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
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).
- Features
- Usage
- Installation
- Advanced Usage
- Index Types and Their Capabilities
- Nested Structs
- Sort
- Text pattern search with LIKE condition
- Join
- Complex Primary Keys and Composite Indices
- Aggregations
- Search in array fields with matching array indexes
- Atomic on update functions
- Expire Data from Namespace by Setting TTL
- Direct JSON operations
- Using object cache
- Logging, debug and profiling
- Integration with other program languages
- Limitations and known issues
- Getting help
Key features:
- Sortable indices
- Aggregation queries
- Indices on array fields
- Complex primary keys
- Composite indices
- Join operations
- Full-text search
- Up to 64 indices for one namespace
- ORM-like query interface
- SQL queries
Performance has been our top priority from the start, and we think we managed to get it pretty good. Benchmarks show that Reindexer's performance is on par with a typical key-value database. On a single CPU core, we get:
- up to 500K queries/sec for queries
SELECT * FROM items WHERE id='?'
- up to 50K queries/sec for queries
SELECT * FROM items WHERE year > 2010 AND name = 'string' AND id IN (....)
- up to 20K queries/sec for queries
SELECT * FROM items WHERE year > 2010 AND name = 'string' JOIN subitems ON ...
See benchmarking results and more details in benchmarking section
Reindexer aims to consume as little memory as possible; most queries are processed without memory allocs at all.
To achieve that, several optimizations are employed, both on the C++ and Go level:
-
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.
Reindexer has internal full text search engine. Full text search usage documentation and examples are here
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.
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 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:[email protected]: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)
}
}
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'
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
- Install Reindexer Server
- go get -a github.com/restream/reindexer
The simplest way to get reindexer server, is pulling & run docker image from dockerhub.
docker run -p9088:9088 -p6534:6534 -it reindexer/reindexer
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.
go get -a github.com/restream/reindexer
bash $GOPATH/src/github.com/restream/reindexer/dependencies.sh
go generate github.com/restream/reindexer/bindings/builtin
# Optional (build builtin server binding)
go generate github.com/restream/reindexer/bindings/builtinserver
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 thanhash
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 withpk
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. Forhash
andtree
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 fortree
andhash
indexes with low selectivity can seriously decrease update performance. Alsodense
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
orrstar
- specify algorithm for construction ofrtree
index (by defaultrstar
). For details see geometry subsection.
Fields with regular indexes are not nullable. Condition is NULL
is supported only by sparse
and array
indexes.
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
}
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
query = db.Query("actors").
Join(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).
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 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 and item you should do the following:
update ns drop array[5]
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.
// 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)
}
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.
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)
- 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.
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.
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))
}
}
}
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"`
}
OR
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})
Reindexer allows to retrieve aggregated results. Currently Average, Sum, Minimum, Maximum Facet and Distinct aggregations are supported.
AggregateMax
- get maximum field valueAggregateMin
- get minimum field valueAggregateSum
- get sum field valueAggregateAvg
- get average field valueAggregateFacet
- get fields facet valueDistinct
- 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)
}
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")
or
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).
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()")
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.
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)
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" }] }
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
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.
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"
}
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{})
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 `reindexer:"id,,pk"`
pointIndexed [2]float64 `reindexer:"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);
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{})
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 executionlevel
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
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
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
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.
A list of connectors for work with Reindexer via other program languages (TBC later):
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).
- 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.
https://github.com/Smolevich/reindexer-client
- Support modes: standalone only
- API Used: HTTP REST API
- Dependency on reindexer library (reindexer-dev package): no
https://github.com/coinrust/reindexer-rs
- Support modes: standalone, builtin
- API Used: binary ABI, cproto
- Dependency on reindexer library (reindexer-dev package): yes
https://github.com/oruchreis/ReindexerNet
- Support modes: builtin
- API Used: binary ABI
- Dependency on reindexer library (reindexer-dev package): yes
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.
You can get help in several ways:
- Join Reindexer Telegram group
- Write an issue