Chapter 13: The outsiders

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The invaders had clearly prepared years in advance. They arrived in large ships and boasted firearms that could strike from a distance. They were well trained, and had not come to talk.

‎

Their invasion failed, spectacularly. Twenty years ago you would have stood no chance, but today is a different story. As the enemy marched past the telegraph poles, thinking them to be religious symbols, their positions were relayed in real time to your commanders. Your troops destroyed their supply lines from behind, and every town they reached was already prepared for their arrival. The troops defending the towns had firearms that fire faster, farther, and more accurately than theirs. And your new financial system made it easy to send funds whenever necessary. After a month it was over.

‎

The invaders even got sick! It turns out that the common flu for your people is a devastating disease for them, as they had never had it before.

‎

Strangely enough, at this point you all began to feel pity for them and set about giving them medical care. You confiscated most of their ships, leaving a few for them to get back home. But some of them were amazed at your society, and you let them stay. All in all, they seemed rather embarrassed by the whole thing.

‎

The incident makes you chuckle and shudder at the same time. What if, twenty years ago, you hadn’t noticed that the mysterious devices in the tunnel were computers? Or if nobody had discovered the tunnel the first place?

‎

After all this time spent on financial and military matters, you feel a strong urge to get back to your true love: books. You remember reading that SurrealDB has some impressive ways to search through text, and begin learning how they work.

Full text search

Full text search is a feature that allows you to customize how to search through text in an intuitive way, instead of just comparing for equality. Full text search is something we interact with every day. When you do a search for a word like “sArrealdb” in a search engine and it asks you if you meant to search for “surrealdb”, that’s thanks to full text search.

Enabling full text search in SurrealDB is fairly straightforward, but it requires a certain amount of setup. It all starts with a DEFINE ANALYZER statement. An example of the syntax may look intimidating at first:

DEFINE ANALYZER book_analyzer
	TOKENIZERS blank, class, camel, punct
	FILTERS snowball(english);

But the syntax for DEFINE ANALYZER is really just a list of menu items that you choose to modify text in the way you prefer.

Everyone who uses full text search will want to use it in a different way. For example, imagine that you would like to search through the first two sentences from this book:

“You manage the town’s library, one of the largest in the known world at over 200 books in size thanks to your hard work. Every month or so you manage to add a new book or two to the collection from a passing traveler or merchant.”

Manually searching through the string is a possibility, but will return false if you don’t have an exact match:

LET $string = "You manage the town's library, one of the largest in the known world at over 200 books in size thanks to your hard work. Every month or so you manage to add a new book or two to the collection from a passing traveler or merchant.";

RETURN "pass"    IN $string; -- true
RETURN "passing" IN $string; -- true
RETURN "passed"  IN $string; -- false, even though it contains 'pass' and 'passing'
RETURN "every"   IN $string; -- false, even though it contains 'Every'

You could work with this a little bit using functions like string::lowercase , but for anything more complex you will want to use full text search. This allows you to search through text in a number of ways, such as:

• Splitting by whitespace, so that you can search by word instead of over the whole string.
• Making every letter lowercase or uppercase. Without this, the words You and you will be treated as different words.
• Removing punctuation and other tokens. Without this, the last word will be seen as merchant. and not merchant without a period at the end.
• Turning the words into their root forms, such as town's to town , passing to pass , and known to know .
• Chopping up the words in certain other ways, so that the word book will show up once a user has typed bo , or so that a search for ollectio matches collection .

The way this is done is by choosing tokenizers and filters.

Tokenizers come first, splitting the input into individual tokens. Here are the tokenizers that are available to choose from:

• blank : Creates a new token each time it encounters a space, tab, or newline character. This tokenizer turns "The invaders have been defeated!" into ["The", "invaders", "have", "been", "defeated!"] .
• class : Splits based on digit, letter, punctuation, and blank. This tokenizer turns "123abc! XYZ" into ["123", "abc", "!", "XYZ"] .
• punct : Splits based on punctiation. This tokenizer turns "Hello, World!" into ["Hello", ",", "World", "!"] .
• camel : This creates a new token each time a capital letter is encountered and is mostly useful for parsing programming language syntax. This tokenizer turns "AeonsFirstRustStruct" into ["Aeons", "First", "Rust", "Struct"] .

SurrealDB has a function called search::analyze() that lets you confirm this output for yourself. This function takes two strings: one string that holds the name of the analyzer, followed by a string to process. Here is the output of the first example for the blank tokenizer given above:

DEFINE ANALYZER blank TOKENIZERS blank;
search::analyze("blank", "The invaders have been defeated!");

Response
[
	'The',
	'invaders',
	'have',
	'been',
	'defeated!'
]

Here is another example, this time using three tokenizers.

DEFINE ANALYZER three_tokenizers TOKENIZERS blank, camel, class;
search::analyze("three_tokenizers", "The invaders have been defeated! Time to finish implementing AeonsFirstRustStruct");

Response
[
	'The',
	'invaders',
	'have',
	'been',
	'defeated',
	'!',
	'Time',
	'to',
	'finish',
	'implementing',
	'Aeons',
	'First',
	'Rust',
	'Struct'
]

At this point, the text will have been split up into tokens but has not been modified in any way. This is where filters come in, which modify the tokens. Some filters are pretty self-explanatory:

• lowercase : Turns into lowercase, changing ["Hello", "World"] to "hello world" .
• uppercase : Turns into uppercase, changing ["Hello", "World"] to ["HELLO", "WORLD"] .
• ascii : Changes any non-ascii characters into ascii, such as "résumé café" to "resume cafe" .

After these basic filters come the most interesting ones. They are:

• ngram : This keyword is followed by two numbers inside parentheses such as (2,4) . An ngram will create a token for every possible part of a word of a length between the first and last number. So if you tokenize "have been found" by blank and then follow up with ngram(2,4) , the output will be [["ha", "av", "ve", "hav", "ave", "have"], ["be", "ee", "en", "bee", "een", "been"], ["fo" "ou", "un", "nd", "fou", "oun", "und", "foun", "ound"]] .

• edgengram (“edge ngram”): This keyword is also followed by two numbers inside parentheses such as (2,4) . An edgengram will create a token for every possible size starting at the beginning of the word of a length between the two numbers. So if you tokenize "have been found" by blank and then follow up with edgengram(2,4) , the output will be [["ha", "hav", "have"], ["be", "bee", "been"], ["fo", "fou", "foun"]] .

• snowball : This reduces each word to a root form and converts it to lowercase. If you take the sentence "You manage the town's library, one of the largest in the known world at over 200 books in size thanks to your hard work." and use the punct tokenizer to remove the punctuation, the snowballed output will be ["you", "manag", "the", "town", "library", "one", "of", "the", "largest", "in", "the", "known", "world", "at", "over", "200", "book", "in", "size", "thank", "to", "your", "hard", "work"] .

Snowball filtering is implemented differently depending on the language. For example, the root form for "manage" is not "manage" itself, but "manag" , in order to match forms like "managing" that do not contain an e. Snowball is currently available for Arabic, Danish, Dutch, English, French, German, Greek, Hungarian, Italian, Norwegian, Portuguese, Romanian, Russian, Spanish, Swedish, Tamil, and Turkish.

So as you can see, the syntax for DEFINE ANALYZER simply involves picking from a menu of tokenizers and analyzers depending on how you would like to modify text in order to search through it.

Aeon’s full text search

Let’s think about how Aeon and the team will want to define an analyzer. There is a good possibility that they have enjoyed reading the stories of Sherlock Holmes, and these books are old enough to be free of copyright in our time too, so let’s go with them.

Take a look at this sample from the first book of Sherlock Holmes and think about how you might want to tokenize and filter it.

“His face—his dress—didn’t you notice them?” Holmes broke in impatiently.

‎

“I should think I did notice them, seeing that I had to prop him up—me and Murcher between us. He was a long chap, with a red face, the lower part muffled round——”

‎

“That will do,” cried Holmes. “What became of him?”

As we just learned, a class tokenizer will split up the input into tokens separated from all of the punctuation around them, while a snowball(english) filter will turn the words into their root forms. This gives us a statement that looks like this:

DEFINE ANALYZER english_analyzer
	TOKENIZERS class
	FILTERS snowball(english);

This analyzer that we have chosen to call english_analyzer is now ready to be used, but SurrealDB doesn’t know where we want to use it yet. In order to apply it, we will need to define an index and tell SurrealDB which fields we want to apply it to. Once this index is applied, we will be able to use the @@ operator (the “matches operator”), which is used for full text search instead of the equals sign.

Let’s give it a try with just the analyzer and the matches operator so that we aren’t hit with too much information at the same time. This will generate an error that informs us that we now need to put an index together.

DEFINE ANALYZER english_analyzer
	TOKENIZERS class
	FILTERS snowball(english);
CREATE sentence SET
	title = "A Study in Scarlet",
	text = "IN the year 1878 I took my degree of Doctor of Medicine of the University of London, and proceeded to Netley to go through the course prescribed for surgeons in the army.";
SELECT text
	FROM sentence
	WHERE text @@ "course";

Response
"There was no suitable index supporting the expression 'text @@ 'course''"

We’ve used DEFINE INDEX to define an index in this book before to ensure that fields are unique, but DEFINE INDEX has many other options such as SEARCH ANALYZER and its name, in whihh case it will become a full text search index.

The parts of the DEFINE INDEX that apply to us look like this:

DEFINE INDEX index_name ON TABLE table_name FIELDS fields_to_index SEARCH ANALYZER name_of_analyzer [ BM25 ] [ HIGHLIGHTS ];

Note the optional clauses called BM25 and HIGHLIGHTS . The BM in BM25 stands for “best matching”, and is a function that ranks results by relevance, while HIGHLIGHTS allows you to highlight words that match so that they are easy to see in the search results. Those sound convenient, so let’s add them too.

Putting everything together, we have our analyzer, index, two example sentences, and our first query using the matching operator.

DEFINE ANALYZER english_analyzer
	TOKENIZERS class
	FILTERS snowball(english);

DEFINE INDEX english_index
	ON TABLE sentence
	FIELDS text
	SEARCH ANALYZER english_analyzer BM25 HIGHLIGHTS;

CREATE sentence SET
	title = "A Study in Scarlet",
	text = "IN the year 1878 I took my degree of Doctor of Medicine of the University of London, and proceeded to Netley to go through the course prescribed for surgeons in the army.";
CREATE sentence SET
	title = "A Study in Scarlet",
	text = "Having completed my studies there, I was duly attached to the Fifth Northumberland Fusiliers as Assistant Surgeon.";
SELECT text
	FROM sentence
	WHERE text @@ "SURGEONS";

The query works! A search for “SURGEONS” still turned up the sentences with "surgeons" and "Surgeon." without needing to directly work with any of the text ourselves.

[
	{
		text: 'IN the year 1878 I took my degree of Doctor of Medicine of the University of London, and proceeded to Netley to go through the course prescribed for surgeons in the army.'
	},
	{
		text: 'Having completed my studies there, I was duly attached to the Fifth Northumberland Fusiliers as Assistant Surgeon.'
	}
]

It makes sense that people would also want to search by the title of a book, so we should build an index for the titles of books as well. This will allow people to find what they are looking for even if they can only remember vague details like “I remember that my favourite part had the word ‘night’ in it”, or “A book that had the word ‘hound’ in the title”.

Because our analyzer is already defined, we can do this by just adding another DEFINE INDEX to this field as well. This time, we probably don’t want to add HIGHLIGHTS because titles are always quite short.

We’ll also add a few more sentences from some other books to make the search more interesting.

DEFINE ANALYZER english_analyzer
	TOKENIZERS class
	FILTERS snowball(english);
DEFINE INDEX text_index
	ON TABLE sentence
	FIELDS text
	SEARCH ANALYZER english_analyzer BM25 HIGHLIGHTS;
DEFINE INDEX title_index
	ON TABLE sentence
	FIELDS title
	SEARCH ANALYZER english_analyzer BM25;

INSERT INTO sentence (title, text) VALUES
    ("A Study in Scarlet", "IN the year 1878 I took my degree of Doctor of Medicine of the University of London, and proceeded to Netley to go through the course prescribed for surgeons in the army."),
    ("A Study in Scarlet", "Having completed my studies there, I was duly attached to the Fifth Northumberland Fusiliers as Assistant Surgeon."),
    ("The Sign of the Four", "SHERLOCK HOLMES took his bottle from the corner of the mantel-piece and his hypodermic syringe from its neat morocco case."),
    ("The Hound of the Baskervilles", "MR. SHERLOCK HOLMES, who was usually very late in the mornings, save upon those not infrequent occasions when he was up all night, was seated at the breakfast table."),
    ("The Hound of the Baskervilles", "I stood upon the hearth-rug and picked up the stick which our visitor had left behind him the night before.");

SELECT text, title
	FROM sentence
	WHERE text @@ "night" AND title @@ "hound";

As requested, we get the two sentences that are from “the book with ‘hound’ in the title” and have the word “night” (or nights, or other forms of the word).

[
	{
		text: 'MR. SHERLOCK HOLMES, who was usually very late in the mornings, save upon those not infrequent occasions when he was up all night, was seated at the breakfast table.',
		title: 'The Hound of the Baskervilles'
	},
	{
		text: 'I stood upon the hearth-rug and picked up the stick which our visitor had left behind him the night before.',
		title: 'The Hound of the Baskervilles'
	}
]

Now what about the highlighting and search relevance that we added with the BM25 HIGHLIGHTS keywords? We haven’t seen any of that in the output yet. There is a reason for this: we don’t always want to see this output in our queries. However, the index has prepared the data in these records for us to use one of the rest of SurrealDB’s search functions when we need to use them. These search functions are as follows:

• search::score() : Returns the relevance score. Searching for “Aeon” will return a much higher score in a sentence like "Aeon Aeon Aeon" than in a sentence that is 500 words and only has “Aeon” in it once.
• search::highlight() : Highlights matching keywords.
• search::offsets() : Returns the position of the matching keywords.

We’ll start with search::score() , which is the most commonly used of the three. This function takes a single number. Why a number? The following query - which doesn’t work - will make this clear. Imagine that we want to include the relevance score of both the text and the title, and changed our query at the end to this:

SELECT
    text,
    title,
    search::score() AS text_score,
    search::score() AS title_score
    FROM sentence
    WHERE
        text  @@ "night" AND
        title @@ "hound";

It doesn’t quite work because SurrealDB doesn’t know what search::score() is supposed to apply to in the WHERE clause: the text field, or the title field? To let SurrealDB know which is which, we add one number between the @@ marks of the matches operator, and then pass these numbers into the functions. It will now know that text_score should apply to the search for “night”, and title_score to “hound”.

SELECT
    text,
    title,
    search::score(0) AS text_score,
    search::score(1) AS title_score
    FROM sentence
    WHERE
        text  @0@ "night" AND
        title @1@ "hound";

Putting it all together, the definitions and query look like this.

DEFINE ANALYZER english_analyzer
	TOKENIZERS class
	FILTERS snowball(english);
DEFINE INDEX text_index ON TABLE sentence FIELDS text SEARCH ANALYZER english_analyzer BM25 HIGHLIGHTS;
DEFINE INDEX title_index ON TABLE sentence FIELDS title SEARCH ANALYZER english_analyzer BM25;

INSERT INTO sentence (title, text) VALUES
    ("A Study in Scarlet", "IN the year 1878 I took my degree of Doctor of Medicine of the University of London, and proceeded to Netley to go through the course prescribed for surgeons in the army."),
    ("A Study in Scarlet", "Having completed my studies there, I was duly attached to the Fifth Northumberland Fusiliers as Assistant Surgeon."),
    ("The Sign of the Four", "SHERLOCK HOLMES took his bottle from the corner of the mantel-piece and his hypodermic syringe from its neat morocco case."),
    ("The Hound of the Baskervilles", "MR. SHERLOCK HOLMES, who was usually very late in the mornings, save upon those not infrequent occasions when he was up all night, was seated at the breakfast table."),
    ("The Hound of the Baskervilles", "I stood upon the hearth-rug and picked up the stick which our visitor had left behind him the night before.");

SELECT
    text,
    title,
    search::score(0) AS text_score,
    search::score(1) AS title_score
    FROM sentence
    WHERE
        text  @0@ "night" AND
        title @1@ "hound";

And now it works!

[
	{
		text: 'MR. SHERLOCK HOLMES, who was usually very late in the mornings, save upon those not infrequent occasions when he was up all night, was seated at the breakfast table.',
		text_score: 0.30209195613861084f,
		title: 'The Hound of the Baskervilles',
		title_score: 0.32491400837898254f
	},
	{
		text: 'I stood upon the hearth-rug and picked up the stick which our visitor had left behind him the night before.',
		text_score: 0.35619309544563293f,
		title: 'The Hound of the Baskervilles',
		title_score: 0.32491400837898254f
	}
]

With this, we can assign weights to parts that we think are most important, and order the queries based on this score. Let’s decide that we want the title of the book to be most important for determining relevance, so we will double its score before adding it to the relevance of the text , and replace the AND with OR so that anything matching either of these fields will show up.

DEFINE ANALYZER english_analyzer
	TOKENIZERS class
	FILTERS snowball(english);
DEFINE INDEX text_index
	ON TABLE sentence
	FIELDS text
	SEARCH ANALYZER english_analyzer BM25 HIGHLIGHTS;
DEFINE INDEX title_index
	ON TABLE sentence
	FIELDS title
	SEARCH ANALYZER english_analyzer BM25;

INSERT INTO sentence (title, text) VALUES
    ("A Study in Scarlet", "IN the year 1878 I took my degree of Doctor of Medicine of the University of London, and proceeded to Netley to go through the course prescribed for surgeons in the army."),
    ("A Study in Scarlet", "Having completed my studies there, I was duly attached to the Fifth Northumberland Fusiliers as Assistant Surgeon."),
    ("The Sign of the Four", "SHERLOCK HOLMES took his bottle from the corner of the mantel-piece and his hypodermic syringe from its neat morocco case."),
    ("The Hound of the Baskervilles", "MR. SHERLOCK HOLMES, who was usually very late in the mornings, save upon those not infrequent occasions when he was up all night, was seated at the breakfast table."),
    ("The Hound of the Baskervilles", "I stood upon the hearth-rug and picked up the stick which our visitor had left behind him the night before.");

SELECT
    text,
    title,
    search::score(0) * 2.0 + search::score(1) AS score
    FROM sentence
    WHERE
        title @0@ "hound" OR
        text  @1@ "holmes"
    ORDER BY score DESC;

The output shows one sentence that matches on both title and text, followed by one that matches on title but not text, and finally the least relevant sentence: a match on just the text but not the title.

[
	{
		score: 0.9519199728965759f,
		text: 'MR. SHERLOCK HOLMES, who was usually very late in the mornings, save upon those not infrequent occasions when he was up all night, was seated at the breakfast table.',
		title: 'The Hound of the Baskervilles'
	},
	{
		score: 0.6498280167579651f,
		text: 'I stood upon the hearth-rug and picked up the stick which our visitor had left behind him the night before.',
		title: 'The Hound of the Baskervilles'
	},
	{
		score: 0.35619309544563293f,
		text: 'SHERLOCK HOLMES took his bottle from the corner of the mantel-piece and his hypodermic syringe from its neat morocco case.',
		title: 'The Sign of the Four'
	}
]

The search::highlight() function is pretty similar, except that you need to tell SurrealDB how to highlight the text on both the left and the right. This is because you might want the word to show up like <b>word</b> , or *word* , or any other way.

Let’s give this a try with hand emojis! Don’t forget that we have only enabled HIGHLIGHTS in the index for text , not title .

SELECT
    title,
    search::highlight('👉', '👈', 1) AS text,
    search::score(0) * 2.0 + search::score(1) AS score
    FROM sentence
    WHERE
        title @0@ "hound" OR
        text  @1@ "holmes" ORDER BY score DESC;

Now it’s much clearer where Sherlock Holmes shows up in the text.

[
	{
		score: 0.9519199728965759f,
		text: 'MR. SHERLOCK 👉HOLMES👈, who was usually very late in the mornings, save upon those not infrequent occasions when he was up all night, was seated at the breakfast table.',
		title: 'The Hound of the Baskervilles'
	},
	{
		score: 0.6498280167579651f,
		text: 'I stood upon the hearth-rug and picked up the stick which our visitor had left behind him the night before.',
		title: 'The Hound of the Baskervilles'
	},
	{
		score: 0.35619309544563293f,
		text: 'SHERLOCK 👉HOLMES👈 took his bottle from the corner of the mantel-piece and his hypodermic syringe from its neat morocco case.',
		title: 'The Sign of the Four'
	}
]

HTTP methods and enabling HTTP requests

And now for a bit of fun, let’s put the entire content of one of these books — The Hound of the Baskervilles — into our database to do some text searching! Unlike Aeon and the team, we have full access to the internet and can simply pull in the content from websites like Project Gutenberg.

SurrealDB has built-in methods to fetch the content of these books, in a module called http . The function names are not surprising:

http::head()
http::get()
http::put()
http::post()
http::patch()
http::delete()

Also unsurprisingly, this functionality is disabled by default. You certainly don’t want to have your database able to make remote calls unless you specify that it should! We will get into more detail on the subject of security in Chapter 15.

To allow these functions to work, we will have to pass in the --allow-net parameter when starting the database. This command will allow us to restart the database in a mode in which these functions to be used.

surreal start --user root --pass root --allow-net

With HTTP enabled, the rest of our work is pretty simple: we’ll use the string::split() function to split the content by ”.” as a quick way to divide the book into smaller parts, and create a sentence record if the split value is at least a certain length. The statements below will take a few seconds to process, as SurrealDB needs to fetch the content of the book and then split it up into a huge number of tokens for easy searching.

DEFINE ANALYZER english_analyzer TOKENIZERS class FILTERS snowball(english);
DEFINE INDEX text_index ON TABLE sentence FIELDS text SEARCH ANALYZER english_analyzer BM25 HIGHLIGHTS;

LET $title = "The Hound of the Baskervilles";
LET $content = http::get("https://www.gutenberg.org/cache/epub/2852/pg2852.txt");

FOR $sentence IN $content.split(".") {
  IF $sentence.len() > 5 {
      CREATE sentence SET
          title = $title,
          text = $sentence;
  }
};

Once this is done though, the magic begins! Let’s see which sentences in the book have the most relevance to the word “Watson London”, and highlight them too.

And while we are at it, let’s take a look at the search::offsets() function to see what the output looks like for this last of SurrealDB’s three search functions.

SELECT
    search::score(0) AS score,
    search::highlight('_', '_', 0) AS text,
    search::offsets(0) AS offsets
    FROM sentence
    WHERE text @0@ "Watson London"
    ORDER BY score DESC;

It looks like there are three results for sentences that include both the word Holmes and London. The first one is very relevant, because the sentence includes only eight words besides Watson and London. Also note how the offsets function works: it shows us the start (s ) and end (e ) index for each word.

{
    offsets: {
        0: [
            {
                e: 30,
                s: 24
            },
            {
                e: 54,
                s: 48
            }
        ]
    },
    score: 9.495235443115234f,
    text: '”

    “I fear that _Watson_ and I must go to _London_'
}

The last of the results is much less relevant in terms of “Watson” and “London”, being quite a long paragraph with London and Watson somewhere in the middle.

{
    offsets: {
        0: [
            {
                e: 269,
                s: 263
            },
            {
                e: 277,
                s: 271
            }
        ]
    },
    score: 2.5910158157348633f,
    text: ' It was
    a cunning device, for, apart from the chance of driving your
    victim to his death, what peasant would venture to inquire too
    closely into such a creature should he get sight of it, as many
    have done, upon the moor? I said it in _London_, _Watson_, and I say
    it again now, that never yet have we helped to hunt down a more
    dangerous man than he who is lying yonder”—he swept his long arm
    towards the huge mottled expanse of green-splotched bog which
    stretched away until it merged into the russet slopes of the
    moor'
}

We should be pretty used to full text search by now. Let’s get back to the financial service set up by Aeon and the team and think about what else they could use to make it more rigorous.

Defining events

We learned in the last chapter about how to make a changefeed, either for a certain table or across the whole database.

DEFINE TABLE person CHANGEFEED 3d;
SHOW CHANGES FOR TABLE person SINCE 1;

A changefeed is good to keep a general eye on all the activities of a table or database, but a changefeed is entirely passive. Sometimes we want to do something more active, by triggering some activity when a change in the data takes place. To do this, we can use a DEFINE EVENT statement. A DEFINE event needs a name, a table to be defined on, and the word THEN .

DEFINE EVENT some_event ON some_table THEN {
	-- do something here
};

Let’s warm up by creating the simplest possible event, an event that creates a record every time some change happens to a table.

DEFINE EVENT track_everything ON person THEN {
    CREATE track_everything_event SET info = "Something happened to a person record!";
};

This isn’t the most useful event, but it’s enough to demonstrate that the event does indeed work.

DEFINE EVENT track_everything ON person THEN {
    CREATE track_everything_event SET info = "Something happened to a person record!";
};

CREATE person;
UPDATE person SET name = "Aeon";
DELETE person;

SELECT * FROM track_everything_event;

As the output shows, a track_everything_event record is being created every time we do something to any person record.

[
	{
		id: track_everything_event:uap4x2edvt22762e1vaq,
		info: 'Something happened to a person record!'
	},
	{
		id: track_everything_event:yrxa0rvcut7tmrztidxt,
		info: 'Something happened to a person record!'
	},
	{
		id: track_everything_event:zfqxk3yaqlmg29i6mfnj,
		info: 'Something happened to a person record!'
	}
]

Let’s make this track_everything event a little more useful by adding a timestamp, as well as a parameter called $event that is always set during an event. Let’s see what the $event parameter contains.

DELETE track_everything_event;
DEFINE EVENT OVERWRITE track_everything ON person THEN {
    CREATE track_everything_event SET info = $event, at = time::now();
};

CREATE person;
UPDATE person SET name = "Aeon";
DELETE person;

SELECT * FROM track_everything_event;

This is now starting to look a little like a custom changefeed. We can now see that the $event parameter is simply the type of operation performed, such as UPDATE or DELETE .

[
	{
		at: d'2024-09-02T06:16:51.663591Z',
		id: track_everything_event:0b1sm9cvbcnc8zp4ned5,
		info: 'DELETE'
	},
	{
		at: d'2024-09-02T06:16:51.662943Z',
		id: track_everything_event:4bqnzmcx52gdquqhwvih,
		info: 'UPDATE'
	},
	{
		at: d'2024-09-02T06:16:51.662259Z',
		id: track_everything_event:amkyha26wazj941cjzzn,
		info: 'CREATE'
	}
]

With this information, we can add a condition to an event such as WHEN $event = "CREATE" .

An event also holds other parameters related to the operation itself, such as $before and $after . Let’s experiment with that a little bit.

This next event makes it impossible for any record on the person table to be deleted by throwing an error if it sees a DELETE event. Because a DELETE statement also has a $before , we can add that to the error message to taunt the user who is trying to delete the record(s).

DEFINE EVENT alive_forever ON person WHEN $event = "DELETE" THEN {
    THROW "\n\nTrying to delete this stuff here?\n\n" + <string>$before + "\n\nNope, still there. ";
};

Now no person records can ever happen on this database, as the query below shows.

DEFINE EVENT alive_forever ON person WHEN $event = "DELETE" THEN {
    THROW "\n\nTrying to delete this stuff here?\n\n" + <string>$before + "\n\nNope, still there. ";
};

CREATE person SET name = "Just a test";
DELETE person WHERE name = "Just a test";

Response
"An error occurred:

Trying to delete this stuff here?

{ id: person:t8pz0sh9guzgmtsjtr22, name: 'Just a test' }

Nope, still there. "

This next event makes it impossible to create a person with the foul names “Dummy” or “Stupidhead”, instead changing the name to “Daisy”.

DEFINE EVENT no_bad_names ON person WHEN $event = "CREATE" THEN {
    IF $after.name IN ["Dummy", "Stupidhead"] {
        UPDATE $after SET name = "Daisy";
    }
};

If we combine these two, we now have a situation where a user who tried to create a user called “Stupidhead” now has to use the name “Daisy”, and cannot even delete the record.

DEFINE EVENT alive_forever ON person WHEN $event = "DELETE" THEN {
    THROW "\n\nTrying to delete this stuff here?\n\n" + <string>$before + "\n\nNaw, still there. ";
};

DEFINE EVENT no_bad_names ON person WHEN $event = "CREATE" THEN {
    IF $after.name IN ["Dummy", "Stupidhead"] {
        UPDATE $after SET name = "Daisy";
    }
};

CREATE person SET name = "Stupidhead";
DELETE person;
SELECT * FROM person;

Response
-------- Query --------

[
	{
		id: person:4xk4392xi0gv0b1f5uy0,
		name: 'Stupidhead'
	}
]

-------- Query --------

"An error occurred:

Trying to delete this stuff here?

{ id: person:4xk4392xi0gv0b1f5uy0, name: 'Daisy' }

 Naw, still there. "

-------- Query --------

[
	{
		id: person:4xk4392xi0gv0b1f5uy0,
		name: 'Daisy'
	},
	{
		id: person:t8pz0sh9guzgmtsjtr22,
		name: 'Just a test'
	}
]

So that was pretty fun. But how might Aeon and Landevin and the rest want to use an event? Well, this part of the world in Aeon’s time has some access to top-of-the-line 21st century database technology, but still needs to communicate via telegrams. At the end of each business day, it might be nice to have a lot of automatically generated messages that can be turned into telegrams and sent to all the customers that visited the bank. We can give them the table name bank_record .

In addition to the customer service aspect, it could also be useful to track down imposters. If an imposter managed to withdraw funds from your account one day, at least you would get a telegram the same day letting you know that the funds were withdrawn!

This is a good case for a record that uses an id based on the time when the transaction took place, because then employees at the end of the day will be able to query all of the bank_record records in between midnight of that day and midnight of the rest.

We can implement this with a quick function that puts together a standard message for the customers of the bank. Note the time::format() function, which when followed by %Y-%m-%d will turn a datetime into a nice readable format like 2024-06-28 . The $transaction_type passed into the function will be a literal that can be one of two strings: “withrew”, or “received”.

DEFINE FUNCTION fn::customer_message($transaction_type: "withdrew" | "received") -> string {
    RETURN "Thank you for your patronage over the years. We see that you "
        + $transaction_type +
        " money at "
        + time::format(time::now(), "%Y-%m-%d")
        + ". If this is incorrect, please contact us at 67-98-10."
};

Whether to pass in “withdrew” or “received” can be determined by $before.talers to $after.talers inside the UPDATE event.

We should also check to see if $before.talers and $after.talers are equal, because if that is the case then the account balance has not changed and we don’t need to tell the user. We won’t check for a person’s pennings either, which are small units of money that nobody cares about.

All together, the event looks like this.

DEFINE EVENT make_statement
	ON person
	WHEN $event = "UPDATE" AND $before.talers != $after.talers THEN {
	LET $operation = IF $before.talers > $after.talers {
		"withdrew"
	} ELSE {
		"received"
	};
	CREATE bank_record SET
		id = <string>time::now(),
		note = fn::customer_message($operation);
};

And now we should be able to do the same transaction as in the last chapter. At the very end, we can then query the bank_record records for the whole day. Don’t forget to change the range to one that goes from today’s date to tomorrow’s date.

INSERT INTO person [
    { name: "Laurence Molkier", talers: 100, pennings: 50 },
    { name: "Asmodean Fitch",   talers: 200, pennings: 10 },
];
BEGIN TRANSACTION;
LET $sender = UPDATE ONLY person SET talers -= 10 WHERE name = "Laurence Molkier";
SLEEP 10ms;
LET $receiver = UPDATE ONLY person SET talers += 10 WHERE name = "Asmodean Fitch";
COMMIT TRANSACTION;

SELECT * FROM bank_record:`2024-09-02T00:00:00`..`2024-09-03T00:00:00`;

The output should now show two records, one for each customer. Not bad at all!

[
	{
		id: bank_record:⟨2024-09-02T06:57:39.241426Z⟩,
		note: 'Thank you for your patronage over the years. We see that you withdrew money at 2024-09-02. If this is incorrect, please contact us at 67-98-10. Thank you very much.'
	},
	{
		id: bank_record:⟨2024-09-02T06:57:39.256491Z⟩,
		note: 'Thank you for your patronage over the years. We see that you received money at 2024-09-02. If this is incorrect, please contact us at 67-98-10. Thank you very much.'
	}
]

DEFINE PARAM and incrementing numbers

The above solution would work pretty well for us in the 21st century. But what about the world in Aeon’s time in which the time::now() function doesn’t work? It’s likely that Aeon’s computer attempted to connect to a server to synchronize its time, failed, and defaulted to the date January 1, 1970. And in any case, the date in terms of the 21st century calendar would be meaningless for Aeon’s world because they use their own calendar to track time.

Fortunately, precise time to the millisecond or microsecond isn’t a necessity for them either. All they need is some sort of general database-wide parameter that can be referenced.

As we’ve seen in a few parts of the book, Aeon’s part of the world uses a calendar based on the number of years since an important date: the founding of Toria. At this part of the book, the current date for Aeon is 444-07-15: the 15th day of the 7th month, 444 years after the founding of Toria.

So how can this be automated for them? One way is to use the DEFINE PARAM statement, which creates a parameter that can be referenced across the entire database. The syntax is quite short: DEFINE PARAM , the parameter name, VALUE , and finally the value of the parameter itself.

DEFINE PARAM $DATE VALUE {
    year: 444,
    month: 7,
    day: 15
};

And since the people in Aeon’s time don’t need a precise time, they can use an incrementing number instead.

We learned back in Chapter 3 that an UPSERT statement on a record ID will create a record if it does not yet exist. This makes it a good way to create a counter. Take a look at the following query and see if you notice anything interesting about it.

UPSERT counter:aeons_counter SET value += 1;

Response
[
	{
		id: counter:aeons_counter,
		value: 1
	}
]

As we learned back in Chapter 4, instead of writing SET value = 1 (which also would have worked), we were able to write SET value += 1 . This is because SurrealDB is able to infer that value is a number, and since it was not yet set, it begins with its default value: 0.

Putting these two together, we can now start to see how a counter will work for Aeon’s team and their new banking system. They can use DEFINE PARAM OVERWRITE at the start of each day to set the new day, and follow the same UPDATE logic for each transaction.

DEFINE PARAM $DATE VALUE {
    year: 444,
    month: 7,
    day: 15
};
UPSERT counter:[$DATE] SET value += 1;

As the days go by, this will eventually lead to a nice output like the one below that shows the number of bank transactions that took place over each day. Let’s give this a try by overwriting the $DATE parameter a few times and UPSERT ing the counter multiple times to simulate a number of bank transactions.

DEFINE PARAM OVERWRITE $DATE VALUE {
    year: 444,
    month: 7,
    day: 16
};
UPSERT counter:[$DATE] SET value += 1;

After a few days, the query SELECT * FROM counter will produce an output like this which shows the number of transactions that took place per day.

[
	{
		id: counter:[
			{
				day: 15,
				month: 7,
				year: 444
			}
		],
		value: 23
	},
	{
		id: counter:[
			{
				day: 16,
				month: 7,
				year: 444
			}
		],
		value: 21
	},
	{
		id: counter:[
			{
				day: 17,
				month: 7,
				year: 444
			}
		],
		value: 29
	}
]

With this $DATE param and counter, we can now change our sample above a bit to fit Aeon’s world. We’ll also add a fn::readable_date() function to turn $DATE into something a bit more readable for a customer.

DEFINE PARAM $DATE VALUE {
    year: 444,
    month: 7,
    day: 15
};

DEFINE FUNCTION fn::readable_date() -> string {
    RETURN "day " + <string>$DATE.day + " of month " + <string>$DATE.month + " of year " + <string>$DATE.year
};

DEFINE FUNCTION fn::customer_message($transaction_type: string) -> string {
    RETURN "Thank you for your patronage over the years. We see that you "
        + $transaction_type +
        " money at "
        + fn::readable_date()
        + ". If this is incorrect, please contact us at 67-98-10."
};

DEFINE EVENT make_statement ON person WHEN $event = "UPDATE" THEN {
    LET $stamp = (UPSERT counter:[$DATE] SET value += 1);
    IF $before.talers > $after.talers {
        CREATE type::thing("bank_record", $stamp) SET note = fn::customer_message("withdrew");
    }
    ELSE IF $before.talers < $after.talers {
        CREATE type::thing("bank_record", $stamp) SET note = fn::customer_message("received");
    };
};

INSERT INTO person [
    { name: "Laurence Molkier", talers: 100, pennings: 50 },
    { name: "Asmodean Fitch",   talers: 200, pennings: 10 },
];

BEGIN TRANSACTION;
LET $sender =   UPDATE ONLY person SET talers -= 10 WHERE name = "Laurence Molkier";
LET $receiver = UPDATE ONLY person SET talers += 10 WHERE name = "Asmodean Fitch";
COMMIT TRANSACTION;

SELECT * FROM bank_record;

Precise timestamps won’t be available anymore, but at least they will be able to see the date at which a transaction was performed, and which transaction of the day it was.

[
	{
		id: bank_record:[
			{
				id: counter:[
					{
						day: 15,
						month: 7,
						year: 444
					}
				],
				value: 1
			}
		],
		note: 'Thank you for your patronage over the years. We see that you withdrew money at Day 15 of month 7 of year 444. If this is incorrect, please contact us at 67-98-10.'
	},
	{
		id: bank_record:[
			{
				id: counter:[
					{
						day: 15,
						month: 7,
						year: 444
					}
				],
				value: 2
			}
		],
		note: 'Thank you for your patronage over the years. We see that you received money at Day 15 of month 7 of year 444. If this is incorrect, please contact us at 67-98-10.'
	}
]

In the next chapter, we are going to take a deeper look at the tool we’ve been using throughout this book: Surrealist.

DEFINE EVENT watch_all ON person THEN {
    CREATE log SET time = time::now(), event = $event, before = $before, after = $after;
};

CREATE person:one;
UPDATE person SET name = "Billy";
DELETE person;

SELECT * FROM log;

[
	{
		after: {
			id: person:one,
			name: 'Billy'
		},
		before: {
			id: person:one
		},
		event: 'UPDATE',
		id: log:5gcl4yrkuq4psu76q3n3,
		time: d'2024-09-03T01:21:51.231213Z'
	},
	{
		before: {
			id: person:one,
			name: 'Billy'
		},
		event: 'DELETE',
		id: log:eavgxdmw6sqpne8vuxbo,
		time: d'2024-09-03T01:21:51.232075Z'
	},
	{
		after: {
			id: person:one
		},
		event: 'CREATE',
		id: log:jxjf6vrz0u9yzrdi7ge4,
		time: d'2024-09-03T01:21:51.228911Z'
	}
]

DEFINE ANALYZER long_ngram TOKENIZERS class FILTERS lowercase, ngram(3,15);

"natio" IN search::analyze("long_ngram", "international");

DEFINE ANALYZER long_ngram TOKENIZERS class FILTERS lowercase, ngram(3,15);
DEFINE INDEX nice_index ON TABLE text FIELDS content SEARCH ANALYZER long_ngram BM25 HIGHLIGHTS;

CREATE text SET content = "Many constructed auxiliary languages have been proposed as international languages. The first was known as Universalglot, and was created in...";
CREATE text SET content = "During the intermission, I helped myself to a glass of water. I then...";
CREATE text SET content = "The United Nations is headquarted in New York. It was established in...";

SELECT content FROM text WHERE content @@ "natio";

SELECT
	search::score(0) AS relevance,
	search::highlight('-', '-', 0) AS text
FROM text
WHERE content @0@ "nation";

Response
[
	{
		relevance: 1.670123815536499f,
		text: 'Many constructed auxiliary languages have been proposed as -international- languages. The first was known as Universalglot, and was created in...'
	},
	{
		relevance: 2.5412206649780273f,
		text: 'The United -Nations- is headquarted in New York. It was established in...'
	}
]

{
	id: date:of,
    year: 2024,
    month: 9,
}

DEFINE FUNCTION fn::make_date() {
    IF date:of.exists() {
        LET $last_month = date:of.month = 12;
        UPDATE date:of SET
            month = IF $last_month { 1 } ELSE { month + 1 },
            year = IF $last_month { year + 1 } ELSE { year }
    } ELSE {
        CREATE date:of SET
            month = 9,
            year = 2024;
    }
};