,
        gps_code text,
        coordinates text
        )"
    )
  end

  def reset_table(conn, table) do
    Exqlite.Sqlite3.execute(conn, "DROP TABLE IF EXISTS #{table}")
    create_table(conn, table)
  end
end

{:ok, conn} = SqilteConn.start()
SqilteConn.create_table(conn, "csv")
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We create a Repo and a Repo since SQLite adaptor ecto_sqlite3 works best with it (as per the docs for the SQLite adaptor for Elixir):

defmodule Repo do
  use Ecto.Repo, adapter: Ecto.Adapters.SQLite3, otp_app: :noop
end

case Repo.start_link(database: ":memory", default_chunk_size: 100) do
  {:ok, pid} -> {:ok, pid}
  {:error, {_, pid}} -> {:ok, pid}
end
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headers = [
  :ident, :elevation_ft, :type, :name, :continent, :iso_country,
  :iso_region, :municipality, :iata_code, :local_code, :gps_code, :coordinates
]

defmodule Airport do
  use Ecto.Schema
  @headers headers

  schema "csv" do
    Enum.each(@headers, &field(&1, :string))
  end
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We check that everything is :ok

Airport.__schema__(:fields) |> IO.inspect()
Repo.all(Airport)
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We can now stream down the CSV endpoint into an SQLite table. This process takes between 1-2s to complete, much less than SurrealDB.

:timer.tc( fn -> 
  Repo.transaction(f, -> 
    HTTPoisonStream.download(unzipped_airports)
    |> Stream.map(&IO.chardata_to_string/1)
    |> CSV.decode!(headers: false, separator: ?,)
    |> Stream.map(&AirportDataset.map/1)
    |> Stream.chunk_every(1000)
    |> Stream.each(&Repo.insert_all(Airport, &1))
    |> Stream.run()
  end)
end)
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The aggregation query below took 24ms, much less than the SurrealDB equivalent:

Repo.aggregate(Airport, :count)
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Let's select a row with "ident = SK-151". It takes around 10ms, much less than SurrealDB.

Repo.get_by(Airport, ident: "SK-151")
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Let's add an index on the column "ident":

Exqlite.Sqlite3.execute(conn,"
CREATE INDEX ident_idx ON csv (ident);
")
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And query again: it takes now 1ms.

Repo.get_by(Airport, ident: "SK-151")
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SurrealDB with Nodejs

We install the npm package "csv", "surrealdb.js" and "sqlite3".

Run the file below: node main.mjs.

// main.mjs
import { parse } from "csv-parse";
import https from "https";
import Surreal from "surrealdb.js";
const db = new Surreal("http://127.0.0.1:8000/rpc");

const unzipped_airports =
  "https://pkgstore.datahub.io/core/airport-codes/airport-codes_csv/data/e07739e49300d125989ee543d5598c4b/airport-codes_csv.csv";

async function upload() {
  const start = new Date();
  return new Promise((resolve, reject) => {
    return https.get(unzipped_airports, (response) => {
      return response
      .pipe(parse({ delimiter: ",", from_line: 2 }))
      .on("error", function (error) {
        console.log(error.message);
      })
      .on("data", async function (row) {
        await db.create("airport", {
          ident: row[0],
          type: row[1],
          name: row[2],
          elevation_ft: row[3],
          continent: row[4],
          iso_country: row[5],
          iso_region: row[6],
          municipality: row[7],
          gps_code: row[8],
          iata_code: row[9],
          local_code: row[10],
          coordinates: row[11],
        });
      })
      .on("end", async function () {
        console.log("finished");
        console.log(new Date() - start);
        // await queries();
      });
   });
  });
}

async function main() {
  await db.signin({
    user: "root",
    pass: "root",
  });

  await db.use("testnd", "testdb");

  return upload();
}

main().then(async()=> {
  // SurrealDB write/read => setTimeout
   await new Promise((resolve) => setTimeout(resolve, 3000));
    const count = await db.query("SELECT count() FROM airport GROUP BY ALL;");
    console.log(JSON.stringify(count));

    const detail = await db.query(
      "SELECT * FROM airport WHERE ident = 'SK-151';"
    );
    console.log(JSON.stringify(detail));
})
.catch((err) => console.log(err));
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SQLite with Nodejs

Run node sqlite.mjs.

// sqlite_db.mj
import sqlite3 from "sqlite3";

function startDB(db) {
  db.exec(`
    DROP TABLE IF EXISTS airport;
    CREATE TABLE IF NOT EXISTS airport (
        ident text,
        elevation_ft text,
        type text,
        name text,
        continent text,
        iso_country text,
        iso_region text,
        municipality text,
        iata_code text,
        local_code text,
        gps_code text,
        coordinates text
        )
  `);
}

export { sqlite3, startDB };
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and

// sqlite.mjs
import { parse } from "csv-parse";
import https from "https";

import { sqlite3, startDB } from "./sqlite_db.mjs";
const db = new sqlite3.Database(":memory:");

let start = new Date();
const unzipped_airports =
  "https://pkgstore.datahub.io/core/airport-codes/airport-codes_csv/data/e07739e49300d125989ee543d5598c4b/airport-codes_csv.csv";

async function upload() {
  startDB(db);
  https.get(unzipped_airports, (response) => {
    response
      .pipe(parse({ delimiter: ",", from_line: 2 }))
      .on("end", function () {
        console.log("finished");
        console.log("total", new Date() - start);
        start = new Date();
        db.get(`SELECT count(*) FROM airport`, function (err, res) {
          console.log("count", new Date() - start);
          console.log("count:", res);
        });
        start = new Date();
        db.get(
          `SELECT * FROM airport WHERE ident = 'SK-151'`,
          function (err, res) {
            console.log("single", new Date() - start);
            console.log("single", res);
          }
        );
        db.close();
      })
      .on("error", function (error) {
        console.log(error.message);
      })
      .on("data", function (row) {
        db.run(
          `INSERT INTO airport VALUES (?, ?, ? , ?, ?, ?, ?, ?, ?, ?, ?, ?)`,
          [
            row[0],
            row[1],
            row[2],
            row[3],
            row[4],
            row[5],
            row[6],
            row[7],
            row[8],
            row[9],
            row[10],
            row[11],
          ],
          function (error) {
            if (error) {
              return console.log(error.message);
            }
          }
        );
      });
  });
}

upload();
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Top comments (4)

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cotneit profile image
Vladyslav Karasov • • Edited

SQLite is running in-process while SurrealDB instance is a separate server. Comparing performance this way hardly seems fair.

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ndrean profile image
NDREAN •

You mean I should have compared to say Postgres? The point wasn't to compare - it is not even scientifically done - but rather to take notes and check the performance and evaluate quickly the tool.

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snarkipus profile image
Matt Jackson •

Thank for this! Very helpful!

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vitalis profile image
Vitaly Gorodetsky •

Thank you for your contribution

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