Core structure
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@@ -17,3 +17,8 @@ Two reasons.
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* SQlite or Postgres
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The use-case is (essentially) single-user.
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## API
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### /fetch/<b64:URL>
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@@ -7,5 +7,14 @@ import (
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)
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func main() {
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fmt.Printf("True: %s", domain64.ReturnsTrue())
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d64 := domain64.Domain64{
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TLD: 1,
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Domain: 1,
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Subdomain: 1,
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Path: 1,
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}
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result := d64.AsInt64()
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fmt.Printf("%064b\n", result)
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fmt.Printf("%016x\n", result)
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fmt.Println(result)
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}
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114
internal/domain64/README.md
Normal file
114
internal/domain64/README.md
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@@ -0,0 +1,114 @@
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# domain64
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`domain64` is a BIGINT (or 64-bit) type that can be used to encode all domains we are likely to encounter. It represents well as JSonnet/JSON, and can be used in partitioning database tables easily.
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## what is it
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To encode all of the TLDs, domains, and subdomains we will encounter, we'll use a `domain64` encoding. It maps the entire URL space into a single, 64-bit number (or, `BIGSERIAL` in Postgres).
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```mermaid
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packet-beta
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0-7: "FF | TLD"
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8-31: "FFFFFF | Domain"
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32-39: "FF | Subdomain"
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40-63: "FFFFFF | Path"
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```
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```
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FF:FFFFFF:FF:FFFFFF
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```
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or
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```
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tld:domain:subdomain:path
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```
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or
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```
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com:jadud:www:teaching:berea
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```
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can be indexed/partitioned uniquely.
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This lets us track
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* 255 (#FF) TLDs
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* 16,777,216 (#FFFFFF) domains under each TLD
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* 255 (#FF) subdomains under each domain
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* 16,777,216 (#FFFFFF) paths on a given domain
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## what that means
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There are only around 10 TLDs that make up the majority of all sites on the internet. The search engine maxes out at tracking 256 unique TLDs (#00-#FF).
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Each TLD can hold up to 16M unique sites. There are 302M `.com` domains, meaning , 36M `.cn`, and 20M `.org`. Again, this is for a "personal" search engine, and it is not intended to scale to handling all of the internet. Handling ~ 5% of `.com` (or 75% of `.org`) is *just fine*.
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Under a domain, it is possible to uniquely partition off 255 subdomains (where `00` is "no subdomain").
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Paths can be indexed uniquely, up to 16M per subdomain.
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## example
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```
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01:000001:00:000000 com.jadud
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01:000001:01:000000 gov.jadud.research
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01:000001:02:000000 gov.jadud.teaching
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01:000001:02:000001 gov.jadud.teaching/olin
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01:000001:02:000002 gov.jadud.teaching/berea
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```
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| tld | domain | sub | path | hex | dec |
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| --- | --- | --- | --- | --- | --- |
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| com | jadud | _ | _ | #x0100000100000000 | 72057598332895232 |
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| com | jadud | research | _ | #x0100000101000000 | 72057598332895488 |
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| com | jadud | teaching | _ | #x0100000102000000 | 72057598366449664 |
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| com | jadud | teaching | olin | #x0100000102000001 | 72057598366449665 |
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| com | jadud | teaching | berea | #x0100000102000002 | 72057598366449666 |
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## for partitioning
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On a table that contains a `domain64` value, we can partition based on numeric ranges very efficiently.
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```sql
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CREATE TABLE comjadud PARTITION OF com
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FOR VALUES FROM (0x0100000100000000) TO (0x01000001FFFFFFFF);
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```
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Or
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```sql
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CREATE TABLE comjadudresearch PARTITION OF com
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FOR VALUES FROM (0x0100000101000000) TO (0xx0100000101FFFFFF);
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```
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## As Jsonnet/JSON
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Jsonnet will naturally sort by the hex key values.
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```
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{
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"01": {
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"name": "com",
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"children": {
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"00000001": {
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"name": "jadud",
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"children": {
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"01": "research",
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"02": "teaching",
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}
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}
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}
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},
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"02": {
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"name": "org",
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"children": {
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...
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}
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}
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}
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```
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@@ -1,5 +1,32 @@
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package domain64
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func ReturnsTrue() bool {
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return true
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type Domain64 struct {
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// The TLD is FF
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TLD uint8
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// The Domain is FFFFFF, so the uint16 is the closest we'll get
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Domain uint16
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// Subdomains are FF
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Subdomain uint8
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// Paths are, again, FFFFFF
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Path uint16
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}
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/*
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```mermaid
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packet-beta
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0-7: "FF | TLD"
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8-31: "FFFFFF | Domain"
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32-39: "FF | Subdomain"
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40-63: "FFFFFF | Path"
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```
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*/
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func (d64 *Domain64) AsInt64() int64 {
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var result int64 = 0
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result = result | (int64(d64.TLD) << (64 - 8))
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result = result | (int64(d64.Domain) << (64 - (8 + 24)))
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result = result | (int64(d64.Subdomain) << (64 - (8 + 24 + 8)))
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result = result | (int64(d64.Path) << (64 - (8 + 24 + 8 + 24)))
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return result
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}
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// https://gobyexample.com/testing-and-benchmarking
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