//! # fuzzy_rocks Overview
//! 
//! A persistent datastore backed by [RocksDB](https://rocksdb.org) with fuzzy key lookup using an arbitrary
//! distance function accelerated by the [SymSpell](https://github.com/wolfgarbe/SymSpell) algorithm.
//! 
//! The reasons to use this crate over another SymSpell implementation are:
//! - You have non-standard key characters (e.g. DNA snippets, etc.)
//! - You want to use a custom distance function
//! - Startup time matters (You can't recompute key variants at load time)
//! - You have millions of keys and care about memory footprint
//! 
//! ## Records & Keys
//! 
//! A [Table] contains records, each of which has a unique [RecordID], and each record is associated
//! with one or more [Key]s.  Keys are used to perform fuzzy lookups of records.  A [Key] is typically
//! a [String] or an [&str], but may be any number of collections of [KeyCharT](TableConfig#keychart), such
//! as a [Vec], [Array](array), or [Slice](slice).
//! 
//! Keys are not required to be unique in the Table and multiple records may have keys in common.  All
//! of the lookup methods may return multiple records if the lookup key and other criteria matches
//! more than one record in the Table.
//! 
//! ## Usage Examples
//! 
//! A simple use case with a default [Table] configuration using `&str`s as keys.
//! ```rust
//! use fuzzy_rocks::{*};
//! 
//! //Create and reset the FuzzyRocks Table
//! let mut table = Table::<DefaultTableConfig, true>::new("test.rocks", DefaultTableConfig()).unwrap();
//! table.reset().unwrap();
//!
//! //Insert some records
//! let thu = table.insert("Thursday", &"Mokuyoubi".to_string()).unwrap();
//! let wed = table.insert("Wednesday", &"Suiyoubi".to_string()).unwrap();
//! let tue = table.insert("Tuesday", &"Kayoubi".to_string()).unwrap();
//! let mon = table.insert("Monday", &"Getsuyoubi".to_string()).unwrap();
//! 
//! //Use lookup_best, to get the closest fuzzy match
//! let result = table.lookup_best("Bonday")
//!     .unwrap().next().unwrap();
//! assert_eq!(result, mon);
//! 
//! //Use lookup_fuzzy, to get all matches and their distances
//! let results : Vec<(RecordID, u8)> = table
//!     .lookup_fuzzy("Tuesday", Some(2))
//!     .unwrap().collect();
//! assert_eq!(results.len(), 2);
//! assert!(results.contains(&(tue, 0))); //Tuesday -> Tuesday with 0 edits
//! assert!(results.contains(&(thu, 2))); //Thursday -> Tuesday with 2 edits
//! 
//! //Retrieve a key and the value from a record
//! assert_eq!(table.get_one_key(wed).unwrap(), "Wednesday");
//! assert_eq!(table.get_value(wed).unwrap(), "Suiyoubi");
//! ```
//! 
//! Another use case with a [Table] that stores (simplified) DNA sequences.
//! For a more comprehensive representation of the format for biological molecules, look at the [FASTA format](https://en.wikipedia.org/wiki/FASTA_format).
//! ```rust
//! use fuzzy_rocks::{*};
//! 
//! //A simplified data type that might represent a Nucleobase.
//! // https://en.wikipedia.org/wiki/Nucleobase
//! #[derive(Copy, Clone, Eq, PartialEq, Hash, serde::Serialize, serde::Deserialize)]
//! enum Nucleobase {
//!     A, // adenine
//!     C, // cytosine
//!     G, // guanine
//!     T, // thymine
//!     U, // uracil
//! }
//! 
//! struct Config();
//! impl TableConfig for Config {
//!     type KeyCharT = Nucleobase;
//!     type DistanceT = u8;
//!     type ValueT = usize;
//!     const UTF8_KEYS : bool = false;
//!     const MAX_DELETES : usize = 2;
//!     const MEANINGFUL_KEY_LEN : usize = 24;
//!     const GROUP_VARIANT_OVERLAP_THRESHOLD : usize = 5;
//!     const DISTANCE_FUNCTION : DistanceFunction<Self::KeyCharT, Self::DistanceT> = Self::levenstein_distance;
//! }
//! 
//! //Create and reset the FuzzyRocks Table
//! let mut table = Table::<Config, false>::new("test.rocks", Config()).unwrap();
//! ``` 
//! 
//! Additional usage examples can be found in the tests, located at the bottom of the [src/lib.rs](https://github.com/luketpeterson/fuzzy_rocks/blob/main/src/lib.rs) file.
//! 
//! ## Table Configuration
//! 
//! A [TableConfig] object is passed as an argument to [Table::new].  The TableConfig specifies a number
//! of things about the table, including:
//! 
//! - Data Types that define the structure and representation of certain aspects of the [Table]
//! - Tuning Parameters to affect the performance of various operations
//! - A Distance Function to calculate the distance between two keys in a [Metric Space](https://en.wikipedia.org/wiki/Metric_space)
//! 
//! [DefaultTableConfig] is a zero-sized type that implements a default [TableConfig] for UTF-8 keys.  This will be sufficient for many situations.
//! If you need to customize the TableConfig, more details about the type parameters and fields can be found in the documentation
//! for [TableConfig].
//! 
//! ### Unicode and UTF-8 Support
//! 
//! A [Table] may be configured to encode keys as [UTF-8](https://en.wikipedia.org/wiki/UTF-8) or not, depending on your requirements.
//! This is configured through the [TableConfig] object's [UTF8_KEYS](TableConfig::UTF8_KEYS) constant.
//! 
//! ## Algorithm Details
//! 
//! The authoritative description of SymSpell is the ReadMe for the [SymSpell project](https://github.com/wolfgarbe/SymSpell).
//! 
//! The fuzzy_rocks implementation has a few additional details to be aware of:
//! 
//! - fuzzy_rocks won't find keys that don't have at least one character in common, regardless of the value
//! of [MAX_DELETES](TableConfig::MAX_DELETES).  For example the key `me` won't be found by the query string `hi`, even with a distance
//! of 2 (or any other value).  This decision was made because the variant space becomes very crowded
//! for short keys, and the extreme example of the empty-string variant was severely damaging performance with
//! short keys.
//! 
//! ## Performance Characteristics
//! 
//! This crate is designed for large databases where startup time and resident memory footprint are significant
//! considerations.  This create has been tested with 200,000 records cumulatively having over 1 million keys,
//! and about 140 million key variants.  In this situation, a fuzzy lookup was between 500us (microseconds)
//! and 1ms, running on my laptop - which I consider to be very expensive in absolute terms, but acceptable
//! for many use cases.
//! 
//! The performance will also vary greatly depending on the key distribution and the table parameters.  Keys
//! that are distinct from eachother will lead to faster searches vs. keys that share many variants in common.
//! 
//! ### Tuning for Performance
//! 
//! Performance is highly dependent on the values for the [TableConfig] used when creating the [Table], but
//! the values must match the characteristics of the keys in the data set.
//! 
//! Briefly, the tuning parameters are:
//! 
//! [MAX_DELETES](TableConfig::MAX_DELETES): A smaller `MAX_DELETES` value will perform exponentially better but be able to find
//! fewer results for a search.  `MAX_DELETES` should be tuned to be as small as you can make it, but no smaller. ;-)
//! 
//! [MEANINGFUL_KEY_LEN](TableConfig::MEANINGFUL_KEY_LEN): A higher value for `MEANINGFUL_KEY_LEN` will result in fewer wasted evaluations of the distance function
//! but will lead to more entries in the variants database and thus reduced database performance.
//! 
//! [GROUP_VARIANT_OVERLAP_THRESHOLD](TableConfig::GROUP_VARIANT_OVERLAP_THRESHOLD) controls the logic about when
//! a key is merged with an existing `key_group` vs. when a new `key_group` is created.
//! 
//! More detailed information on these tuning parameters can be found in the docs for [TableConfig].
//! 
//! If your use-case can cope with a higher startup latency and you are ok with all of your keys and
//! variants being loaded into memory, then query performance will certainly be better using a solution
//! built on Rust's native collections, such as this [symspell](https://crates.io/crates/symspell)
//! crate on [crates.io](http://crates.io).
//! 
//! ### Performance Counters
//! 
//! You can use the [PerfCounterFields] to measure the number of internal operations being performed, and use
//! that information to adjust the [TableConfig] parameters.
//! 
//! First, you must enable the `perf_counters`
//! feature in the `Cargo.toml` file, with an entry similar to this:
//! 
//! ```toml
//! [dependencies]
//! fuzzy_rocks = { version = "0.2.0", features = ["perf_counters"] }
//! ```
//! 
//! Then, the performance counters may be reset by calling [Table::reset_perf_counters] and read by calling [Table::get_perf_counters].
//! 
//! ### Benchmarks
//! 
//! Fuzzy_rocks contains a (small but growing) suite of benchmarks, implemented with [criterion](https://docs.rs/criterion).
//! These can be invoked with:
//! 
//! ```sh
//! cargo bench -- --nocapture
//! ```
//! 
//! ## Database Format
//! 
//! DB contents are encoded using the [bincode] crate.  Currently the database contains 4 Column Families.
//! 
//! 1. The "rec_data" CF uses a little-endian-encoded [RecordID] as its key, and stores a varint-encoded `Vec` of
//!     integers, which represent key_group indices, each of which can be combined with a `RecordID` to create a
//!     `KeyGroupID`.  Each referenced key_group contains at least one key associated with the record.
//!     The `rec_data` CF is the place to start when constructing the complete set of keys associated with a record.
//! 
//! 2. The "keys" CF uses a little-endian-encoded `KeyGroupID` as its key, and stores a varint-encoded `Vec` of
//!     OwnedKeys (think Strings), each representing a key in a key_group.  In the present implementation,
//!     a given key_group only stores keys for a single record, and the `KeyGroupID` embeds the associated
//!     [RecordID] in its lower 44 bits.  This scheme is likely to change in the future.
//! 
//! 3. The "variants" CF uses a serialized key variant as its key, and stores a fixint-encoded `Vec` of
//!     `KeyGroupID`s representing every key_group that holds a key that can be reduced to this variant.
//!     Complete key strings themselves are represented as variants in this CF.
//! 
//! 4. The "values" CF uses a little-endian-encoded [RecordID] as its key, and stores the [bincode] serialized
//!     [ValueT](TableConfig::ValueT) associated with the record.
//! 
//! ## Future Work
//! 
//! 1. Optimization for crowded neighborhoods in the key metric-space.  The current design optimizes for the case
//!     where a single record has multiple keys with overlapping variants.  This is an efficient design in the
//!     case where many proximate keys belong to the same record, as is the case when multiple spelling variations
//!     for the same word are submitted to the Table.
//! 
//!     First, here is some background on how we got to this design.  In v0.2.0, multi-key
//!     support was added because many use cases involved creating multiple records that were conceptually the same
//!     record in order to represent key variations such as multiple valid spellings of the same thing. Becauase
//!     these keys are often similar to each other and shared many variants in the database, the key_groups feature
//!     was added to improve performance by reducing the number of keys in each variant entry.  Ideally
//!     a given variant entry would only contain one reference to a record even if that record had many similar keys.
//! 
//!     Unfortunately this meant all the record's keys would be loaded together and that lead to unnecessary
//!     invocations of the distance function to evaluate all of the keys for each record.  The solution in v0.2.0
//!     was to segment a record's keys into key_groups, so similar keys would be together in the same key group
//!     but other keys needn't be tested.  This solution works well for individual records with many keys that
//!     cluster together.
//! 
//!     However, a large performance problem still exists when many keys from *Different* records cluster together.
//!     To address this, I think that a different database layout is needed, and here is my thinking:
//! 
//!     - Create a separate CF to store full keys is needed, and each key entry would contain the [RecordID]s
//!         assiciated with that key. There are two possible implementations:
//! 
//!         - A more compact implementation for longer keys would be to have a "KeyID" to reference each key entry.
//!             Then the entry would contain the key itself, and the [RecordID]s associated with the key in the same
//!             entry or in another CF that is also indexed by `KeyID`.  In this implementation, the variant CF is
//!             still used to find the KeyID, and perhaps we could establish a convention where a variant keeps the
//!             KeyID for an exact match at the beginning of the list.
//!         - An implementation that requires less lookup indirection would be to use the exact keys themselves,
//!             (or perhaps meaningful keys. See [TableConfig::MEANINGFUL_KEY_LEN]) as the Rocks keys to the CF.
//!             Then we would separate the exact keys from the "variants" CF, and keys themselves wouldn't be placed
//!             into the "variants" CF.  Each variant entry would include all of the full keys that it is a variant of.
//! 
//!         The decision about which of these implementations is better depends on the average key length.  A 64-bit
//!         KeyID is the equivalent of an 8-byte key, but an average key is around 16 bytes.  On the other hand,
//!         eliminating the indirection associated with a second DB query might tip the scales in favor of embedding
//!         the keys directly.
//!     
//!     - The changes above address many records with the same keys, but we may still want to retain the optimization
//!         where many similar keys are implemented under only one `KeyGroupID` in a variant entry, instead of one
//!         reference for each individual key.  Especially if those keys are fetched together often.
//!     
//!         So, I believe the best path forward looks more like the first option above.  So we would:
//!         
//!         - Rework the `KeyGroupID` so it no longer implies a [RecordID] within its lower 44 bits, and is instead
//!             a completely different number, assigned as needed.
//! 
//!         - Rework the key_group entries so that each key is associated with a list of [RecordID]s.
//! 
//!         - Rework the "rec_data" CF, so that, instead of a list of key_group indices (which are then converted
//!             into `KeyGroupID`s), the "rec_data" entry would store the complete list of keys for the record.
//!
//!         I believe this approach will improve lookup performance when there are many similar keys belonging to
//!         different records.  I can identify several downsides however:
//! 
//!         - The lookup_exact function no longer has the existing fast path because it must pull the key_group
//!             entry no matter what, in order to get the [RecordID]s.  This can be partially ameliorated by moving
//!             the exact match key_group to the beginning of a variant's list.  In addition, the current fast-path
//!             has a BUG! (see item 8. in this list.)
//! 
//!         - The key_group entry will be more expensive to parse on account of it containing a vector of [RecordID]s
//!             associated with each key.  However, this will be offset by needing to load many fewer key_group
//!             entries, so I think this may be a net win - or at least a wash.
//! 
//!         - The "rec_data" CF will bloat as each key group index (about 1 byte) is replaced by a whole key (avg 16
//!             bytes).  However, the rec_data is only loaded for bookkeeping operations, and doesn't figure into the
//!             fuzzy lookup speed.  Therefore the main impact will be DB size, and we can expect a 5-10% increase
//!             from this factor.
//! 
//! 2. BK-Tree search within a key-group.  If we end up with many proximate keys, there may be an advantage to having
//!     a secondary search structure within the table.  In fact, I think a BL-Tree always outperforms a list, so
//!     the real question is whether the performance boost justifies the work and code complexity.
//! 
//!     SymSpell is very well optimized for sparse key spaces, but in dense key spaces (where many keys share many
//!     variants), the SymSpell optimization still results in the need to test hundreds of keys with the distance
//!     function.  In this case, a BK-Tree may speed up the fuzzy lookups substantially.
//! 
//! 3. Multi-threading within a lookup. Rework internal plumbing so that variant entries can be fetched and processed
//!     in parallel, leading to parallel evaluation of key groups and parallel execution of distance functions.
//! 
//! 4. Investigate adding a "k-nn lookup" method that would return up to k results, ordered by their distance from the
//!     lookup key.  Perhaps in addition to, or instead of, the [Table::lookup_best] method, because this new method is
//!     essentially a generalization of [Table::lookup_best]
//! 
//!     This idea is a little bit problematic because a function that returns exactly k results cannot be deterministic
//!     in cases where there are equal-distance results.  We will often find that k straddles a border and the only
//!     way to return a deterministic result set is to return more than k results. (or fewer than k, but that may
//!     mean a set with no results, which is probably not what the caller wants.)
//! 
//! 5. Save the TableConfig to the database, in order to detect an error when the config changes in a way that makes
//!     the database invalid.  Also include a software version check, and create a function to represent which
//!     software versions contain database format compatibility breaks.  Open Question: Should we store a checksum
//!     of the distance function?  The function may be re-compiled or changed internally without changing behavior,
//!     but we can't know that.
//! 
//! 6. Remove the `KeyUnsafe` trait as soon as "GenericAssociatedTypes" is stabilized in Rust.  
//!     <https://github.com/rust-lang/rust/issues/44265>.  As soon as I can implement an associated type with an
//!     internal lifetime that isn't a parameter to the Key trait itself, then I'll be able to return objects that
//!     have the same base type but a shorter associated lifetime, and thus eliminate the need to transmute lifetimes.
//! 
//! 7. Investigate tools to detect when a [Table] parameter is tuned badly for a data set, based on known optimal
//!     ratios for certain performance counters.  Alternatively, build tools to assist in performing a parameter
//!     search optimization process, that could automatically sweep the parameters, searching for the optimal
//!     [TableConfig] for a given data set.
//! 
//! 8. Fix BUG! in [Table::lookup_exact] caused by the optimization where we don't load the key_group associated with
//!     the exact variant, so we may return records whose keys are supersets of the lookup key.  NOTE: This will
//!     likely be fixed as a side-effect of the changes proposed in 1.
//! 
//! ## Release History
//! 
//! ### 0.2.0
//! - Multi-key support. Records may now be associated with more than one key
//! - Lookup_best now returns an iterator instead of one arbitrarily-chosen record
//! - Support for Keys composed of a generic KeyCharT type, vs. only ASCII or UTF-8
//! - Central TableConfig trait to centralize all tuning parameters
//! - Added micro-benchmarks using Criterion
//! - Added `perf_counters` feature
//! - Massive performance optimizations for lookups (10x-100x) in some cases
//!     - lookup_best checks lookup_exact first before more expensive lookup_fuzzy
//!     - key_groups mingle similar keys for a record
//!     - micro-optimizations to levenstein_distance function for 3x speedup
//!     - record value stored separately from keys in DB, so Value isn't parsed unnecessarily
//! 
//! ### 0.1.1
//! - Initial Release
//! 
//! ## Misc
//! 
//! **NOTE**: The included `geonames_megacities.txt` file is a stub for the `geonames_test`, designed to stress-test
//! this crate.  The abriged file is included so the test will pass regardless, and to avoid bloating the
//! download.  The content of `geonames_megacities.txt` was derived from data on [geonames.org](http://geonames.org),
//! and licensed under a [Creative Commons Attribution 4.0 License](https://creativecommons.org/licenses/by/4.0/legalcode)
//! 

mod unicode_string_helpers;
mod bincode_helpers;
mod database;
mod key;
pub use key::Key;
mod records;
pub use records::RecordID;
mod table_config;
pub use table_config::{TableConfig, DistanceFunction, DefaultTableConfig, MAX_KEY_LENGTH};
mod key_groups;
mod sym_spell;
mod perf_counters;
mod table;
pub use table::{Table};
pub use perf_counters::{PerfCounterFields};


#[cfg(test)]
mod tests {
    use std::collections::{HashSet};
    use std::fs;
    use std::path::PathBuf;
    use csv::ReaderBuilder;
    use serde::{Serialize, Deserialize};

    use crate::{*};
    use crate::unicode_string_helpers::{*};

    #[test]
    /// This test is designed to stress the database with many thousand entries.
    ///  
    /// You may download an alternate GeoNames file in order to get a more rigorous test.  The included
    /// `geonames_megacities.txt` file is just a stub to avoid bloating the crate download.  The content
    /// of `geonames_megacities.txt` was derived from data on [http://geonames.org], and licensed under
    /// a [Creative Commons Attribution 4.0 License](https://creativecommons.org/licenses/by/4.0/legalcode)
    /// 
    /// A geonames file may be downloaded from: [http://download.geonames.org/export/dump/cities15000.zip]
    /// for the smallest file, and "cities500.zip" for the largest, depending on whether you want this
    /// to pass in the a lightweight way or the most thorough.
    fn geonames_test() {

        let mut geonames_file_path = PathBuf::from(env!("CARGO_MANIFEST_DIR"));
        geonames_file_path.push("geonames_megacities.txt");

        // Alternate geonames file
        // NOTE: Uncomment this to use a different file
        // let geonames_file_path = PathBuf::from("/path/to/file/cities500.txt");
    
        //Create the FuzzyRocks Table with an appropriate config
        struct Config();
        impl TableConfig for Config {
            type KeyCharT = char;
            type DistanceT = u8;
            type ValueT = i32;
        }
        let mut table = Table::<Config, true>::new("geonames.rocks", Config()).unwrap();

        //Clear out any records that happen to be hanging out
        table.reset().unwrap();

        //Data structure to parse the GeoNames TSV file into
        #[derive(Clone, Debug, Serialize, Deserialize)]
        struct GeoName {
            geonameid         : i32, //integer id of record in geonames database
            name              : String, //name of geographical point (utf8) varchar(200)
            asciiname         : String, //name of geographical point in plain ascii characters, varchar(200)
            alternatenames    : String, //alternatenames, comma separated, ascii names automatically transliterated, convenience attribute from alternatename table, varchar(10000)
            latitude          : f32, //latitude in decimal degrees (wgs84)
            longitude         : f32, //longitude in decimal degrees (wgs84)
            feature_class     : char, //see http://www.geonames.org/export/codes.html, char(1)
            feature_code      : String,//[char; 10], //see http://www.geonames.org/export/codes.html, varchar(10)
            country_code      : String,//[char; 2], //ISO-3166 2-letter country code, 2 characters
            cc2               : String, //alternate country codes, comma separated, ISO-3166 2-letter country code, 200 characters
            admin1_code       : String,//[char; 20], //fipscode (subject to change to iso code), see exceptions below, see file admin1Codes.txt for display names of this code; varchar(20)
            admin2_code       : String, //code for the second administrative division, a county in the US, see file admin2Codes.txt; varchar(80) 
            admin3_code       : String,//[char; 20], //code for third level administrative division, varchar(20)
            admin4_code       : String,//[char; 20], //code for fourth level administrative division, varchar(20)
            population        : i64, //bigint (8 byte int)
            #[serde(deserialize_with = "default_if_empty")]
            elevation         : i32, //in meters, integer
            #[serde(deserialize_with = "default_if_empty")]
            dem               : i32, //digital elevation model, srtm3 or gtopo30, average elevation of 3''x3'' (ca 90mx90m) or 30''x30'' (ca 900mx900m) area in meters, integer. srtm processed by cgiar/ciat.
            timezone          : String, //the iana timezone id (see file timeZone.txt) varchar(40)
            modification_date : String, //date of last modification in yyyy-MM-dd format
        }

        fn default_if_empty<'de, D, T>(de: D) -> Result<T, D::Error>
            where D: serde::Deserializer<'de>, T: serde::Deserialize<'de> + Default,
        {
            Option::<T>::deserialize(de).map(|x| x.unwrap_or_else(|| T::default()))
        }

        //Open the tab-saparated value file
        let tsv_file_contents = fs::read_to_string(geonames_file_path).expect("Error reading geonames file");
        let mut tsv_parser = ReaderBuilder::new()
            .delimiter(b'\t')
            .has_headers(false)
            .flexible(true) //We want to permit situations where some rows have fewer columns for now
            .quote(0)
            .double_quote(false)
            .from_reader(tsv_file_contents.as_bytes());

        //Iterate over every geoname entry in the geonames file and insert it (lowercase) into our table
        let mut record_id = RecordID::NULL;
        let mut tsv_record_count = 0;
        for geoname in tsv_parser.deserialize::<GeoName>().map(|result| result.unwrap()) {

            //Separate the comma-separated alternatenames field
            let mut names : HashSet<String> = HashSet::from_iter(geoname.alternatenames.split(',').map(|string| string.to_lowercase()));
            
            //Add the primary name for the place
            names.insert(geoname.name.to_lowercase());

            //Create a record in the table
            let names_vec : Vec<String> = names.into_iter()
                .map(|string| unicode_truncate(string.as_str(), MAX_KEY_LENGTH))
                .collect();
            record_id = table.create(&names_vec[..], &geoname.geonameid).unwrap();
            tsv_record_count += 1;

            //Status Print
            if record_id.0 % 500 == 499 {
                println!("inserting... {}, {}", geoname.name.to_lowercase(), record_id.0);
            }
        }

        //Indirectly that the number of records roughly matches the number of entries from the CSV
        //NOTE: RocksDB doesn't have a "record_count" feature, and therefore neither does our Table,
        //but since we started from a reset table, we can ensure that the last assigned record_id
        //should roughly correspond to the number of entries we inserted
        assert_eq!(record_id.0 + 1, tsv_record_count);

        //Confirm we can find a known city (London)
        let london_results : Vec<i32> = table.lookup_exact("london").unwrap().map(|record_id| table.get_value(record_id).unwrap()).collect();
        assert!(london_results.contains(&2643743)); //2643743 is the geonames_id of "London"

        //Confirm we can find a known city with a longer key name (not on the fast-path)
        let rio_results : Vec<i32> = table.lookup_exact("rio de janeiro").unwrap().map(|record_id| table.get_value(record_id).unwrap()).collect();
        assert!(rio_results.contains(&3451190)); //3451190 is the geonames_id of "Rio de Janeiro"

        //Close RocksDB connection by dropping the table object
        drop(table);
        drop(london_results);

        //Reopen the table and confirm that "London" is still there
        let table = Table::<Config, true>::new("geonames.rocks", Config()).unwrap();
        let london_results : Vec<i32> = table.lookup_exact("london").unwrap().map(|record_id| table.get_value(record_id).unwrap()).collect();
        assert!(london_results.contains(&2643743)); //2643743 is the geonames_id of "London"
    }

    #[test]
    fn fuzzy_rocks_test() {

        //Configure and Create the FuzzyRocks Table
        struct Config();
        impl TableConfig for Config {
            type KeyCharT = char;
            type DistanceT = u8;
            type ValueT = String;
            const MEANINGFUL_KEY_LEN : usize = 8;
        }
        let mut table = Table::<Config, true>::new("test.rocks", Config()).unwrap();

        //Clear out any records that happen to be hanging out from a previous run
        table.reset().unwrap();

        //Insert some records
        let sun = table.insert("Sunday", &"Nichiyoubi".to_string()).unwrap();
        let sat = table.insert("Saturday", &"Douyoubi".to_string()).unwrap();
        let fri = table.insert("Friday", &"Kinyoubi".to_string()).unwrap();
        let thu = table.insert("Thursday", &"Mokuyoubi".to_string()).unwrap();
        let wed = table.insert("Wednesday", &"Suiyoubi".to_string()).unwrap();
        let tue = table.insert("Tuesday", &"Kayoubi".to_string()).unwrap();
        let mon = table.insert("Monday", &"Getsuyoubi".to_string()).unwrap();

        //Test lookup_exact
        let results : Vec<(String, String)> = table.lookup_exact("Friday").unwrap().map(|record_id| table.get(record_id).unwrap()).collect();
        assert_eq!(results.len(), 1);
        assert_eq!(results[0].0, "Friday");
        assert_eq!(results[0].1, "Kinyoubi");

        //Test lookup_exact, with a query that should provide no results
        let results : Vec<RecordID> = table.lookup_exact("friday").unwrap().collect();
        assert_eq!(results.len(), 0);

        //Test lookup_best, using the supplied edit_distance function
        let results : Vec<RecordID> = table.lookup_best("Bonday").unwrap().collect();
        assert_eq!(results.len(), 1);
        assert!(results.contains(&mon));

        //Test lookup_best, when there is no acceptable match
        let results : Vec<RecordID> = table.lookup_best("Rahu").unwrap().collect();
        assert_eq!(results.len(), 0);

        //Test lookup_fuzzy with a perfect match, using the supplied edit_distance function
        //In this case, we should only get one match within edit-distance 2
        let results : Vec<(String, String, u8)> = table.lookup_fuzzy("Saturday", Some(2))
            .unwrap().map(|(record_id, distance)| {
                let (key, val) = table.get(record_id).unwrap();
                (key, val, distance)
            }).collect();
        assert_eq!(results.len(), 1);
        assert_eq!(results[0].0, "Saturday");
        assert_eq!(results[0].1, "Douyoubi");
        assert_eq!(results[0].2, 0);

        //Test lookup_fuzzy with a perfect match, but where we'll hit another imperfect match as well
        let results : Vec<(String, String, u8)> = table.lookup_fuzzy("Tuesday", Some(2))
            .unwrap().map(|(record_id, distance)| {
                let (key, val) = table.get(record_id).unwrap();
                (key, val, distance)
            }).collect();
        assert_eq!(results.len(), 2);
        assert!(results.contains(&("Tuesday".to_string(), "Kayoubi".to_string(), 0)));
        assert!(results.contains(&("Thursday".to_string(), "Mokuyoubi".to_string(), 2)));

        //Test lookup_fuzzy where we should get no match
        let results : Vec<(RecordID, u8)> = table.lookup_fuzzy("Rahu", Some(2)).unwrap().collect();
        assert_eq!(results.len(), 0);

        //Test lookup_fuzzy_raw, to get all of the SymSpell Delete variants
        //We're testing the fact that characters beyond `config.meaningful_key_len` aren't used for the comparison
        let results : Vec<RecordID> = table.lookup_fuzzy_raw("Sunday. That's my fun day.").unwrap().collect();
        assert_eq!(results.len(), 1);
        assert_eq!(results[0], sun);

        //Test deleting a record, and ensure we can't access it or any trace of its variants
        table.delete(tue).unwrap();
        assert!(table.get_one_key(tue).is_err());

        //Since "Tuesday" had one variant overlap with "Thursday", i.e. "Tusday", make sure we now find
        // "Thursday" when we attempt to lookup "Tuesday"
        let results : Vec<RecordID> = table.lookup_best("Tuesday").unwrap().collect();
        assert_eq!(results.len(), 1);
        assert!(results.contains(&thu));

        //Delete "Saturday" and make sure we see no matches when we try to search for it
        table.delete(sat).unwrap();
        assert!(table.get_one_key(sat).is_err());
        let results : Vec<RecordID> = table.lookup_fuzzy_raw("Saturday").unwrap().collect();
        assert_eq!(results.len(), 0);

        //Test replacing a record with another one and ensure the right data is retained
        table.replace_keys(wed, &["Miercoles"]).unwrap();
        table.replace_value(wed, &"Zhousan".to_string()).unwrap();
        let results : Vec<(String, String)> = table.lookup_exact("Miercoles").unwrap().map(|record_id| (table.get_one_key(record_id).unwrap(), table.get_value(record_id).unwrap())).collect();
        assert_eq!(results.len(), 1);
        assert_eq!(results[0].0, "Miercoles");
        assert_eq!(results[0].1, "Zhousan");
        let results : Vec<RecordID> = table.lookup_fuzzy_raw("Mercoledi").unwrap().collect();
        assert_eq!(results.len(), 1);
        assert_eq!(results[0], wed);

        //Try replacing the keys and value on a record that we deleted earlier, and make sure
        // we get the right errors
        assert!(table.replace_keys(sat, &["Sabado"]).is_err());
        assert!(table.replace_value(sat, &"Zhouliu".to_string()).is_err());

        //Attempt to replace a record's keys with an empty list, check the error
        let empty_slice : &[&str] = &[];
        assert!(table.replace_keys(sat, empty_slice).is_err());
        
        //Attempt to replace an invalid record and confirm we get a reasonable error
        assert!(table.replace_keys(RecordID::NULL, &["Nullday"]).is_err());
        assert!(table.replace_value(RecordID::NULL, &"Null".to_string()).is_err());

        //Test that create() returns the right error if no keys are supplied
        let empty_slice : &[&str] = &[];
        assert!(table.create(empty_slice, &"Douyoubi".to_string()).is_err());

        //Recreate Saturday using the create() api
        //While we're here, Also test that the same key string occurring more than once
        // doesn't result in additional keys being added
        let sat = table.create(&["Saturday", "Saturday"], &"Douyoubi".to_string()).unwrap();
        table.add_keys(sat, &["Saturday", "Saturday"]).unwrap();
        assert_eq!(table.keys_count(sat).unwrap(), 1);

        //Add some new keys to it, and verify that it can be found using any of its three keys
        table.add_keys(sat, &["Sabado", "Zhouliu"]).unwrap();
        assert_eq!(table.keys_count(sat).unwrap(), 3);
        let results : Vec<RecordID> = table.lookup_fuzzy_raw("Saturday").unwrap().collect();
        assert_eq!(results.len(), 1);
        assert_eq!(results[0], sat);
        let results : Vec<RecordID> = table.lookup_exact("Zhouliu").unwrap().collect();
        assert_eq!(results.len(), 1);
        assert_eq!(results[0], sat);
        let results : Vec<RecordID> = table.lookup_fuzzy_raw("Sabato").unwrap().collect();
        assert_eq!(results.len(), 1);
        assert_eq!(results[0], sat);

        //Test deleting one of the keys from a record, and make sure we can't find it using that
        //key but the other keys are unaffected
        table.remove_keys(sat, &["Sabado"]).unwrap();
        assert_eq!(table.keys_count(sat).unwrap(), 2);
        let results : Vec<RecordID> = table.lookup_exact("Sabado").unwrap().collect();
        assert_eq!(results.len(), 0);
        let results : Vec<RecordID> = table.lookup_fuzzy_raw("Sabato").unwrap().collect();
        assert_eq!(results.len(), 0);
        let results : Vec<RecordID> = table.lookup_fuzzy_raw("Saturnsday").unwrap().collect();
        assert_eq!(results.len(), 1);
        assert_eq!(results[0], sat);
        let results : Vec<RecordID> = table.lookup_exact("Zhouliu").unwrap().collect();
        assert_eq!(results.len(), 1);
        assert_eq!(results[0], sat);

        //Attempt to remove the remaining keys and ensure we get an error and both keys remain
        assert!(table.remove_keys(sat, &["Saturday", "Zhouliu"]).is_err());
        assert_eq!(table.keys_count(sat).unwrap(), 2);
        let results : Vec<RecordID> = table.lookup_exact("Saturday").unwrap().collect();
        assert_eq!(results.len(), 1);
        let results : Vec<RecordID> = table.lookup_exact("Zhouliu").unwrap().collect();
        assert_eq!(results.len(), 1);

        //Test that replacing the keys of a record doesn't leave any orphaned variants
        table.replace_keys(sat, &["Sabado"]).unwrap();
        assert_eq!(table.keys_count(sat).unwrap(), 1);
        let results : Vec<RecordID> = table.lookup_fuzzy_raw("Saturday").unwrap().collect();
        assert_eq!(results.len(), 0);
        let results : Vec<RecordID> = table.lookup_fuzzy_raw("Zhouliu").unwrap().collect();
        assert_eq!(results.len(), 0);

        //Test that adding the same key again doesn't result in multiple copies of the key
        table.add_keys(sat, &["Sabado"]).unwrap();
        assert_eq!(table.keys_count(sat).unwrap(), 1);
        table.add_keys(sat, &["Saturday", "Saturday"]).unwrap();
        assert_eq!(table.keys_count(sat).unwrap(), 2);

        //Test that nothing breaks when we have two keys with overlapping variants, and then
        // delete one
        // "Venerdi" & "Vendredi" have overlapping variant: "Venedi"
        table.add_keys(fri, &["Geumyoil", "Viernes", "Venerdi", "Vendredi"]).unwrap();
        assert_eq!(table.keys_count(fri).unwrap(), 5);
        table.remove_keys(fri, &["Vendredi"]).unwrap();
        assert_eq!(table.keys_count(fri).unwrap(), 4);
        let results : Vec<RecordID> = table.lookup_fuzzy_raw("Vendredi").unwrap().collect();
        assert_eq!(results.len(), 1); //We'll still get Venerdi as a fuzzy match

        //Try deleting the non-existent key with valid variants, to make sure nothing breaks
        table.remove_keys(fri, &["Vendredi"]).unwrap();
        let results : Vec<RecordID> = table.lookup_fuzzy_raw("Vendredi").unwrap().collect();
        assert_eq!(results.len(), 1); //We'll still get Venerdi as a fuzzy match
        assert_eq!(table.keys_count(fri).unwrap(), 4);

        //Finally delete "Venerdi", and make sure the variants are all gone
        table.remove_keys(fri, &["Venerdi"]).unwrap();
        let results : Vec<RecordID> = table.lookup_fuzzy_raw("Vendredi").unwrap().collect();
        assert_eq!(results.len(), 0);
        assert_eq!(table.keys_count(fri).unwrap(), 3);

        //Test whether [char] keys get properly converted to UTF-8-encoded Strings internally
        // when used as the key to a Table with UTF-8 key encoding.
        let sun_japanese = table.insert("日曜日", &"Sunday".to_string()).unwrap();
        let key_array = ['日', '曜', '日'];
        let results : Vec<RecordID> = table.lookup_exact(&key_array).unwrap().collect();
        assert_eq!(results.len(), 1);
        assert!(results.contains(&sun_japanese));
        let results : Vec<RecordID> = table.lookup_fuzzy_raw(&key_array).unwrap().collect();
        assert_eq!(results.len(), 1);
        assert!(results.contains(&sun_japanese));

        let key_array = ['土', '曜', '日'];
        let sat_japanese = table.insert(&key_array, &"Saturday".to_string()).unwrap();
        let results : Vec<RecordID> = table.lookup_exact("土曜日").unwrap().collect();
        assert_eq!(results.len(), 1);
        assert!(results.contains(&sat_japanese));
        let results : Vec<RecordID> = table.lookup_fuzzy_raw("土曜日").unwrap().collect();
        assert_eq!(results.len(), 2);
        assert!(results.contains(&sat_japanese));
        assert!(results.contains(&sun_japanese));
    }

    #[test]
    /// This test is tests some basic non-unicode key functionality.
    fn non_unicode_key_test() {

        //Configure and Create the FuzzyRocks Table
        struct Config();
        impl TableConfig for Config {
            type KeyCharT = u8;
            type DistanceT = u8;
            type ValueT = f32;
            const MAX_DELETES : usize = 1;
            const MEANINGFUL_KEY_LEN : usize = 8;
            const UTF8_KEYS : bool = false;
        }
        let mut table = Table::<Config, false>::new("test2.rocks", Config()).unwrap();

        //Clear out any records that happen to be hanging out from a previous run
        table.reset().unwrap();

        let one = table.insert(b"One", &1.0).unwrap();
        let _two = table.insert(b"Dos", &2.0).unwrap();
        let _three = table.insert(b"San", &3.0).unwrap();
        let pi = table.insert(b"Pi", &3.1415926535).unwrap();

        let results : Vec<RecordID> = table.lookup_best(b"P").unwrap().collect();
        assert_eq!(results.len(), 1);
        assert!(results.contains(&pi));
        
        let results : Vec<RecordID> = table.lookup_fuzzy_raw(b"ne").unwrap().collect();
        assert_eq!(results.len(), 1);
        assert_eq!(results[0], one);
    }

    #[test]
    /// This tests the perf-counters
    fn perf_counters_test() {

        //Configure and Create the FuzzyRocks Table using a very big database
        struct Config();
        impl TableConfig for Config {
            type KeyCharT = char;
            type DistanceT = u8;
            type ValueT = i32;
        }
        let table = Table::<Config, true>::new("all_cities.geonames.rocks", Config()).unwrap();

        //Make sure we have no pathological case of a variant for a zero-length string
        let iter = table.lookup_fuzzy_raw("").unwrap();
        assert_eq!(iter.count(), 0);

        #[cfg(feature = "perf_counters")]
        {
            //Make sure we are on the fast path that doesn't fetch the keys for the case when the key
            //length entirely fits within config.meaningful_key_len
            //BUG!!: This optimization is probably bogus and causes additional incorrect results.  See the
            // comment in Table::lookup_exact_internal()
            table.reset_perf_counters();
            let _iter = table.lookup_exact("london").unwrap();
            assert_eq!(table.get_perf_counters().key_group_load_count, 0);

            //Test that the other counters do something...
            table.reset_perf_counters();
            let iter = table.lookup_fuzzy("london", None).unwrap();
            let _ = iter.count();
            assert!(table.get_perf_counters().variant_lookup_count > 0);
            assert!(table.get_perf_counters().variant_load_count > 0);
            assert!(table.get_perf_counters().key_group_ref_count > 0);
            assert!(table.get_perf_counters().max_variant_entry_refs > 0);
            assert!(table.get_perf_counters().key_group_load_count > 0);
            assert!(table.get_perf_counters().keys_found_count > 0);
            assert!(table.get_perf_counters().distance_function_invocation_count > 0);
            assert!(table.get_perf_counters().records_found_count > 0);

            //Debug Prints
            println!("-=-=-=-=-=-=-=-=- lookup_fuzzy london test -=-=-=-=-=-=-=-=-");
            println!("variant_lookup_count {}", table.get_perf_counters().variant_lookup_count);
            println!("variant_load_count {}", table.get_perf_counters().variant_load_count);
            println!("key_group_ref_count {}", table.get_perf_counters().key_group_ref_count);
            println!("max_variant_entry_refs {}", table.get_perf_counters().max_variant_entry_refs);
            println!("key_group_load_count {}", table.get_perf_counters().key_group_load_count);
            println!("keys_found_count {}", table.get_perf_counters().keys_found_count);
            println!("distance_function_invocation_count {}", table.get_perf_counters().distance_function_invocation_count);
            println!("records_found_count {}", table.get_perf_counters().records_found_count);

            //Test the perf counters with lookup_exact
            table.reset_perf_counters();
            let iter = table.lookup_exact("london").unwrap();
            let _ = iter.count();
            println!("-=-=-=-=-=-=-=-=- lookup_exact london test -=-=-=-=-=-=-=-=-");
            println!("variant_lookup_count {}", table.get_perf_counters().variant_lookup_count);
            println!("variant_load_count {}", table.get_perf_counters().variant_load_count);
            println!("key_group_ref_count {}", table.get_perf_counters().key_group_ref_count);
            println!("max_variant_entry_refs {}", table.get_perf_counters().max_variant_entry_refs);
            println!("key_group_load_count {}", table.get_perf_counters().key_group_load_count);
            println!("keys_found_count {}", table.get_perf_counters().keys_found_count);
            println!("distance_function_invocation_count {}", table.get_perf_counters().distance_function_invocation_count);
            println!("records_found_count {}", table.get_perf_counters().records_found_count);
        }
        
        #[cfg(not(feature = "perf_counters"))]
        {
            println!("perf_counters feature not enabled");
        }
    }

}