Reindexer

Reindexer is an embeddable, in-memory, document-oriented database with a high-level Query builder interface.

Reindexer's goal is to provide fast search with complex queries. We at Restream weren't happy with Elasticsearch and created Reindexer as a more performant alternative.

The core is written in C++ and the application level API is in Go.

This document describes Go connector and its API. To get information about reindexer server and HTTP API refer to reindexer documentation

Versions overview

There are two LTS-versions of reindexer available: v3.x.x and v4.x.x.

3.x.x is currently our mainstream branch and 4.x.x (release/4 branch) is beta-version with experimental RAFT-cluster and sharding support. Storages are compatible between those versions, hovewer, replication configs are totally different. Versions 3 and 4 are geting all the same bugfixes and features (except replication-related ones).

Documents and indices are stored in dense binary C++ structs, so they don't impose any load on Go's garbage collector.
String duplicates are merged.
Memory overhead is about 32 bytes per document + ≈4-16 bytes per each search index.
There is an object cache on the Go level for deserialized documents produced after query execution. Future queries use pre-deserialized documents, which cuts repeated deserialization and allocation costs
The Query interface uses sync.Pool for reusing internal structures and buffers. Combining of these techings lets Reindexer execute most of queries without any allocations.

Full text search

Reindexer has internal full text search engine. Full text search usage documentation and examples are here

Disk Storage

Reindexer can store documents to and load documents from disk via LevelDB. Documents are written to the storage backend asynchronously by large batches automatically in background.

When a namespace is created, all its documents are stored into RAM, so the queries on these documents run entirely in in-memory mode.

Usage

Here is complete example of basic Reindexer usage:

package main

// Import package
import (
	"fmt"
	"math/rand"

	"github.com/restream/reindexer"
	// choose how the Reindexer binds to the app (in this case "builtin," which means link Reindexer as a static library)
	_ "github.com/restream/reindexer/bindings/builtin"

	// OR use Reindexer as standalone server and connect to it via TCP.
	// _ "github.com/restream/reindexer/bindings/cproto"

	// OR link Reindexer as static library with bundled server.
	// _ "github.com/restream/reindexer/bindings/builtinserver"
	// "github.com/restream/reindexer/bindings/builtinserver/config"

)

// Define struct with reindex tags
type Item struct {
	ID       int64  `reindex:"id,,pk"`    // 'id' is primary key
	Name     string `reindex:"name"`      // add index by 'name' field
	Articles []int  `reindex:"articles"`  // add index by articles 'articles' array
	Year     int    `reindex:"year,tree"` // add sortable index by 'year' field
}

func main() {
	// Init a database instance and choose the binding (builtin)
	db := reindexer.NewReindex("builtin:///tmp/reindex/testdb")

	// OR - Init a database instance and choose the binding (connect to server)
	// Database should be created explicitly via reindexer_tool or via WithCreateDBIfMissing option:
	// If server security mode is enabled, then username and password are mandatory
	// db := reindexer.NewReindex("cproto://user:pass@127.0.0.1:6534/testdb", reindexer.WithCreateDBIfMissing())

	// OR - Init a database instance and choose the binding (builtin, with bundled server)
	// serverConfig := config.DefaultServerConfig ()
	// If server security mode is enabled, then username and password are mandatory
	// db := reindexer.NewReindex("builtinserver://user:pass@testdb",reindexer.WithServerConfig(100*time.Second, serverConfig))

	// Create new namespace with name 'items', which will store structs of type 'Item'
	db.OpenNamespace("items", reindexer.DefaultNamespaceOptions(), Item{})

	// Generate dataset
	for i := 0; i < 100000; i++ {
		err := db.Upsert("items", &Item{
			ID:       int64(i),
			Name:     "Vasya",
			Articles: []int{rand.Int() % 100, rand.Int() % 100},
			Year:     2000 + rand.Int()%50,
		})
		if err != nil {
			panic(err)
		}
	}

	// Query a single document
	elem, found := db.Query("items").
		Where("id", reindexer.EQ, 40).
		Get()

	if found {
		item := elem.(*Item)
		fmt.Println("Found document:", *item)
	}

	// Query multiple documents
	query := db.Query("items").
		Sort("year", false).                          // Sort results by 'year' field in ascending order
		WhereString("name", reindexer.EQ, "Vasya").   // 'name' must be 'Vasya'
		WhereInt("year", reindexer.GT, 2020).         // 'year' must be greater than 2020
		WhereInt("articles", reindexer.SET, 6, 1, 8). // 'articles' must contain one of [6,1,8]
		Limit(10).                                    // Return maximum 10 documents
		Offset(0).                                    // from 0 position
		ReqTotal()                                    // Calculate the total count of matching documents

	// Execute the query and return an iterator
	iterator := query.Exec()
	// Iterator must be closed
	defer iterator.Close()

	fmt.Println("Found", iterator.TotalCount(), "total documents, first", iterator.Count(), "documents:")

	// Iterate over results
	for iterator.Next() {
		// Get the next document and cast it to a pointer
		elem := iterator.Object().(*Item)
		fmt.Println(*elem)
	}
	// Check the error
	if err := iterator.Error(); err != nil {
		panic(err)
	}
}

There are also some basic samples for C++ and Go here

SQL compatible interface

As alternative to Query builder Reindexer provides SQL compatible query interface. Here is sample of SQL interface usage:

    ...
	iterator := db.ExecSQL ("SELECT * FROM items WHERE name='Vasya' AND year > 2020 AND articles IN (6,1,8) ORDER BY year LIMIT 10")
    ...

Please note, that Query builder interface is preferable way: It have more features, and faster than SQL interface

String literals should be enclosed in single quotes.

Composite indexes should be enclosed in double quotes.

	SELECT * FROM items WHERE "field1+field2" = 'Vasya'

If the field name does not start with alpha, '_' or '#' it must be enclosed in double quotes, examples:

	UPDATE items DROP "123"

	SELECT * FROM ns WHERE "123" = 'some_value'

	SELECT * FROM ns WHERE "123abc" = 123

	DELETE FROM ns WHERE "123abc123" = 111

Installation

Reindexer can run in 3 different modes:

embedded (builtin) Reindexer is embedded into application as static library, and does not reuqire separate server proccess.
embedded with server (builtinserver) Reindexer is embedded into application as static library, and start server. In this mode other clients can connect to application via cproto or http.
standalone Reindexer run as standalone server, application connects to Reindexer via network

Installation for server mode

In this mode reindexer's Go-binding does not depend on reindexer's static library.

Install Reindexer Server
go get -a github.com/restream/reindexer

Official docker image

The simplest way to get reindexer server, is pulling & run docker image from dockerhub.

docker run -p9088:9088 -p6534:6534 -it reindexer/reindexer

Dockerfile

Installation for embedded mode

Prerequirements

Reindexer's core is written in C++17 and uses LevelDB as the storage backend, so the Cmake, C++17 toolchain and LevelDB must be installed before installing Reindexer.

To build Reindexer, g++ 8+, clang 7+ or mingw64 is required.

In those modes reindexer's Go-binding depends on reindexer's static libraries (core, server and resource).

Get Reindexer using go.mod

Go modules do not allow to build C++ libraries in modules' directories. Go-binding will use pkg-config to detect libraries' directories.

Reindexer's libraries must be either installed from sources or from prebuilt package via package manager.

Then get the module:

go get -a github.com/restream/reindexer@master

Get Reindexer using go.mod and replace

If you need modified reindexer's sources, you can use replace like that.

Download and build reindexer:

# Clone reindexer's repo (sources could be copied any other way, for example via 'go get -a github.com/restream/reindexer' on older Go versions)
git clone https://github.com/restream/reindexer.git $GOPATH/src/reindexer
# Generate builtin binding
go generate $GOPATH/src/reindexer/bindings/builtin
# Optional (build builtin server binding)
go generate $GOPATH/src/reindexer/bindings/builtinserver

Add reindexer's module into your application and the replace it with local package:

# Go to your app's directory
cd /your/app/path
go get -a github.com/restream/reindexer@master
go mod edit -replace github.com/restream/reindexer=$GOPATH/src/github.com/restream/reindexer

In this case, Go-binding will generate explicit libraries' and paths' list and will not use pkg-config.

Get Reindexer (vendoring)

Go does not support proper vendoring for CGO code (golang/go#26366), however, it's possible to use vend to copy reindexer's sources into vendor-directory.

With vend you'll be able to call go generate -mod=vendor for builtin and builtinserver, placed in your vendor-directory.

It's also possible to copy simply copy reindexer's sources into yout project, using git clone.

In this cases all the dependecies from reindexer's go.mod must be installed manually with proper versions.

Advanced Usage

Index Types and Their Capabilities

Internally, structs are split into two parts:

indexed fields, marked with reindex struct tag
tuple of non-indexed fields

Queries are possible only on the indexed fields, marked with reindex tag. The reindex tag contains the index name, type, and additional options:

reindex:"<name>[[,<type>],<opts>]"

name – index name.
type – index type:
- hash – fast select by EQ and SET match. Used by default. Allows slow and inefficient sorting by field.
- tree – fast select by RANGE, GT, and LT matches. A bit slower for EQ and SET matches than hash index. Allows fast sorting results by field.
- text – full text search index. Usage details of full text search is described here
- - – column index. Can't perform fast select because it's implemented with full-scan technic. Has the smallest memory overhead.
- ttl - TTL index that works only with int64 fields. These indexes are quite convenient for representation of date fields (stored as UNIX timestamps) that expire after specified amount of seconds.
- rtree - available only DWITHIN match. Acceptable only for [2]float64 field type. For details see geometry subsection.
opts – additional index options:
- pk – field is part of a primary key. Struct must have at least 1 field tagged with pk
- composite – create composite index. The field type must be an empty struct: struct{}.
- joined – field is a recipient for join. The field type must be []*SubitemType.
- dense - reduce index size. For hash and tree it will save 8 bytes per unique key value. For - it will save 4-8 bytes per each element. Useful for indexes with high selectivity, but for tree and hash indexes with low selectivity can seriously decrease update performance. Also dense will slow down wide fullscan queries on - indexes, due to lack of CPU cache optimization.
- sparse - Row (document) contains a value of Sparse index only in case if it's set on purpose - there are no empty (or default) records of this type of indexes in the row (document). It allows to save RAM but it will cost you performance - it works a bit slower than regular indexes.
- collate_numeric - create string index that provides values order in numeric sequence. The field type must be a string.
- collate_ascii - create case-insensitive string index works with ASCII. The field type must be a string.
- collate_utf8 - create case-insensitive string index works with UTF8. The field type must be a string.
- collate_custom=<ORDER> - create custom order string index. The field type must be a string. <ORDER> is sequence of letters, which defines sort order.
- linear, quadratic, greene or rstar - specify algorithm for construction of rtree index (by default rstar). For details see geometry subsection.

Fields with regular indexes are not nullable. Condition is NULL is supported only by sparse and array indexes.

Nested Structs

By default Reindexer scans all nested structs and adds their fields to the namespace (as well as indexes specified).

type Actor struct {
	Name string `reindex:"actor_name"`
}

type BaseItem struct {
	ID int64 `reindex:"id,hash,pk"`
}

type ComplexItem struct {
	BaseItem         // Index fields of BaseItem will be added to reindex
	actor    []Actor // Index fields of Actor will be added to reindex as arrays
	Name     string  `reindex:"name"`
	Year     int     `reindex:"year,tree"`
	parent   *Item   `reindex:"-"` // Index fields of parent will NOT be added to reindex
}

Sort

Reindexer can sort documents by fields (including nested and fields of joined namespaces) or by expressions in ascending or descending order.

Sort expressions can contain fields names (including nested and fields of joined namespaces) of int, float or bool type, numbers, functions rank(), abs() and ST_Distance(), parenthesis and arithmetic operations: +, - (unary and binary), * and /. If field name followed by '+' they must be separated by space (to distinguish composite index name). Fields of joined namespaces writes in form joined_namespace.field.

Abs() means absolute value of an argument.

Rank() means fulltext rank of match and is applicable only in fulltext query.

ST_Distance() means distance between geometry points (see geometry subsection). The points could be columns in current or joined namespaces or fixed point in format ST_GeomFromText('point(1 -3)')

In SQL query sort expression must be quoted.

type Person struct {
	Name string `reindex:"name"`
	Age  int    `reindex:"age"`
}

type City struct {
	Id                 int        `reindex:"id"`
	NumberOfPopulation int        `reindex:"population"`
	Center             [2]float64 `reindex:"center,rtree,linear"`
}

type Actor struct {
	ID          int        `reindex:"id"`
	PersonData  Person     `reindex:"person"`
	Price       int        `reindex:"price"`
	Description string     `reindex:"description,text"`
	BirthPlace  int        `reindex:"birth_place_id"`
	Location    [2]float64 `reindex:"location,rtree,greene"`
}
....

query := db.Query("actors").Sort("id", true)           // Sort by field
....
query = db.Query("actors").Sort("person.age", true)   // Sort by nested field
....
// Sort by joined field
// Works for inner join only, when each item from left namespace has exactly one joined item from right namespace
query = db.Query("actors").
	InnerJoin(db.Query("cities")).On("birth_place_id", reindexer.EQ, "id").
	Sort("cities.population", true)
....
// Sort by expression:
query = db.Query("actors").Sort("person.age / -10 + price / 1000 * (id - 5)", true)
....
query = db.Query("actors").Where("description", reindexer.EQ, "ququ").
    Sort("rank() + id / 100", true)   // Sort with fulltext rank
....
// Sort by geometry distance
query = db.Query("actors").
    Join(db.Query("cities")).On("birth_place_id", reindexer.EQ, "id").
    Sort("ST_Distance(cities.center, ST_GeomFromText('point(1 -3)'))", true).
    Sort("ST_Distance(location, cities.center)", true)
....
// In SQL query:
iterator := db.ExecSQL ("SELECT * FROM actors ORDER BY person.name ASC")
....
iterator := db.ExecSQL ("SELECT * FROM actors WHERE description = 'ququ' ORDER BY 'rank() + id / 100' DESC")
....
iterator := db.ExecSQL ("SELECT * FROM actors ORDER BY 'ST_Distance(location, ST_GeomFromText(\'point(1 -3)\'))' ASC")

It is also possible to set a custom sort order like this

type SortModeCustomItem struct {
	ID      int    `reindex:"id,,pk"`
	InsItem string `reindex:"item_custom,hash,collate_custom=a-zA-Z0-9"`
}

or like this

type SortModeCustomItem struct {
	ID      int    `reindex:"id,,pk"`
	InsItem string `reindex:"item_custom,hash,collate_custom=АаБбВвГгДдЕеЖжЗзИиКкЛлМмНнОоПпРрСсТтУуФфХхЦцЧчШшЩщЪъЫыЬьЭ-ЯAaBbCcDdEeFfGgHhIiJjKkLlMmNnOoPpQqRrSsTtUuVvWwXxYyZz0-9ЁёЙйэ-я"`
}

The very first character in this list has the highest priority, priority of the last character is the smallest one. It means that sorting algorithm will put items that start with the first character before others. If some characters are skipped their priorities would have their usual values (according to characters in the list).

Text pattern search with LIKE condition

For simple searching text pattern in string fields condition LIKE can be used. It search strings which match a pattern. In the pattern _ means any char and % means any sequence of chars.

Go example:

	query := db.Query("items").
		Where("field", reindexer.LIKE, "pattern")

SQL example:
```sql
	SELECT * FROM items WHERE fields LIKE 'pattern'

'me_t' corresponds to 'meet', 'meat', 'melt' and so on '%tion' corresponds to 'tion', 'condition', 'creation' and so on

CAUTION: condition LIKE uses scan method. It can be used for debug purposes or within queries with another good selective conditions.

Generally for full text search with reasonable speed we recommend to use fulltext index.

Update queries

UPDATE queries are used to modify existing items of a namespace. There are several kinds of update queries: updating existing fields, adding new fields and dropping existing non-indexed fields.

UPDATE Sql-Syntax

UPDATE nsName
SET field1 = value1, field2 = value2, ..
WHERE condition;

It is also possible to use arithmetic expressions with +, -, /, * and brackets

UPDATE NS SET field1 = field2+field3-(field4+5)/2

including functions like now(), sec() and serial(). To use expressions from Golang code SetExpression() method needs to be called instead of Set().

To make an array-field empty

UPDATE NS SET arrayfield = [] where id = 100

and set it to null

UPDATE NS SET field = null where id > 100

In case of non-indexed fields, setting its value to a value of a different type will replace it completely; in case of indexed fields, it is only possible to convert it from adjacent type (integral types and bool), numeric strings (like "123456") to integral types and back. Setting indexed field to null resets it to a default value.

It is possible to add new fields to existing items

UPDATE Ns set newField = 'Brand new!' where id > 100

and even add a new field by a complex nested path like this

UPDATE Ns set nested.nested2.nested3.nested4.newField = 'new nested field!' where id > 100

will create the following nested objects: nested, nested2, nested3, nested4 and newField as a member of object nested4.

Example of using Update queries in golang code:

db.Query("items").Where("id", reindexer.EQ, 40).Set("field1", values).Update()

Reindexer enables to update and add object fields. Object can be set by either a struct, a map or a byte array (that is a JSON version of object representation).

type ClientData struct {
    Name          string `reindex:"name" json:"name"`
	Age           int    `reindex:"age" json:"age"`
	Address       int    `reindex:"year" json:"year"`
	Occupation    string `reindex:"occupation" json:"occupation"`
	TaxYear       int    `reindex:tax_year json:"tax_year"`
	TaxConsultant string `reindex:tax_consultant json:"tax_consultant"`
}
type Client struct {
	ID      int         `reindex:"id" json:"id"`
	Data    ClientData  `reindex:"client_data" json:"client_data"`
	...
}
clientData := updateClientData(clientId)
db.Query("clients").Where("id", reindexer.EQ, 100).SetObject("client_data", clientData).Update()

In this case, Map in golang can only work with string as a key. map[string]interface{} is a perfect choice.

Updating of object field by Sql statement:

UPDATE clients SET client_data = {"Name":"John Doe","Age":40,"Address":"Fifth Avenue, Manhattan","Occupation":"Bank Manager","TaxYear":1999,"TaxConsultant":"Jane Smith"} where id = 100;

UPDATE Sql-Syntax of queries that drop existing non-indexed fields:

UPDATE nsName
DROP field1, field2, ..
WHERE condition;

db.Query("items").Where("id", reindexer.EQ, 40).Drop("field1").Update()

Reindexer update mechanism enables to modify array fields: to modify a certain item of an existing array or even to replace an entire field.

To update an item subscription operator syntax is used:

update ns set array[*].prices[0] = 9999 where id = 5

where * means all items.

To update entire array the following is used:

update ns set prices = [999, 1999, 2999] where id = 9

any non-indexed field can be easily converted to array using this syntax.

Reindexer also allows to update items of object arrays:

update ns set extra.objects[0] = {"Id":0,"Description":"Updated!"} where id = 9

also like this

db.Query("clients").Where("id", reindexer.EQ, 100).SetObject("extra.objects[0]", updatedValue).Update()

To add items to an existing array the following syntax is supported:

update ns set integer_array = integer_array || [5,6,7,8]

and

update ns set integer_array = [1,2,3,4,5] || integer_array

The first one adds elements to the end of integer_array, the second one adds 5 items to the front of it. To make this code work in Golang SetExpression() should be used instead of Set().

To remove item by index you should do the following:

update ns drop array[5]

Transactions and batch update

Reindexer supports transactions. Transaction are performs atomic namespace update. There are synchronous and async transaction available. To start transaction method db.BeginTx() is used. This method creates transaction object, which provides usual Update/Upsert/Insert/Delete interface for application. For RPC clients there is transactions count limitation - each connection can't has more than 1024 opened transactions at the same time.

Synchronous mode

	// Create new transaction object
	tx, err := db.BeginTx("items");
	if err != nil {
		panic(err)
	}
	// Fill transaction object
	tx.Upsert(&Item{ID: 100})
	tx.Upsert(&Item{ID: 101})
	tx.Query().WhereInt("id", reindexer.EQ, 102).Set("Name", "Petya").Update()
	// Apply transaction
	if err := tx.Commit(); err != nil {
		panic(err)
	}

Async batch mode

For speed up insertion of bulk records async mode can be used.

	// Create new transaction object
	tx, err := db.BeginTx("items");
	if err != nil {
		panic(err)
	}
	// Prepare transaction object async.
	tx.UpsertAsync(&Item{ID: 100},func(err error) {})
	tx.UpsertAsync(&Item{ID: 100},func(err error) {})
	// Wait for async operations done, and apply transaction.
	if err := tx.Commit(); err != nil {
		panic(err)
	}

The second argument of UpsertAsync is completion function, which will be called after receiving server response. Also, if any error occurred during prepare process, then tx.Commit should return an error. So it is enough, to check error returned by tx.Commit - to be sure, that all data has been successfully committed or not.

Transactions commit strategies

Depending on amount of changes in transaction there are 2 possible Commit strategies:

Locked atomic update. Reindexer locks namespace and applying all changes under common lock. This mode is used with small amounts of changes.
Copy & atomic replace. In this mode Reindexer makes namespace's snapshot, applying all changes to this snapshot, and atomically replaces namespace without lock

Data amount for choosing Commit strategy can be choose in namespaces config. Check fields StartCopyPolicyTxSize, CopyPolicyMultiplier and TxSizeToAlwaysCopy in struct DBNamespacesConfig(describer.go)

Implementation notes

Transaction object is not thread safe and can't be used from different goroutines;
Transaction object holds Reindexer's resources, therefore application should explicitly call Rollback or Commit, otherwise resources will leak;
It is safe to call Rollback after Commit;
It is possible to call Query from transaction by call tx.Query("ns").Exec() ...;
Only serializable isolation is available, i.e. each transaction takes exclusive lock over the target namespace until all of the steps of the transaction commited.

Join

Reindexer can join documents from multiple namespaces into a single result:

type Actor struct {
	ID        int    `reindex:"id"`
	Name      string `reindex:"name"`
	IsVisible bool   `reindex:"is_visible"`
}

type ItemWithJoin struct {
	ID          int      `reindex:"id"`
	Name        string   `reindex:"name"`
	ActorsIDs   []int    `reindex:"actors_ids"`
	ActorsNames []int    `reindex:"actors_names"`
	Actors      []*Actor `reindex:"actors,,joined"`
}
....

query := db.Query("items_with_join").Join(
	db.Query("actors").
		WhereBool("is_visible", reindexer.EQ, true),
	"actors"
).On("actors_ids", reindexer.SET, "id")

query.Exec ()

In this example, Reindexer uses reflection under the hood to create Actor slice and copy Actor struct.

Join query may have from one to several On conditions connected with And (by default), Or or Not operators:

query := db.Query("items_with_join").
	Join(
		db.Query("actors").
			WhereBool("is_visible", reindexer.EQ, true),
		"actors").
	On("actors_ids", reindexer.SET, "id").
	Or().
	On("actors_names", reindexer.SET, "name")

An InnerJoin combines data from two namespaces where there is a match on the joining fields in both namespaces. A LeftJoin returns all valid items from the namespaces on the left side of the LeftJoin keyword, along with the values from the table on the right side, or nothing if a matching item doesn't exist. Join is an alias for LeftJoin.

InnerJoins can be used as a condition in Where clause:

query1 := db.Query("items_with_join").
	WhereInt("id", reindexer.RANGE, []int{0, 100}).
	Or().
	InnerJoin(db.Query("actors").WhereString("name", reindexer.EQ, "ActorName"), "actors").
	On("actors_ids", reindexer.SET, "id").
	Or().
	InnerJoin(db.Query("actors").WhereInt("id", reindexer.RANGE, []int{100, 200}), "actors").
	On("actors_ids", reindexer.SET, "id")

query2 := db.Query("items_with_join").
	WhereInt("id", reindexer.RANGE, []int{0, 100}).
	Or().
	OpenBracket().
		InnerJoin(db.Query("actors").WhereString("name", reindexer.EQ, "ActorName"), "actors").
		On("actors_ids", reindexer.SET, "id").
		InnerJoin(db.Query("actors").WhereInt("id", reindexer.RANGE, []int{100, 200}), "actors").
		On("actors_ids", reindexer.SET, "id").
	CloseBracket()

query3 := db.Query("items_with_join").
	WhereInt("id", reindexer.RANGE, []int{0, 100}).
	Or().
	InnerJoin(db.Query("actors").WhereInt("id", reindexer.RANGE, []int{100, 200}), "actors").
	On("actors_ids", reindexer.SET, "id").
	Limit(0)

Note that usually Or operator implements short-circuiting for Where conditions: if the previous condition is true the next one is not evaluated. But in case of InnerJoin it works differently: in query1 (from the example above) both InnerJoin conditions are evaluated despite the result of WhereInt. Limit(0) as part of InnerJoin (query3 from the example above) does not join any data - it works like a filter only to verify conditions.

Joinable interface

To avoid using reflection, Item can implement Joinable interface. If that implemented, Reindexer uses this instead of the slow reflection-based implementation. This increases overall performance by 10-20%, and reduces the amount of allocations.

// Joinable interface implementation.
// Join adds items from the joined namespace to the `ItemWithJoin` object.
// When calling Joinable interface, additional context variable can be passed to implement extra logic in Join.
func (item *ItemWithJoin) Join(field string, subitems []interface{}, context interface{}) {

	switch field {
	case "actors":
		for _, joinItem := range subitems {
			item.Actors = append(item.Actors, joinItem.(*Actor))
		}
	}
}

Complex Primary Keys and Composite Indexes

A Document can have multiple fields as a primary key. To enable this feature add composite index to struct. Composite index is an index that involves multiple fields, it can be used instead of several separate indexes.

type Item struct {
	ID    int64 `reindex:"id"`     // 'id' is a part of a primary key
	SubID int   `reindex:"sub_id"` // 'sub_id' is a part of a primary key
	// Fields
	//	....
	// Composite index
	_ struct{} `reindex:"id+sub_id,,composite,pk"`
}

type Item struct {
	ID       int64 `reindex:"id,-"`         // 'id' is a part of primary key, WITHOUT personal searchable index
	SubID    int   `reindex:"sub_id,-"`     // 'sub_id' is a part of a primary key, WITHOUT a personal searchable index
	SubSubID int   `reindex:"sub_sub_id,-"` // 'sub_sub_id' is a part of a primary key WITHOUT a personal searchable index

	// Fields
	// ....

	// Composite index
	_ struct{} `reindex:"id+sub_id+sub_sub_id,,composite,pk"`
}

Also composite indexes are useful for sorting results by multiple fields:

type Item struct {
	ID     int64 `reindex:"id,,pk"`
	Rating int   `reindex:"rating"`
	Year   int   `reindex:"year"`

	// Composite index
	_ struct{} `reindex:"rating+year,tree,composite"`
}

...
	// Sort query results by rating first, then by year
	query := db.Query("items").Sort("rating+year", true)

	// Sort query results by rating first, then by year, and put item where rating == 5 and year == 2010 first
	query := db.Query("items").Sort("rating+year", true,[]interface{}{5,2010})

For make query to the composite index, pass []interface{} to .WhereComposite function of Query builder:

	// Get results where rating == 5 and year == 2010
	query := db.Query("items").WhereComposite("rating+year", reindexer.EQ,[]interface{}{5,2010})

Aggregations

Reindexer allows to retrieve aggregated results. Currently Average, Sum, Minimum, Maximum Facet and Distinct aggregations are supported.

AggregateMax - get maximum field value
AggregateMin - get minimum field value
AggregateSum - get sum field value
AggregateAvg - get average field value
AggregateFacet - get fields facet value
Distinct - get list of unique values of the field

In order to support aggregation, Query has methods AggregateAvg, AggregateSum, AggregateMin, AggregateMax, AggregateFacet and Distinct those should be called before the Query execution: this will ask reindexer to calculate data aggregations. Aggregation Facet is applicable to multiple data columns and the result of that could be sorted by any data column or 'count' and cutted off by offset and limit. In order to support this functionality method AggregateFacet returns AggregationFacetRequest which has methods Sort, Limit and Offset.

To get aggregation results, Iterator has method AggResults: it is available after query execution and returns slice of results.

Example code for aggregate items by price and name

	query := db.Query("items")
	query.AggregateMax("price")
	query.AggregateFacet("name", "price").Sort("name", true).Sort("count", false).Offset(10).Limit(100)
	iterator := query.Exec()

	aggMaxRes := iterator.AggResults()[0]

	fmt.Printf ("max price = %d", aggMaxRes.Value)

	aggFacetRes := iterator.AggResults()[1]

	fmt.Printf ("'name' 'price' -> count")
	for _, facet := range aggFacetRes.Facets {
		fmt.Printf ("'%s' '%s' -> %d", facet.Values[0], facet.Values[1], facet.Count)
	}

	query := db.Query("items")
	query.Distinct("name").Distinct("price")
	iterator := query.Exec()

	aggResults := iterator.aggResults()

	distNames := aggResults[0]
	fmt.Println ("names:")
	for _, name := range distNames.Distincts {
		fmt.Println(name)
	}

	distPrices := aggResults[1]
	fmt.Println ("prices:")
	for _, price := range distPrices.Distincts {
		fmt.Println(price)
	}

Search in array fields with matching array indexes

Reindexer allows to search data in array fields when matching values have same indexes positions. For instance, we've got an array of structures:

type Elem struct {
   F1 int `reindex:"f1"`
   F2 int `reindex:"f2"`
}

type A struct {
   Elems []Elem
}

Common attempt to search values in this array

db.Query("Namespace").Where("f1",EQ,1).Where("f2",EQ,2)

finds all items of array Elem[] where f1 is equal to 1 and f2 is equal to 2.

EqualPosition function allows to search in array fields with equal indexes. Queries like this:

db.Query("Namespace").Where("f1", reindexer.GE, 5).Where("f2", reindexer.EQ, 100).EqualPosition("f1", "f2")

SELECT * FROM Namespace WHERE f1 >= 5 AND f2 = 100 EQUAL_POSITION(f1,f2);

will find all the items of array Elem[] with equal array indexes where f1 is greater or equal to 5 and f2 is equal to 100 (for instance, query returned 5 items where only 3rd elements of both arrays have appropriate values).

With complex expressions (expressions with brackets) equal_position() could be within a bracket:

SELECT * FROM Namespace WHERE (f1 >= 5 AND f2 = 100 EQUAL_POSITION(f1,f2)) OR (f3 = 3 AND f4 < 4 AND f5 = 7 EQUAL_POSITION(f3,f4,f5));
SELECT * FROM Namespace WHERE (f1 >= 5 AND f2 = 100 AND f3 = 3 AND f4 < 4 EQUAL_POSITION(f1,f3) EQUAL_POSITION(f2,f4)) OR (f5 = 3 AND f6 < 4 AND f7 = 7 EQUAL_POSITION(f5,f7));
SELECT * FROM Namespace WHERE f1 >= 5 AND (f2 = 100 AND f3 = 3 AND f4 < 4 EQUAL_POSITION(f2,f3)) AND f5 = 3 AND f6 < 4 EQUAL_POSITION(f1,f5,f6);

equal_position doesn't work with the following conditions: IS NULL, IS EMPTY and IN(with empty parameter list).

Atomic on update functions

There are atomic functions, which executes under namespace lock, and therefore guarantees data consistency:

serial() - sequence of integer, useful for auto-increment keys
now() - current time stamp, useful for data synchronization. It may have one of the following arguments: msec, usec, nsec and sec. The “sec” argument is used by default.

These functions can be passed to Upsert/Insert/Update in 3-rd and next arguments.

If these functions are provided, the passed by reference item will be changed to updated value

   // set ID field from serial generator
   db.Insert ("items",&item,"id=serial()")

   // set current timestamp in nanoseconds to updated_at field
   db.Update ("items",&item,"updated_at=now(NSEC)")

   // set current timestamp and ID
   db.Upsert ("items",&item,"updated_at=now(NSEC)","id=serial()")

Expire Data from Namespace by Setting TTL

Data expiration is useful for some classes of information, including machine generated event data, logs, and session information that only need to persist for a limited period of time.

Reindexer makes it possible to set TTL (time to live) for Namespace items. Adding TtlIndex to Namespace automatically removes items after a specified number of seconds.

Ttl indexes work only with int64 fields and store UNIX timestamp data. Items containing ttl index expire after expire_after seconds. Example of declaring TtlIndex in Golang:

            type NamespaceExample struct {
                ID   int    `reindex:"id,,pk" json:"id"`
                Date int64  `reindex:"date,ttl,,expire_after=3600" json:"date"`
            }
            ...
            ns.Date = time.Now().Unix()

In this case items of namespace NamespaceExample expire in 3600 seconds after NamespaceExample.Date field value (which is UNIX timestamp).

A TTL index supports queries in the same way non-TTL indexes do.

Direct JSON operations

Upsert data in JSON format

If source data is available in JSON format, then it is possible to improve performance of Upsert/Delete operations by directly passing JSON to reindexer. JSON deserialization will be done by C++ code, without extra allocs/deserialization in Go code.

Upsert or Delete functions can process JSON just by passing []byte argument with json

	json := []byte (`{"id":1,"name":"test"}`)
	db.Upsert  ("items",json)

It is just faster equivalent of:

	item := &Item{}
	json.Unmarshal ([]byte (`{"id":1,"name":"test"}`),item)
	db.Upsert ("items",item)

Get Query results in JSON format

In case of requirement to serialize results of Query in JSON format, then it is possible to improve performance by directly obtaining results in JSON format from reindexer. JSON serialization will be done by C++ code, without extra allocs/serialization in Go code.

...
	iterator := db.Query("items").
		Select ("id","name").        // Filter output JSON: Select only "id" and "name" fields of items, another fields will be omitted
		Limit (1).
		ExecToJson ("root_object")   // Name of root object of output JSON

	json,err := iterator.FetchAll()
	// Check the error
	if err != nil {
		panic(err)
	}
	fmt.Printf ("%s\n",string (json))
...

This code will print something like:

{ "root_object": [{ "id": 1, "name": "test" }] }

Using object cache

To avoid race conditions, by default object cache is turned off and all objects are allocated and deserialized from reindexer internal format (called CJSON) per each query. The deserialization is uses reflection, so its speed is not optimal (in fact CJSON deserialization is ~3-10x faster than JSON, and ~1.2x faster than GOB), but performance is still seriously limited by reflection overhead.

There are 2 ways to enable object cache:

Provide DeepCopy interface
Ask query return shared objects from cache

DeepCopy interface

If object is implements DeepCopy interface, then reindexer will turn on object cache and use DeepCopy interface to copy objects from cache to query results. The DeepCopy interface is responsible to make deep copy of source object.

Here is sample of DeepCopy interface implementation

func (item *Item) DeepCopy () interface {} {
	copyItem := &Item{
		ID: item.ID,
		Name: item.Name,
		Articles: make ([]int,cap (item.Articles),len (item.Articles)),
		Year: item.Year,
	}
	copy (copyItem.Articles,item.Articles)
	return copyItem
}

There are available code generation tool gencopy, which can automatically generate DeepCopy interface for structs.

Get shared objects from object cache (USE WITH CAUTION)

To speed up queries and do not allocate new objects per each query it is possible ask query return objects directly from object cache. For enable this behavior, call AllowUnsafe(true) on Iterator.

WARNING: when used AllowUnsafe(true) queries returns shared pointers to structs in object cache. Therefore application MUST NOT modify returned objects.

	res, err := db.Query("items").WhereInt ("id",reindexer.EQ,1).Exec().AllowUnsafe(true).FetchAll()
	if err != nil {
		panic (err)
	}

	if len (res) > 1 {
		// item is SHARED pointer to struct in object cache
		item = res[0].(*Item)

		// It's OK - fmt.Printf will not modify item
		fmt.Printf ("%v",item)

		// It's WRONG - can race, and will corrupt data in object cache
		item.Name = "new name"
	}

Limit size of object cache

By default maximum size of object cache is 256000 items for each namespace. To change maximum size use ObjCacheSize method of NameapaceOptions, passed to OpenNamespace. e.g.

	// Set object cache limit to 4096 items
	db.OpenNamespace("items_with_huge_cache", reindexer.DefaultNamespaceOptions().ObjCacheSize(4096), Item{})

Geometry

The only supported geometry data type is 2D point, which implemented in Golang as [2]float64.

In SQL, a point can be created as ST_GeomFromText('point(1 -3)').

The only supported request for geometry field is to find all points within a distance from a point. DWithin(field_name, point, distance) as on example below.

Corresponding SQL function is ST_DWithin(field_name, point, distance).

RTree index can be created for points. To do so, rtree and linear, quadratic, greene or rstar tags should be declared. linear, quadratic, greene or rstar means which algorithm of RTree construction would be used. Here algorithms are listed in order from optimized for insertion to optimized for search. But it depends on data. Test which is more appropriate for you. Default algorithm is rstar.

type Item struct {
	id              int        `reindex:"id,,pk"`
	pointIndexed    [2]float64 `reindex:"point_indexed,rtree,linear"`
	pointNonIndexed [2]float64 `json:"point_non_indexed"`
}

query1 := db.Query("items").DWithin("point_indexed", [2]float64{-1.0, 1.0}, 4.0)

SELECT * FROM items WHERE ST_DWithin(point_non_indexed, ST_GeomFromText('point(1 -3.5)'), 5.0);

Logging, debug and profiling

Turn on logger

Reindexer logger can be turned on by db.SetLogger() method, just like in this snippet of code:

type Logger struct {
}
func (Logger) Printf(level int, format string, msg ...interface{}) {
	log.Printf(format, msg...)
}
...
	db.SetLogger (Logger{})

Slow actions logging

Reindexer supports logging of slow actions. It can be configured via profiling.long_queries_logging section of the #config system namespace. The logging of next actions can be configured:

SELECT queries:
- threshold_us (integer): The threshold value (in microseconds) for execution of SELECT query. If exceeded, a core-log entry will be made, if threshold_us is -1 logging is disabled.
- normalized (boolean): Output the query in a normalized form.
UPDATE and DELETE queries:
- threshold_us (integer): The threshold value (in microseconds) for execution of UPDATE or DELETE query. If exceeded, a core-log entry will be made, if threshold_us is -1 logging is disabled.
- normalized (boolean): Output the query in a normalized form.
Transactions:
- threshold_us (integer): Threshold value (in microseconds) for total transaction commit time, if threshold_us is -1 logging by total transaction commit time is disabled.
- avg_step_threshold_us (integer): Threshold value (in microseconds) for the average step duration time in the transaction. If avg_step_threshold_us is -1 logging by average transaction's step duraction is disabled.

Debug queries

Another useful feature is debug print of processed Queries. To debug print queries details there are 2 methods:

db.SetDefaultQueryDebug(namespace string,level int) - it globally enables print details of all queries by namespace
query.Debug(level int) - print details of query execution level is level of verbosity:
reindexer.INFO - will print only query conditions
reindexer.TRACE - will print query conditions and execution details with timings
query.Explain () - calculate and store query execution details.
iterator.GetExplainResults () - return query execution details

Custom allocators support

Reindexer has support for TCMalloc (which is also a part of GPerfTools) and JEMalloc allocators (check ENABLE_TCMALLOC and ENABLE_JEMALLOC in CMakeLists.txt).

If you have built standalone server from sources available allocators will be detected and used automatically.

In go:generate builds and prebuilt packages reindexer has TCMalloc support, however none of TCMalloc libraries will be linked automatically. To force allocator's libraries linkage LD_PRELOAD with required library has to be used:

LD_PRELOAD=/usr/lib/x86_64-linux-gnu/libtcmalloc_and_profiler.so ./my_executable

Custom allocator may be handy to track memory consumation, profile heap/CPU or to improve general perforamnce.

Profiling

Heap profiling

Because reindexer core is written in C++ all calls to reindexer and their memory consumption are not visible for go profiler. To profile reindexer core there are cgo profiler available. cgo profiler now is part of reindexer, but it can be used with any another cgo code.

Usage of cgo profiler is very similar with usage of go profiler.

Add import:

import _ "github.com/restream/reindexer/pprof"

If your application is not already running an http server, you need to start one. Add "net/http" and "log" to your imports and the following code to your main function:

go func() {
	log.Println(http.ListenAndServe("localhost:6060", nil))
}()

Run application with environment variable HEAPPROFILE=/tmp/pprof
Then use the pprof tool to look at the heap profile:

pprof -symbolize remote http://localhost:6060/debug/cgo/pprof/heap

CPU profiling

Internal reindexer's profiler is based on gperf_tools library and unable to get CPU profile via Go runtime. However, go profiler may be used with symbolizer to retrieve C++ CPU usage.

Add import:

import _ "net/http/pprof"

If your application is not already running an http server, you need to start one. Add "net/http" and "log" to your imports and the following code to your main function:

go func() {
	log.Println(http.ListenAndServe("localhost:6060", nil))
}()

Run application with environment variable REINDEXER_CGOBACKTRACE=1
Then use the pprof tool to get CPU profile:

pprof -symbolize remote http://localhost:6060/debug/pprof/profile?seconds=10

Known issues

Due to internal Golang's specific it's not recommended to try to get CPU and heap profiles simultaneously, because it may cause deadlock inside the profiler.

Integration with other program languages

A list of connectors for work with Reindexer via other program languages (TBC later):

Pyreindexer for Python

Pyreindexer is official connector, and maintained by Reindexer's team. It supports both builtin and standalone modes. Before installation reindexer-dev (version >= 2.10) should be installed. See installation instructions for details.

Support modes: standalone, builtin
API Used: binary ABI, cproto
Dependency on reindexer library (reindexer-dev package): yes

For install run:

pip3 install pyreindexer

https://github.com/Restream/reindexer-py https://pypi.org/project/pyreindexer/ Python version >=3.6 is required).

Reindexer for Java

Support modes: standalone, builtin, builtinserver
API Used: binary ABI, cproto
Dependency on reindexer library (reindexer-dev package): yes, for builtin & builtinserver

Reindexer for java is official connector, and maintained by Reindexer's team. It supports both builtin and standalone modes. For enable builtin mode support reindexer-dev (version >= 3.1.0) should be installed. See installation instructions for details.

For install reindexer to Java or Kotlin project add the following lines to maven project file

<dependency>
    <groupId>com.github.restream</groupId>
    <artifactId>rx-connector</artifactId>
    <version>[LATEST_VERSION]</version>
</dependency>

https://github.com/Restream/reindexer-java
Note: Java version >= 1.8 is required.

Spring wrapper

Spring wrapper for Java-connector: https://github.com/evgeniycheban/spring-data-reindexer

3rd party open source connectors

PHP

https://github.com/Smolevich/reindexer-client

Support modes: standalone only
API Used: HTTP REST API
Dependency on reindexer library (reindexer-dev package): no

Rust

https://github.com/coinrust/reindexer-rs

Support modes: standalone, builtin
API Used: binary ABI, cproto
Dependency on reindexer library (reindexer-dev package): yes

.NET

https://github.com/oruchreis/ReindexerNet

Support modes: builtin
API Used: binary ABI
Dependency on reindexer library (reindexer-dev package): yes

Limitations and known issues

Currently Reindexer is stable and production ready, but it is still a work in progress, so there are some limitations and issues:

Internal C++ API is not stabilized and is subject to change.

Getting help

You can get help in several ways:

Join Reindexer Telegram group
Write an issue