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https://github.com/meilisearch/meilisearch.git
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Merge #556
556: Add EXISTS filter r=loiclec a=loiclec ## What does this PR do? Fixes issue [#2484](https://github.com/meilisearch/meilisearch/issues/2484) in the meilisearch repo. It creates a `field EXISTS` filter which selects all documents containing the `field` key. For example, with the following documents: ```json [{ "id": 0, "colour": [] }, { "id": 1, "colour": ["blue", "green"] }, { "id": 2, "colour": 145238 }, { "id": 3, "colour": null }, { "id": 4, "colour": { "green": [] } }, { "id": 5, "colour": {} }, { "id": 6 }] ``` Then the filter `colour EXISTS` selects the ids `[0, 1, 2, 3, 4, 5]`. The filter `colour NOT EXISTS` selects `[6]`. ## Details There is a new database named `facet-id-exists-docids`. Its keys are field ids and its values are bitmaps of all the document ids where the corresponding field exists. To create this database, the indexing part of milli had to be adapted. The implementation there is basically copy/pasted from the code handling the `facet-id-f64-docids` database, with appropriate modifications in place. There was an issue involving the flattening of documents during (re)indexing. Previously, the following JSON: ```json { "id": 0, "colour": [], "size": {} } ``` would be flattened to: ```json { "id": 0 } ``` prior to being given to the extraction pipeline. This transformation would lose the information that is needed to populate the `facet-id-exists-docids` database. Therefore, I have also changed the implementation of the `flatten-serde-json` crate. Now, as it traverses the Json, it keeps track of which key was encountered. Then, at the end, if a previously encountered key is not present in the flattened object, it adds that key to the object with an empty array as value. For example: ```json { "id": 0, "colour": { "green": [], "blue": 1 }, "size": {} } ``` becomes ```json { "id": 0, "colour": [], "colour.green": [], "colour.blue": 1, "size": [] } ``` Co-authored-by: Kerollmops <clement@meilisearch.com>
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@ -6,6 +6,8 @@ mod facet_string_zero_bounds_value_codec;
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mod field_doc_id_facet_f64_codec;
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mod field_doc_id_facet_string_codec;
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use heed::types::OwnedType;
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pub use self::facet_level_value_f64_codec::FacetLevelValueF64Codec;
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pub use self::facet_level_value_u32_codec::FacetLevelValueU32Codec;
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pub use self::facet_string_level_zero_codec::FacetStringLevelZeroCodec;
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@ -15,6 +17,9 @@ pub use self::facet_string_level_zero_value_codec::{
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pub use self::facet_string_zero_bounds_value_codec::FacetStringZeroBoundsValueCodec;
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pub use self::field_doc_id_facet_f64_codec::FieldDocIdFacetF64Codec;
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pub use self::field_doc_id_facet_string_codec::FieldDocIdFacetStringCodec;
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use crate::BEU16;
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pub type FieldIdCodec = OwnedType<BEU16>;
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/// Tries to split a slice in half at the given middle point,
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/// `None` if the slice is too short.
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@ -15,13 +15,13 @@ use crate::error::{InternalError, UserError};
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use crate::fields_ids_map::FieldsIdsMap;
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use crate::heed_codec::facet::{
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FacetLevelValueF64Codec, FacetStringLevelZeroCodec, FacetStringLevelZeroValueCodec,
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FieldDocIdFacetF64Codec, FieldDocIdFacetStringCodec,
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FieldDocIdFacetF64Codec, FieldDocIdFacetStringCodec, FieldIdCodec,
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};
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use crate::{
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default_criteria, BEU32StrCodec, BoRoaringBitmapCodec, CboRoaringBitmapCodec, Criterion,
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DocumentId, ExternalDocumentsIds, FacetDistribution, FieldDistribution, FieldId,
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FieldIdWordCountCodec, GeoPoint, ObkvCodec, Result, RoaringBitmapCodec, RoaringBitmapLenCodec,
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Search, StrBEU32Codec, StrStrU8Codec, BEU32,
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Search, StrBEU32Codec, StrStrU8Codec, BEU16, BEU32,
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};
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pub const DEFAULT_MIN_WORD_LEN_ONE_TYPO: u8 = 5;
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@ -75,6 +75,7 @@ pub mod db_name {
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pub const WORD_PREFIX_POSITION_DOCIDS: &str = "word-prefix-position-docids";
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pub const FIELD_ID_WORD_COUNT_DOCIDS: &str = "field-id-word-count-docids";
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pub const FACET_ID_F64_DOCIDS: &str = "facet-id-f64-docids";
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pub const FACET_ID_EXISTS_DOCIDS: &str = "facet-id-exists-docids";
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pub const FACET_ID_STRING_DOCIDS: &str = "facet-id-string-docids";
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pub const FIELD_ID_DOCID_FACET_F64S: &str = "field-id-docid-facet-f64s";
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pub const FIELD_ID_DOCID_FACET_STRINGS: &str = "field-id-docid-facet-strings";
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@ -116,6 +117,9 @@ pub struct Index {
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/// Maps the position of a word prefix with all the docids where this prefix appears.
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pub word_prefix_position_docids: Database<StrBEU32Codec, CboRoaringBitmapCodec>,
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/// Maps the facet field id and the docids for which this field exists
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pub facet_id_exists_docids: Database<FieldIdCodec, CboRoaringBitmapCodec>,
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/// Maps the facet field id, level and the number with the docids that corresponds to it.
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pub facet_id_f64_docids: Database<FacetLevelValueF64Codec, CboRoaringBitmapCodec>,
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/// Maps the facet field id and the string with the original string and docids that corresponds to it.
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@ -134,7 +138,7 @@ impl Index {
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pub fn new<P: AsRef<Path>>(mut options: heed::EnvOpenOptions, path: P) -> Result<Index> {
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use db_name::*;
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options.max_dbs(16);
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options.max_dbs(17);
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unsafe { options.flag(Flags::MdbAlwaysFreePages) };
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let env = options.open(path)?;
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@ -152,6 +156,8 @@ impl Index {
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let word_prefix_position_docids = env.create_database(Some(WORD_PREFIX_POSITION_DOCIDS))?;
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let facet_id_f64_docids = env.create_database(Some(FACET_ID_F64_DOCIDS))?;
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let facet_id_string_docids = env.create_database(Some(FACET_ID_STRING_DOCIDS))?;
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let facet_id_exists_docids = env.create_database(Some(FACET_ID_EXISTS_DOCIDS))?;
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let field_id_docid_facet_f64s = env.create_database(Some(FIELD_ID_DOCID_FACET_F64S))?;
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let field_id_docid_facet_strings =
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env.create_database(Some(FIELD_ID_DOCID_FACET_STRINGS))?;
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@ -174,6 +180,7 @@ impl Index {
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field_id_word_count_docids,
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facet_id_f64_docids,
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facet_id_string_docids,
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facet_id_exists_docids,
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field_id_docid_facet_f64s,
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field_id_docid_facet_strings,
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documents,
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@ -806,6 +813,18 @@ impl Index {
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}
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}
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/// Retrieve all the documents which contain this field id
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pub fn exists_faceted_documents_ids(
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&self,
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rtxn: &RoTxn,
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field_id: FieldId,
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) -> heed::Result<RoaringBitmap> {
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match self.facet_id_exists_docids.get(rtxn, &BEU16::new(field_id))? {
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Some(docids) => Ok(docids),
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None => Ok(RoaringBitmap::new()),
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}
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}
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/* distinct field */
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pub(crate) fn put_distinct_field(
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@ -44,6 +44,7 @@ pub use self::search::{
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pub type Result<T> = std::result::Result<T, error::Error>;
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pub type Attribute = u32;
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pub type BEU16 = heed::zerocopy::U16<heed::byteorder::BE>;
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pub type BEU32 = heed::zerocopy::U32<heed::byteorder::BE>;
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pub type BEU64 = heed::zerocopy::U64<heed::byteorder::BE>;
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pub type DocumentId = u32;
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@ -280,6 +280,18 @@ impl<'a> Filter<'a> {
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Condition::LowerThan(val) => (Included(f64::MIN), Excluded(val.parse()?)),
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Condition::LowerThanOrEqual(val) => (Included(f64::MIN), Included(val.parse()?)),
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Condition::Between { from, to } => (Included(from.parse()?), Included(to.parse()?)),
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Condition::Exists => {
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let exist = index.exists_faceted_documents_ids(rtxn, field_id)?;
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return Ok(exist);
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}
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Condition::NotExists => {
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let all_ids = index.documents_ids(rtxn)?;
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let exist = Self::evaluate_operator(rtxn, index, field_id, &Condition::Exists)?;
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let notexist = all_ids - exist;
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return Ok(notexist);
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}
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Condition::Equal(val) => {
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let (_original_value, string_docids) = strings_db
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.get(rtxn, &(field_id, &val.value().to_lowercase()))?
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@ -30,6 +30,7 @@ impl<'t, 'u, 'i> ClearDocuments<'t, 'u, 'i> {
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word_prefix_position_docids,
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facet_id_f64_docids,
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facet_id_string_docids,
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facet_id_exists_docids,
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field_id_docid_facet_f64s,
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field_id_docid_facet_strings,
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documents,
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@ -69,6 +70,7 @@ impl<'t, 'u, 'i> ClearDocuments<'t, 'u, 'i> {
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field_id_word_count_docids.clear(self.wtxn)?;
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word_prefix_position_docids.clear(self.wtxn)?;
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facet_id_f64_docids.clear(self.wtxn)?;
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facet_id_exists_docids.clear(self.wtxn)?;
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facet_id_string_docids.clear(self.wtxn)?;
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field_id_docid_facet_f64s.clear(self.wtxn)?;
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field_id_docid_facet_strings.clear(self.wtxn)?;
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@ -170,6 +170,7 @@ impl<'t, 'u, 'i> DeleteDocuments<'t, 'u, 'i> {
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word_position_docids,
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word_prefix_position_docids,
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facet_id_f64_docids,
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facet_id_exists_docids,
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facet_id_string_docids,
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field_id_docid_facet_f64s,
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field_id_docid_facet_strings,
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@ -424,11 +425,17 @@ impl<'t, 'u, 'i> DeleteDocuments<'t, 'u, 'i> {
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}
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// We delete the documents ids that are under the facet field id values.
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remove_docids_from_facet_field_id_number_docids(
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remove_docids_from_facet_field_id_docids(
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self.wtxn,
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facet_id_f64_docids,
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&self.to_delete_docids,
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)?;
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// We delete the documents ids that are under the facet field id values.
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remove_docids_from_facet_field_id_docids(
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self.wtxn,
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facet_id_exists_docids,
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&self.to_delete_docids,
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)?;
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remove_docids_from_facet_field_id_string_docids(
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self.wtxn,
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@ -618,7 +625,7 @@ fn remove_docids_from_facet_field_id_string_docids<'a, C, D>(
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Ok(())
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}
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fn remove_docids_from_facet_field_id_number_docids<'a, C>(
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fn remove_docids_from_facet_field_id_docids<'a, C>(
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wtxn: &'a mut heed::RwTxn,
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db: &heed::Database<C, CboRoaringBitmapCodec>,
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to_remove: &RoaringBitmap,
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@ -1,15 +1,19 @@
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use std::collections::HashSet;
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use std::collections::{BTreeMap, HashSet};
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use std::convert::TryInto;
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use std::fs::File;
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use std::io;
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use std::mem::size_of;
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use heed::zerocopy::AsBytes;
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use heed::BytesEncode;
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use roaring::RoaringBitmap;
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use serde_json::Value;
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use super::helpers::{create_sorter, keep_first, sorter_into_reader, GrenadParameters};
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use crate::error::InternalError;
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use crate::facet::value_encoding::f64_into_bytes;
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use crate::{DocumentId, FieldId, Result};
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use crate::update::index_documents::{create_writer, writer_into_reader};
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use crate::{CboRoaringBitmapCodec, DocumentId, FieldId, Result, BEU32};
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/// Extracts the facet values of each faceted field of each document.
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///
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@ -20,7 +24,7 @@ pub fn extract_fid_docid_facet_values<R: io::Read + io::Seek>(
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obkv_documents: grenad::Reader<R>,
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indexer: GrenadParameters,
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faceted_fields: &HashSet<FieldId>,
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) -> Result<(grenad::Reader<File>, grenad::Reader<File>)> {
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) -> Result<(grenad::Reader<File>, grenad::Reader<File>, grenad::Reader<File>)> {
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let max_memory = indexer.max_memory_by_thread();
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let mut fid_docid_facet_numbers_sorter = create_sorter(
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@ -39,6 +43,8 @@ pub fn extract_fid_docid_facet_values<R: io::Read + io::Seek>(
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max_memory.map(|m| m / 2),
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);
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let mut facet_exists_docids = BTreeMap::<FieldId, RoaringBitmap>::new();
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let mut key_buffer = Vec::new();
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let mut cursor = obkv_documents.into_cursor()?;
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while let Some((docid_bytes, value)) = cursor.move_on_next()? {
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@ -46,16 +52,26 @@ pub fn extract_fid_docid_facet_values<R: io::Read + io::Seek>(
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for (field_id, field_bytes) in obkv.iter() {
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if faceted_fields.contains(&field_id) {
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let value =
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serde_json::from_slice(field_bytes).map_err(InternalError::SerdeJson)?;
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let (numbers, strings) = extract_facet_values(&value);
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key_buffer.clear();
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// prefix key with the field_id and the document_id
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// Set key to the field_id
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// Note: this encoding is consistent with FieldIdCodec
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key_buffer.extend_from_slice(&field_id.to_be_bytes());
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// Here, we know already that the document must be added to the “field id exists” database
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let document: [u8; 4] = docid_bytes[..4].try_into().ok().unwrap();
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let document = BEU32::from(document).get();
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facet_exists_docids.entry(field_id).or_default().insert(document);
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// For the other extraction tasks, prefix the key with the field_id and the document_id
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key_buffer.extend_from_slice(&docid_bytes);
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let value =
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serde_json::from_slice(field_bytes).map_err(InternalError::SerdeJson)?;
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let (numbers, strings) = extract_facet_values(&value);
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// insert facet numbers in sorter
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for number in numbers {
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key_buffer.truncate(size_of::<FieldId>() + size_of::<DocumentId>());
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@ -77,9 +93,21 @@ pub fn extract_fid_docid_facet_values<R: io::Read + io::Seek>(
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}
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}
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let mut facet_exists_docids_writer = create_writer(
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indexer.chunk_compression_type,
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indexer.chunk_compression_level,
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tempfile::tempfile()?,
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);
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for (fid, bitmap) in facet_exists_docids.into_iter() {
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let bitmap_bytes = CboRoaringBitmapCodec::bytes_encode(&bitmap).unwrap();
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facet_exists_docids_writer.insert(fid.to_be_bytes(), &bitmap_bytes)?;
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}
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let facet_exists_docids_reader = writer_into_reader(facet_exists_docids_writer)?;
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Ok((
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sorter_into_reader(fid_docid_facet_numbers_sorter, indexer.clone())?,
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sorter_into_reader(fid_docid_facet_strings_sorter, indexer)?,
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sorter_into_reader(fid_docid_facet_strings_sorter, indexer.clone())?,
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facet_exists_docids_reader,
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))
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}
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@ -53,7 +53,7 @@ pub(crate) fn data_from_obkv_documents(
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})
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.collect::<Result<()>>()?;
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let result: Result<(Vec<_>, (Vec<_>, Vec<_>))> = flattened_obkv_chunks
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let result: Result<(Vec<_>, (Vec<_>, (Vec<_>, Vec<_>)))> = flattened_obkv_chunks
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.par_bridge()
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.map(|flattened_obkv_chunks| {
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send_and_extract_flattened_documents_data(
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@ -72,9 +72,28 @@ pub(crate) fn data_from_obkv_documents(
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let (
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docid_word_positions_chunks,
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(docid_fid_facet_numbers_chunks, docid_fid_facet_strings_chunks),
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(
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docid_fid_facet_numbers_chunks,
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(docid_fid_facet_strings_chunks, facet_exists_docids_chunks),
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),
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) = result?;
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// merge facet_exists_docids and send them as a typed chunk
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{
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let lmdb_writer_sx = lmdb_writer_sx.clone();
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rayon::spawn(move || {
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debug!("merge {} database", "facet-id-exists-docids");
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match facet_exists_docids_chunks.merge(merge_cbo_roaring_bitmaps, &indexer) {
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Ok(reader) => {
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let _ = lmdb_writer_sx.send(Ok(TypedChunk::FieldIdFacetExistsDocids(reader)));
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}
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Err(e) => {
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let _ = lmdb_writer_sx.send(Err(e));
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}
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}
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});
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}
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spawn_extraction_task::<_, _, Vec<grenad::Reader<File>>>(
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docid_word_positions_chunks.clone(),
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indexer.clone(),
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@ -197,6 +216,7 @@ fn send_original_documents_data(
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/// - docid_word_positions
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/// - docid_fid_facet_numbers
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/// - docid_fid_facet_strings
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/// - docid_fid_facet_exists
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fn send_and_extract_flattened_documents_data(
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flattened_documents_chunk: Result<grenad::Reader<File>>,
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indexer: GrenadParameters,
|
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@ -209,7 +229,10 @@ fn send_and_extract_flattened_documents_data(
|
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max_positions_per_attributes: Option<u32>,
|
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) -> Result<(
|
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grenad::Reader<CursorClonableMmap>,
|
||||
(grenad::Reader<CursorClonableMmap>, grenad::Reader<CursorClonableMmap>),
|
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(
|
||||
grenad::Reader<CursorClonableMmap>,
|
||||
(grenad::Reader<CursorClonableMmap>, grenad::Reader<File>),
|
||||
),
|
||||
)> {
|
||||
let flattened_documents_chunk =
|
||||
flattened_documents_chunk.and_then(|c| unsafe { as_cloneable_grenad(&c) })?;
|
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@ -250,12 +273,15 @@ fn send_and_extract_flattened_documents_data(
|
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Ok(docid_word_positions_chunk)
|
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},
|
||||
|| {
|
||||
let (docid_fid_facet_numbers_chunk, docid_fid_facet_strings_chunk) =
|
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extract_fid_docid_facet_values(
|
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flattened_documents_chunk.clone(),
|
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indexer.clone(),
|
||||
faceted_fields,
|
||||
)?;
|
||||
let (
|
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docid_fid_facet_numbers_chunk,
|
||||
docid_fid_facet_strings_chunk,
|
||||
fid_facet_exists_docids_chunk,
|
||||
) = extract_fid_docid_facet_values(
|
||||
flattened_documents_chunk.clone(),
|
||||
indexer.clone(),
|
||||
faceted_fields,
|
||||
)?;
|
||||
|
||||
// send docid_fid_facet_numbers_chunk to DB writer
|
||||
let docid_fid_facet_numbers_chunk =
|
||||
@ -273,7 +299,10 @@ fn send_and_extract_flattened_documents_data(
|
||||
docid_fid_facet_strings_chunk.clone(),
|
||||
)));
|
||||
|
||||
Ok((docid_fid_facet_numbers_chunk, docid_fid_facet_strings_chunk))
|
||||
Ok((
|
||||
docid_fid_facet_numbers_chunk,
|
||||
(docid_fid_facet_strings_chunk, fid_facet_exists_docids_chunk),
|
||||
))
|
||||
},
|
||||
);
|
||||
|
||||
|
||||
@ -613,6 +613,7 @@ mod tests {
|
||||
use super::*;
|
||||
use crate::documents::DocumentsBatchBuilder;
|
||||
use crate::update::DeleteDocuments;
|
||||
use crate::BEU16;
|
||||
|
||||
#[test]
|
||||
fn simple_document_replacement() {
|
||||
@ -2040,6 +2041,109 @@ mod tests {
|
||||
assert_eq!(ids.len(), map.len());
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn index_documents_check_exists_database() {
|
||||
let config = IndexerConfig::default();
|
||||
let indexing_config = IndexDocumentsConfig::default();
|
||||
|
||||
let faceted_fields = hashset!(S("colour"));
|
||||
let content = || {
|
||||
documents!([
|
||||
{
|
||||
"id": 0,
|
||||
"colour": 0,
|
||||
},
|
||||
{
|
||||
"id": 1,
|
||||
"colour": []
|
||||
},
|
||||
{
|
||||
"id": 2,
|
||||
"colour": {}
|
||||
},
|
||||
{
|
||||
"id": 3,
|
||||
"colour": null
|
||||
},
|
||||
{
|
||||
"id": 4,
|
||||
"colour": [1]
|
||||
},
|
||||
{
|
||||
"id": 5
|
||||
},
|
||||
{
|
||||
"id": 6,
|
||||
"colour": {
|
||||
"green": 1
|
||||
}
|
||||
},
|
||||
{
|
||||
"id": 7,
|
||||
"colour": {
|
||||
"green": {
|
||||
"blue": []
|
||||
}
|
||||
}
|
||||
}
|
||||
])
|
||||
};
|
||||
let make_index = || {
|
||||
let path = tempfile::tempdir().unwrap();
|
||||
let mut options = EnvOpenOptions::new();
|
||||
options.map_size(10 * 1024 * 1024); // 10 MB
|
||||
Index::new(options, &path).unwrap()
|
||||
};
|
||||
|
||||
let set_filterable_fields = |index: &Index| {
|
||||
let mut wtxn = index.write_txn().unwrap();
|
||||
let mut builder = update::Settings::new(&mut wtxn, &index, &config);
|
||||
builder.set_filterable_fields(faceted_fields.clone());
|
||||
builder.execute(|_| ()).unwrap();
|
||||
wtxn.commit().unwrap();
|
||||
};
|
||||
let add_documents = |index: &Index| {
|
||||
let mut wtxn = index.write_txn().unwrap();
|
||||
let builder =
|
||||
IndexDocuments::new(&mut wtxn, index, &config, indexing_config.clone(), |_| ())
|
||||
.unwrap();
|
||||
let (builder, user_error) = builder.add_documents(content()).unwrap();
|
||||
user_error.unwrap();
|
||||
builder.execute().unwrap();
|
||||
wtxn.commit().unwrap();
|
||||
};
|
||||
|
||||
let check_ok = |index: &Index| {
|
||||
let rtxn = index.read_txn().unwrap();
|
||||
let facets = index.faceted_fields(&rtxn).unwrap();
|
||||
assert_eq!(facets, hashset!(S("colour"), S("colour.green"), S("colour.green.blue")));
|
||||
|
||||
let colour_id = index.fields_ids_map(&rtxn).unwrap().id("colour").unwrap();
|
||||
let colour_green_id = index.fields_ids_map(&rtxn).unwrap().id("colour.green").unwrap();
|
||||
|
||||
let bitmap_colour =
|
||||
index.facet_id_exists_docids.get(&rtxn, &BEU16::new(colour_id)).unwrap().unwrap();
|
||||
assert_eq!(bitmap_colour.into_iter().collect::<Vec<_>>(), vec![0, 1, 2, 3, 4, 6, 7]);
|
||||
|
||||
let bitmap_colour_green = index
|
||||
.facet_id_exists_docids
|
||||
.get(&rtxn, &BEU16::new(colour_green_id))
|
||||
.unwrap()
|
||||
.unwrap();
|
||||
assert_eq!(bitmap_colour_green.into_iter().collect::<Vec<_>>(), vec![6, 7]);
|
||||
};
|
||||
|
||||
let index = make_index();
|
||||
add_documents(&index);
|
||||
set_filterable_fields(&index);
|
||||
check_ok(&index);
|
||||
|
||||
let index = make_index();
|
||||
set_filterable_fields(&index);
|
||||
add_documents(&index);
|
||||
check_ok(&index);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn primary_key_must_not_contain_floats() {
|
||||
let tmp = tempfile::tempdir().unwrap();
|
||||
|
||||
@ -35,6 +35,7 @@ pub(crate) enum TypedChunk {
|
||||
WordPairProximityDocids(grenad::Reader<File>),
|
||||
FieldIdFacetStringDocids(grenad::Reader<File>),
|
||||
FieldIdFacetNumberDocids(grenad::Reader<File>),
|
||||
FieldIdFacetExistsDocids(grenad::Reader<File>),
|
||||
GeoPoints(grenad::Reader<File>),
|
||||
}
|
||||
|
||||
@ -146,6 +147,17 @@ pub(crate) fn write_typed_chunk_into_index(
|
||||
)?;
|
||||
is_merged_database = true;
|
||||
}
|
||||
TypedChunk::FieldIdFacetExistsDocids(facet_id_exists_docids) => {
|
||||
append_entries_into_database(
|
||||
facet_id_exists_docids,
|
||||
&index.facet_id_exists_docids,
|
||||
wtxn,
|
||||
index_is_empty,
|
||||
|value, _buffer| Ok(value),
|
||||
merge_cbo_roaring_bitmaps,
|
||||
)?;
|
||||
is_merged_database = true;
|
||||
}
|
||||
TypedChunk::WordPairProximityDocids(word_pair_proximity_docids_iter) => {
|
||||
append_entries_into_database(
|
||||
word_pair_proximity_docids_iter,
|
||||
|
||||
@ -1,17 +1,17 @@
|
||||
{"id":"A","word_rank":0,"typo_rank":1,"proximity_rank":15,"attribute_rank":505,"exact_rank":5,"asc_desc_rank":0,"sort_by_rank":0,"geo_rank":43,"title":"hell o","description":"hell o is the fourteenth episode of the american television series glee performing songs with this word","tag":"blue","_geo": { "lat": 50.62984446145472, "lng": 3.085712705162039 },"":""}
|
||||
{"id":"B","word_rank":2,"typo_rank":0,"proximity_rank":0,"attribute_rank":0,"exact_rank":4,"asc_desc_rank":1,"sort_by_rank":2,"geo_rank":191,"title":"hello","description":"hello is a song recorded by english singer songwriter adele","tag":"red","_geo": { "lat": 50.63047567664291, "lng": 3.088852230809636 },"":""}
|
||||
{"id":"C","word_rank":0,"typo_rank":1,"proximity_rank":8,"attribute_rank":336,"exact_rank":4,"asc_desc_rank":2,"sort_by_rank":0,"geo_rank":283,"title":"hell on earth","description":"hell on earth is the third studio album by american hip hop duo mobb deep","tag":"blue","_geo": { "lat": 50.6321800003937, "lng": 3.088331882262139 },"":""}
|
||||
{"id":"D","word_rank":0,"typo_rank":1,"proximity_rank":10,"attribute_rank":757,"exact_rank":4,"asc_desc_rank":3,"sort_by_rank":2,"geo_rank":1381,"title":"hell on wheels tv series","description":"the construction of the first transcontinental railroad across the united states in the world","tag":"red","_geo": { "lat": 50.63728851135729, "lng": 3.0703951595971626 },"":""}
|
||||
{"id":"E","word_rank":2,"typo_rank":0,"proximity_rank":0,"attribute_rank":0,"exact_rank":4,"asc_desc_rank":4,"sort_by_rank":1,"geo_rank":1979,"title":"hello kitty","description":"also known by her full name kitty white is a fictional character produced by the japanese company sanrio","tag":"green","_geo": { "lat": 50.64264610511925, "lng": 3.0665099941857634 },"":""}
|
||||
{"id":"F","word_rank":2,"typo_rank":1,"proximity_rank":0,"attribute_rank":1017,"exact_rank":5,"asc_desc_rank":5,"sort_by_rank":0,"geo_rank":65022,"title":"laptop orchestra","description":"a laptop orchestra lork or lo is a chamber music ensemble consisting primarily of laptops like helo huddersfield experimental laptop orchestra","tag":"blue","_geo": { "lat": 51.05028653642387, "lng": 3.7301072771642096 },"":""}
|
||||
{"id":"G","word_rank":1,"typo_rank":0,"proximity_rank":0,"attribute_rank":0,"exact_rank":3,"asc_desc_rank":5,"sort_by_rank":2,"geo_rank":34692,"title":"hello world film","description":"hello world is a 2019 japanese animated sci fi romantic drama film directed by tomohiko ito and produced by graphinica","tag":"red","_geo": { "lat": 50.78776041427129, "lng": 2.661201766290338 },"":""}
|
||||
{"id":"H","word_rank":1,"typo_rank":0,"proximity_rank":1,"attribute_rank":0,"exact_rank":3,"asc_desc_rank":4,"sort_by_rank":1,"geo_rank":202182,"title":"world hello day","description":"holiday observed on november 21 to express that conflicts should be resolved through communication rather than the use of force","tag":"green","_geo": { "lat": 48.875617484531965, "lng": 2.346747821504194 },"":""}
|
||||
{"id":"A","word_rank":0,"typo_rank":1,"proximity_rank":15,"attribute_rank":505,"exact_rank":5,"asc_desc_rank":0,"sort_by_rank":0,"geo_rank":43,"title":"hell o","description":"hell o is the fourteenth episode of the american television series glee performing songs with this word","tag":"blue","_geo": { "lat": 50.62984446145472, "lng": 3.085712705162039 },"":"", "opt1": [null]}
|
||||
{"id":"B","word_rank":2,"typo_rank":0,"proximity_rank":0,"attribute_rank":0,"exact_rank":4,"asc_desc_rank":1,"sort_by_rank":2,"geo_rank":191,"title":"hello","description":"hello is a song recorded by english singer songwriter adele","tag":"red","_geo": { "lat": 50.63047567664291, "lng": 3.088852230809636 },"":"", "opt1": []}
|
||||
{"id":"C","word_rank":0,"typo_rank":1,"proximity_rank":8,"attribute_rank":336,"exact_rank":4,"asc_desc_rank":2,"sort_by_rank":0,"geo_rank":283,"title":"hell on earth","description":"hell on earth is the third studio album by american hip hop duo mobb deep","tag":"blue","_geo": { "lat": 50.6321800003937, "lng": 3.088331882262139 },"":"", "opt1": null}
|
||||
{"id":"D","word_rank":0,"typo_rank":1,"proximity_rank":10,"attribute_rank":757,"exact_rank":4,"asc_desc_rank":3,"sort_by_rank":2,"geo_rank":1381,"title":"hell on wheels tv series","description":"the construction of the first transcontinental railroad across the united states in the world","tag":"red","_geo": { "lat": 50.63728851135729, "lng": 3.0703951595971626 },"":"", "opt1": 4}
|
||||
{"id":"E","word_rank":2,"typo_rank":0,"proximity_rank":0,"attribute_rank":0,"exact_rank":4,"asc_desc_rank":4,"sort_by_rank":1,"geo_rank":1979,"title":"hello kitty","description":"also known by her full name kitty white is a fictional character produced by the japanese company sanrio","tag":"green","_geo": { "lat": 50.64264610511925, "lng": 3.0665099941857634 },"":"", "opt1": "E"}
|
||||
{"id":"F","word_rank":2,"typo_rank":1,"proximity_rank":0,"attribute_rank":1017,"exact_rank":5,"asc_desc_rank":5,"sort_by_rank":0,"geo_rank":65022,"title":"laptop orchestra","description":"a laptop orchestra lork or lo is a chamber music ensemble consisting primarily of laptops like helo huddersfield experimental laptop orchestra","tag":"blue","_geo": { "lat": 51.05028653642387, "lng": 3.7301072771642096 },"":"", "opt1": ["F"]}
|
||||
{"id":"G","word_rank":1,"typo_rank":0,"proximity_rank":0,"attribute_rank":0,"exact_rank":3,"asc_desc_rank":5,"sort_by_rank":2,"geo_rank":34692,"title":"hello world film","description":"hello world is a 2019 japanese animated sci fi romantic drama film directed by tomohiko ito and produced by graphinica","tag":"red","_geo": { "lat": 50.78776041427129, "lng": 2.661201766290338 },"":"", "opt1": [7]}
|
||||
{"id":"H","word_rank":1,"typo_rank":0,"proximity_rank":1,"attribute_rank":0,"exact_rank":3,"asc_desc_rank":4,"sort_by_rank":1,"geo_rank":202182,"title":"world hello day","description":"holiday observed on november 21 to express that conflicts should be resolved through communication rather than the use of force","tag":"green","_geo": { "lat": 48.875617484531965, "lng": 2.346747821504194 },"":"", "opt1": ["H", 8]}
|
||||
{"id":"I","word_rank":0,"typo_rank":0,"proximity_rank":8,"attribute_rank":338,"exact_rank":3,"asc_desc_rank":3,"sort_by_rank":0,"geo_rank":740667,"title":"hello world song","description":"hello world is a song written by tom douglas tony lane and david lee and recorded by american country music group lady antebellum","tag":"blue","_geo": { "lat": 43.973998070351065, "lng": 3.4661837318345032 },"":""}
|
||||
{"id":"J","word_rank":1,"typo_rank":0,"proximity_rank":1,"attribute_rank":1,"exact_rank":3,"asc_desc_rank":2,"sort_by_rank":1,"geo_rank":739020,"title":"hello cruel world","description":"hello cruel world is an album by new zealand band tall dwarfs","tag":"green","_geo": { "lat": 43.98920130353838, "lng": 3.480519311627928 },"":""}
|
||||
{"id":"K","word_rank":0,"typo_rank":2,"proximity_rank":9,"attribute_rank":670,"exact_rank":5,"asc_desc_rank":1,"sort_by_rank":2,"geo_rank":738830,"title":"hallo creation system","description":"in few word hallo was a construction toy created by the american company mattel to engage girls in construction play","tag":"red","_geo": { "lat": 43.99155030238669, "lng": 3.503453528249425 },"":""}
|
||||
{"id":"L","word_rank":0,"typo_rank":0,"proximity_rank":2,"attribute_rank":250,"exact_rank":4,"asc_desc_rank":0,"sort_by_rank":0,"geo_rank":737861,"title":"good morning world","description":"good morning world is an american sitcom broadcast on cbs tv during the 1967 1968 season","tag":"blue","_geo": { "lat": 44.000507750283695, "lng": 3.5116812040621572 },"":""}
|
||||
{"id":"M","word_rank":0,"typo_rank":0,"proximity_rank":0,"attribute_rank":0,"exact_rank":0,"asc_desc_rank":0,"sort_by_rank":2,"geo_rank":739203,"title":"hello world america","description":"a perfect match for a perfect engine using the query hello world america","tag":"red","_geo": { "lat": 43.99150729038736, "lng": 3.606143957295055 },"":""}
|
||||
{"id":"N","word_rank":0,"typo_rank":0,"proximity_rank":0,"attribute_rank":0,"exact_rank":1,"asc_desc_rank":4,"sort_by_rank":1,"geo_rank":9499586,"title":"hello world america unleashed","description":"a very good match for a very good engine using the query hello world america","tag":"green","_geo": { "lat": 35.511540843367115, "lng": 138.764368875787 },"":""}
|
||||
{"id":"O","word_rank":0,"typo_rank":0,"proximity_rank":0,"attribute_rank":10,"exact_rank":0,"asc_desc_rank":6,"sort_by_rank":0,"geo_rank":9425163,"title":"a perfect match for a perfect engine using the query hello world america","description":"hello world america","tag":"blue","_geo": { "lat": 35.00536702277189, "lng": 135.76118763940391 },"":""}
|
||||
{"id":"P","word_rank":0,"typo_rank":0,"proximity_rank":0,"attribute_rank":12,"exact_rank":1,"asc_desc_rank":3,"sort_by_rank":2,"geo_rank":9422437,"title":"a very good match for a very good engine using the query hello world america","description":"hello world america unleashed","tag":"red","_geo": { "lat": 35.06462306367058, "lng": 135.8338440354251 },"":""}
|
||||
{"id":"J","word_rank":1,"typo_rank":0,"proximity_rank":1,"attribute_rank":1,"exact_rank":3,"asc_desc_rank":2,"sort_by_rank":1,"geo_rank":739020,"title":"hello cruel world","description":"hello cruel world is an album by new zealand band tall dwarfs","tag":"green","_geo": { "lat": 43.98920130353838, "lng": 3.480519311627928 },"":"", "opt1": {}}
|
||||
{"id":"K","word_rank":0,"typo_rank":2,"proximity_rank":9,"attribute_rank":670,"exact_rank":5,"asc_desc_rank":1,"sort_by_rank":2,"geo_rank":738830,"title":"hallo creation system","description":"in few word hallo was a construction toy created by the american company mattel to engage girls in construction play","tag":"red","_geo": { "lat": 43.99155030238669, "lng": 3.503453528249425 },"":"", "opt1": [{"opt2": 11}] }
|
||||
{"id":"L","word_rank":0,"typo_rank":0,"proximity_rank":2,"attribute_rank":250,"exact_rank":4,"asc_desc_rank":0,"sort_by_rank":0,"geo_rank":737861,"title":"good morning world","description":"good morning world is an american sitcom broadcast on cbs tv during the 1967 1968 season","tag":"blue","_geo": { "lat": 44.000507750283695, "lng": 3.5116812040621572 },"":"", "opt1": {"opt2": [12]}}
|
||||
{"id":"M","word_rank":0,"typo_rank":0,"proximity_rank":0,"attribute_rank":0,"exact_rank":0,"asc_desc_rank":0,"sort_by_rank":2,"geo_rank":739203,"title":"hello world america","description":"a perfect match for a perfect engine using the query hello world america","tag":"red","_geo": { "lat": 43.99150729038736, "lng": 3.606143957295055 },"":"", "opt1": [13, [{"opt2": null}]]}
|
||||
{"id":"N","word_rank":0,"typo_rank":0,"proximity_rank":0,"attribute_rank":0,"exact_rank":1,"asc_desc_rank":4,"sort_by_rank":1,"geo_rank":9499586,"title":"hello world america unleashed","description":"a very good match for a very good engine using the query hello world america","tag":"green","_geo": { "lat": 35.511540843367115, "lng": 138.764368875787 },"":"", "opt1": {"a": 1, "opt2": {"opt3": 14}} }
|
||||
{"id":"O","word_rank":0,"typo_rank":0,"proximity_rank":0,"attribute_rank":10,"exact_rank":0,"asc_desc_rank":6,"sort_by_rank":0,"geo_rank":9425163,"title":"a perfect match for a perfect engine using the query hello world america","description":"hello world america","tag":"blue","_geo": { "lat": 35.00536702277189, "lng": 135.76118763940391 },"":"", "opt1": [[[[]]]] }
|
||||
{"id":"P","word_rank":0,"typo_rank":0,"proximity_rank":0,"attribute_rank":12,"exact_rank":1,"asc_desc_rank":3,"sort_by_rank":2,"geo_rank":9422437,"title":"a very good match for a very good engine using the query hello world america","description":"hello world america unleashed","tag":"red","_geo": { "lat": 35.06462306367058, "lng": 135.8338440354251 },"":"", "opt1.opt2": 16}
|
||||
{"id":"Q","word_rank":1,"typo_rank":0,"proximity_rank":0,"attribute_rank":0,"exact_rank":3,"asc_desc_rank":2,"sort_by_rank":1,"geo_rank":9339230,"title":"hello world","description":"a hello world program generally is a computer program that outputs or displays the message hello world","tag":"green","_geo": { "lat": 34.39548365683149, "lng": 132.4535960928883 },"":""}
|
||||
|
||||
@ -80,3 +80,9 @@ test_filter!(
|
||||
lower_complex_filter_2,
|
||||
vec![Left(vec!["tag=red", "tag=green"]), Left(vec!["asc_desc_rank<3", "asc_desc_rank<1"])]
|
||||
);
|
||||
test_filter!(exists_filter_1, vec![Right("opt1 EXISTS")]);
|
||||
test_filter!(exists_filter_1_not, vec![Right("opt1 NOT EXISTS")]);
|
||||
test_filter!(exists_filter_1_not_alt, vec![Right("NOT opt1 EXISTS")]);
|
||||
test_filter!(exists_filter_1_double_not, vec![Right("NOT opt1 NOT EXISTS")]);
|
||||
|
||||
test_filter!(exists_filter_2, vec![Right("opt1.opt2 EXISTS")]);
|
||||
|
||||
@ -9,8 +9,7 @@ use maplit::{hashmap, hashset};
|
||||
use milli::documents::{DocumentsBatchBuilder, DocumentsBatchReader};
|
||||
use milli::update::{IndexDocuments, IndexDocumentsConfig, IndexerConfig, Settings};
|
||||
use milli::{AscDesc, Criterion, DocumentId, Index, Member, Object};
|
||||
use serde::Deserialize;
|
||||
use serde_json::Deserializer;
|
||||
use serde::{Deserialize, Deserializer};
|
||||
use slice_group_by::GroupBy;
|
||||
|
||||
mod distinct;
|
||||
@ -44,6 +43,8 @@ pub fn setup_search_index_with_criteria(criteria: &[Criterion]) -> Index {
|
||||
S("tag"),
|
||||
S("asc_desc_rank"),
|
||||
S("_geo"),
|
||||
S("opt1"),
|
||||
S("opt1.opt2")
|
||||
});
|
||||
builder.set_sortable_fields(hashset! {
|
||||
S("tag"),
|
||||
@ -65,7 +66,7 @@ pub fn setup_search_index_with_criteria(criteria: &[Criterion]) -> Index {
|
||||
let mut documents_builder = DocumentsBatchBuilder::new(Vec::new());
|
||||
let reader = Cursor::new(CONTENT.as_bytes());
|
||||
|
||||
for result in Deserializer::from_reader(reader).into_iter::<Object>() {
|
||||
for result in serde_json::Deserializer::from_reader(reader).into_iter::<Object>() {
|
||||
let object = result.unwrap();
|
||||
documents_builder.append_json_object(&object).unwrap();
|
||||
}
|
||||
@ -194,12 +195,44 @@ fn execute_filter(filter: &str, document: &TestDocument) -> Option<String> {
|
||||
id = (document.geo_rank < 100000).then(|| document.id.clone());
|
||||
} else if filter.starts_with("NOT _geoRadius") {
|
||||
id = (document.geo_rank > 1000000).then(|| document.id.clone());
|
||||
} else if matches!(filter, "opt1 EXISTS" | "NOT opt1 NOT EXISTS") {
|
||||
id = document.opt1.is_some().then(|| document.id.clone());
|
||||
} else if matches!(filter, "NOT opt1 EXISTS" | "opt1 NOT EXISTS") {
|
||||
id = document.opt1.is_none().then(|| document.id.clone());
|
||||
} else if matches!(filter, "opt1.opt2 EXISTS") {
|
||||
if document.opt1opt2.is_some() {
|
||||
id = Some(document.id.clone());
|
||||
} else if let Some(opt1) = &document.opt1 {
|
||||
id = contains_key_rec(opt1, "opt2").then(|| document.id.clone());
|
||||
}
|
||||
}
|
||||
id
|
||||
}
|
||||
|
||||
pub fn contains_key_rec(v: &serde_json::Value, key: &str) -> bool {
|
||||
match v {
|
||||
serde_json::Value::Array(v) => {
|
||||
for v in v.iter() {
|
||||
if contains_key_rec(v, key) {
|
||||
return true;
|
||||
}
|
||||
}
|
||||
false
|
||||
}
|
||||
serde_json::Value::Object(v) => {
|
||||
for (k, v) in v.iter() {
|
||||
if k == key || contains_key_rec(v, key) {
|
||||
return true;
|
||||
}
|
||||
}
|
||||
false
|
||||
}
|
||||
_ => false,
|
||||
}
|
||||
}
|
||||
|
||||
pub fn expected_filtered_ids(filters: Vec<Either<Vec<&str>, &str>>) -> HashSet<String> {
|
||||
let dataset: HashSet<TestDocument> =
|
||||
let dataset: Vec<TestDocument> =
|
||||
serde_json::Deserializer::from_str(CONTENT).into_iter().map(|r| r.unwrap()).collect();
|
||||
|
||||
let mut filtered_ids: HashSet<_> = dataset.iter().map(|d| d.id.clone()).collect();
|
||||
@ -227,7 +260,7 @@ pub fn expected_filtered_ids(filters: Vec<Either<Vec<&str>, &str>>) -> HashSet<S
|
||||
filtered_ids
|
||||
}
|
||||
|
||||
#[derive(Debug, Clone, Deserialize, PartialEq, Eq, Hash)]
|
||||
#[derive(Debug, Clone, Deserialize, PartialEq, Eq)]
|
||||
pub struct TestDocument {
|
||||
pub id: String,
|
||||
pub word_rank: u32,
|
||||
@ -241,4 +274,16 @@ pub struct TestDocument {
|
||||
pub title: String,
|
||||
pub description: String,
|
||||
pub tag: String,
|
||||
#[serde(default, deserialize_with = "some_option")]
|
||||
pub opt1: Option<serde_json::Value>,
|
||||
#[serde(default, deserialize_with = "some_option", rename = "opt1.opt2")]
|
||||
pub opt1opt2: Option<serde_json::Value>,
|
||||
}
|
||||
|
||||
fn some_option<'de, D>(deserializer: D) -> Result<Option<serde_json::Value>, D::Error>
|
||||
where
|
||||
D: Deserializer<'de>,
|
||||
{
|
||||
let result = serde_json::Value::deserialize(deserializer)?;
|
||||
Ok(Some(result))
|
||||
}
|
||||
|
||||
Reference in New Issue
Block a user