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V3 casl 561 query tags #370

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Original file line number Diff line number Diff line change
Expand Up @@ -4,7 +4,7 @@ import kotlin.js.ExperimentalJsExport
import kotlin.js.JsExport

/**
* Please refer []Unicode Normalization Forms](https://www.unicode.org/reports/tr15/#Norm_Forms)
* Please refer [Unicode Normalization Forms](https://www.unicode.org/reports/tr15/#Norm_Forms)
*/
@OptIn(ExperimentalJsExport::class)
@JsExport
Expand Down
86 changes: 0 additions & 86 deletions here-naksha-lib-model/src/commonMain/kotlin/naksha/model/Tag.kt

This file was deleted.

54 changes: 38 additions & 16 deletions here-naksha-lib-model/src/commonMain/kotlin/naksha/model/TagList.kt
Original file line number Diff line number Diff line change
Expand Up @@ -3,7 +3,9 @@
package naksha.model

import naksha.base.ListProxy
import naksha.model.XyzNs.XyzNsCompanion.normalizeTag
import naksha.base.NormalizerForm
import naksha.base.Platform
import naksha.model.TagNormalizer.TagNormalizer_C.normalizeTag
import kotlin.js.JsExport
import kotlin.js.JsName
import kotlin.js.JsStatic
Expand All @@ -24,21 +26,6 @@ open class TagList() : ListProxy<String>(String::class) {
addTags(listOf(*tags), false)
}

companion object TagList_C {
/**
* Create a tag list from the given array; the tags are normalized.
* @param tags the tags.
* @return the tag-list.
*/
@JvmStatic
@JsStatic
fun fromArray(tags: Array<String>): TagList {
val list = TagList()
list.addAndNormalizeTags(*tags)
return list
}
}

/**
* Returns 'true' if the tag was removed, 'false' if it was not present.
*
Expand Down Expand Up @@ -161,9 +148,44 @@ open class TagList() : ListProxy<String>(String::class) {
return this
}


/**
* Convert this tag-list into a tag-map.
* @return this tag-list as tag-map.
*/
fun toTagMap(): TagMap = TagMap(this)

companion object TagList_C {
/**
* Create a tag list from the given array; the tags are normalized.
* @param tags the tags.
* @return the tag-list.
*/
@JvmStatic
@JsStatic
fun fromArray(tags: Array<String>): TagList {
val list = TagList()
list.addAndNormalizeTags(*tags)
return list
}

/**
* A method to normalize a list of tags.
*
* @param tags a list of tags.
* @return the same list, just that the content is normalized.
*/
@JvmStatic
@JsStatic
fun normalizeTags(tags: TagList?): TagList? {
if (!tags.isNullOrEmpty()) {
for ((idx, tag) in tags.withIndex()) {
if (tag != null) {
tags[idx] = normalizeTag(tag)
}
}
}
return tags
}
}
}
53 changes: 39 additions & 14 deletions here-naksha-lib-model/src/commonMain/kotlin/naksha/model/TagMap.kt
Original file line number Diff line number Diff line change
Expand Up @@ -2,25 +2,32 @@

package naksha.model

import naksha.base.Int64
import naksha.base.MapProxy
import naksha.model.request.query.*
import kotlin.js.JsExport
import kotlin.js.JsName

// TODO: Document me!
// Improve me!

/**
* Map of tags persisted as (key, value) pairs where values are nullable.
* This class represents the persisted form of [TagList].
* It is stored as byte_array and can be accessed in PG via `naksha_tags` function.
*
* It is advised to only construct it in one of two ways:
* 1) Via [TagList]-based constructor
* 2) By deserializing byte array fetched from DB
*
* If for some reason, one would like to use it otherwise, it is advised to properly prepare tags upfront
* with use of [TagNormalizer] (that is used for example by [TagList])
*/
@JsExport
open class TagMap() : MapProxy<String, Tag>(String::class, Tag::class) {
open class TagMap() : MapProxy<String, Any>(String::class, Any::class) {

@Suppress("LeakingThis")
@JsName("of")
constructor(tagList: TagList) : this(){
for (s in tagList) {
if (s == null) continue
val tag = Tag.parse(s)
put(tag.key, tag)
}
constructor(tagList: TagList) : this() {
tagList.filterNotNull()
.map { TagNormalizer.splitNormalizedTag(it) }
.forEach { (key, value) -> put(key, value) }
}

/**
Expand All @@ -29,10 +36,28 @@ open class TagMap() : MapProxy<String, Tag>(String::class, Tag::class) {
*/
fun toTagList(): TagList {
val list = TagList()
for (e in this) {
val tag = e.value?.tag
if (tag != null) list.add(tag)
forEach { (key, value) ->
list.add(flattenTag(key, value))
}
return list
}

/**
* Converts (key, value) pair to String, so it can be part of [TagList].
* The result depends on the value:
* - Null value is omitted: ('foo', null) -> 'foo'
* - String value is separated with simple '=': ('foo', 'bar') -> 'foo=bar'
* - Numbers and booleans are separated with ':=' -> 'foo:=true', 'foo:=12.34'
*/
private fun flattenTag(key: String, value: Any?): String =
when (value) {
null -> key
is String -> "$key=$value"
is Boolean, is Long, is Int64 -> "$key:=$value"
is Number -> "$key:=${value.toDouble()}"
else -> throw NakshaException(
NakshaError.ILLEGAL_ARGUMENT,
"Tag values can only be String, Boolean or Number"
)
}
}
Original file line number Diff line number Diff line change
@@ -0,0 +1,129 @@
package naksha.model

import naksha.base.NormalizerForm
import naksha.base.NormalizerForm.NFD
import naksha.base.NormalizerForm.NFKC
import naksha.base.Platform
import naksha.model.TagNormalizer.TagNormalizer_C.normalizeTag
import naksha.model.TagNormalizer.TagNormalizer_C.splitNormalizedTag

/**
* An object used for Tag normalization and splitting.
*
* Process of normalization happens in [normalizeTag] method and includes following steps:
* 1) Always: apply normalization form (see [NormalizerForm])
* 2) Conditional: lowercase the whole tag
* 3) Conditional: remove all non-ASCII characters
*
* Normalization form used in step #1 and subsequent conditional steps depend on tag prefix.
*
* Process of splitting happens in [splitNormalizedTag] method.
* Note that not all tags can be split, it depends on their prefix.
*
* Summarised per-prefix behavior:
* +----------+------------+-----------+----------+-------+
* | prefix | norm. form | lowercase | no ASCII | split |
* +----------+------------+-----------+----------+-------+
* | @ | NFKC | false | false | true |
* | ref_ | NFKC | false | false | false |
* | ~ | NFD | false | true | true |
* | # | NFD | false | true | true |
* | sourceID | NFKC | false | false | false |
* | < ELSE > | NFD | true | true | true |
* +----------+------------+-----------+----------+-------+
*
* By default, (if no special prefix is found) tag is normalized with NFD, lowercased, cleaned of non-ASCII and splittable.
*/
class TagNormalizer private constructor() {
private data class TagProcessingPolicy(
val normalizerForm: NormalizerForm,
val removeNonAscii: Boolean,
val lowercase: Boolean,
val split: Boolean
)

companion object TagNormalizer_C {
private val DEFAULT_POLICY =
TagProcessingPolicy(NFD, removeNonAscii = true, lowercase = true, split = true)
private val PREFIX_TO_POLICY = mapOf(
"@" to TagProcessingPolicy( NFKC, removeNonAscii = false, lowercase = false, split = true),
"ref_" to TagProcessingPolicy( NFKC, removeNonAscii = false, lowercase = false, split = false),
"sourceID" to TagProcessingPolicy( NFKC, removeNonAscii = false, lowercase = false, split = false),
"~" to TagProcessingPolicy(NFD, removeNonAscii = true, lowercase = false, split = true),
"#" to TagProcessingPolicy(NFD, removeNonAscii = true, lowercase = false, split = true)
)

private val AS_IS: CharArray = CharArray(128 - 32) { (it + 32).toChar() }
private val TO_LOWER: CharArray = CharArray(128 - 32) { (it + 32).toChar().lowercaseChar() }

/**
* Main method for raw tag normalization. See[TagNormalizer] doc for more
*/
fun normalizeTag(tag: String): String {
val policy = policyFor(tag)
val normalized = Platform.normalize(tag, policy.normalizerForm)
return if (policy.lowercase) {
if (policy.removeNonAscii) {
removeNonAscii(normalized, TO_LOWER)
} else {
normalized.lowercase()
}
} else if (policy.removeNonAscii){
removeNonAscii(normalized, AS_IS)
} else {
normalized
}
}

private fun removeNonAscii(input: String, outputCharacterSet: CharArray): String {
val sb = StringBuilder()
for (element in input) {
val c = (element.code - 32).toChar()
if (c.code < outputCharacterSet.size) {
sb.append(outputCharacterSet[c.code])
}
}
return sb.toString()
}


/**
* Main method for normalized tag splitting. See[TagNormalizer] doc for more
*/
fun splitNormalizedTag(normalizedTag: String): Pair<String, Any?> {
if (!policyFor(normalizedTag).split) {
return normalizedTag to null
}
val i = normalizedTag.indexOf('=')
val key: String
val value: Any?
if (i > 1) {
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This will fail for a=1, because in this case index will be 1

if (normalizedTag[i - 1] == ':') { // :=
key = normalizedTag.substring(0, i - 1).trim()
val raw = normalizedTag.substring(i + 1).trim()
value = if ("true".equals(raw, ignoreCase = true)) {
true
} else if ("false".equals(raw, ignoreCase = true)) {
false
} else {
raw.toDouble()
}
} else {
key = normalizedTag.substring(0, i).trim()
value = normalizedTag.substring(i + 1).trim()
}
} else {
key = normalizedTag
value = null
}
return key to value
}

private fun policyFor(tag: String): TagProcessingPolicy {
for ((prefix, policy) in PREFIX_TO_POLICY) {
if (tag.startsWith(prefix)) return policy
}
return DEFAULT_POLICY
}
}
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The code is wrong in one thing: The prefix is case-sensitive, so ignoreCase must not be used, Ref_ != ref_!

Actually, another thing of this notation is, that it is hard to read for everyone that is not an Java/Kotlin expert. I would like to avoid streaming-APIs, unless there is a big benefit, so this code should be (IMHO):

private fun policyFor(tag: String): TagProcessingPolicy {
  for ((prefix, policy) in PREFIX_TO_POLICY) {
    if (tag.startsWith(prefix)) return policy
  }
  return DEFAULT_POLICY
}

This does not make the code slower, nor longer, but much more readable IMHO. Lets please use loops, unless there is realy specific situation in which a streaming-api provides an advantage.

There is another point against streaming API, I saw this so often now. People tend to ignore, that behind a stream there is a loop, and you then often find things like this, especially with inexperienced developers:

val a = m.map { (k, v) -> v.a }
val b = m.map { (k, v) -> v.b }
...

I really saw this often. If that would have been a loop, it would become clear that you can collect both properties in one loop, you do not need two loops, but the code hides the loop and therefore makes it hard to understand what happens.

When loops can be collapsed, it should be done, looping above things is not free of cost IMHO.

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The firstOrNull triggers loop that exits on the first predicate-check or return nulls if nothing satisfied the condition. The loop runs only once. As you mentioned - all map, filter etc operations trigger traversal, sometimes its full, sometimes not (like in this original case).

The remarkd regarding readability is more crucial IMO, if you don't "feel it" then let's ditch it - c-like style should be fine for all so I'm switching to your suggestion

}
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