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https://github.com/timshannon/badgerhold
BadgerHold is an embeddable NoSQL store for querying Go types built on Badger
https://github.com/timshannon/badgerhold
Last synced: about 12 hours ago
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BadgerHold is an embeddable NoSQL store for querying Go types built on Badger
- Host: GitHub
- URL: https://github.com/timshannon/badgerhold
- Owner: timshannon
- License: mit
- Created: 2019-01-18T22:10:11.000Z (almost 6 years ago)
- Default Branch: master
- Last Pushed: 2024-04-19T13:08:26.000Z (7 months ago)
- Last Synced: 2024-08-02T11:15:03.291Z (3 months ago)
- Language: Go
- Homepage:
- Size: 188 KB
- Stars: 515
- Watchers: 10
- Forks: 52
- Open Issues: 18
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-ccamel - timshannon/badgerhold - BadgerHold is an embeddable NoSQL store for querying Go types built on Badger (Go)
README
# BadgerHold
[](https://github.com/timshannon/badgerhold/actions/workflows/go.yml) [](https://godoc.org/github.com/timshannon/badgerhold) [](https://coveralls.io/github/timshannon/badgerhold?branch=master) [](https://goreportcard.com/report/github.com/timshannon/badgerhold)
BadgerHold is a simple querying and indexing layer on top of a [Badger](https://github.com/dgraph-io/badger) instance. The goal is to create a simple, higher level interface on top of Badger DB that simplifies dealing with Go Types and finding data, but exposes the underlying Badger DB for customizing as you wish. By default the encoding used is Gob, so feel free to use the GobEncoder/Decoder interface for faster serialization. Or, alternately, you can use any serialization you want by supplying encode / decode funcs to the `Options` struct on Open.
One Go Type will be prefixed with it's type name, so you can store multiple types in a single Badger database with conflicts.
This project is a rewrite of the [BoltHold](https://github.com/timshannon/bolthold) project on the Badger KV database instead of [Bolt](https://github.com/etcd-io/bbolt). For a performance comparison between bolt and badger, see https://blog.dgraph.io/post/badger-lmdb-boltdb/. I've written up my own comparison of the two focusing on characteristics _other_ than performance here: https://tech.townsourced.com/post/boltdb-vs-badger/.
## Indexes
Indexes allow you to skip checking any records that don't meet your index criteria. If you have 1000 records and only 10 of them are of the Division you want to deal with, then you don't need to check to see if the other 990 records match your query criteria if you create an index on the Division field. The downside of an index is added disk reads and writes on every write operation. For read heavy operations datasets, indexes can be very useful.
In every BadgerHold store, there will be a reserved bucket _\_indexes_ which will be used to hold indexes that point back to another bucket's Key system. Indexes will be defined by setting the `badgerhold:"index"` struct tag on a field in a type.
```Go
type Person struct {
Name string
Division string `badgerhold:"index"`
}
// alternate struct tag if you wish to specify the index name
type Person struct {
Name string
Division string `badgerholdIndex:"IdxDivision"`
}
```
This means that there will be an index created for `Division` that will contain the set of unique divisions, and the main record keys they refer to.
Optionally, you can implement the `Storer` interface, to specify your own indexes, rather than using the `badgerHoldIndex` struct tag.
## Queries
Queries are chain-able constructs that filters out any data that doesn't match it's criteria. An index will be used if the `.Index()` chain is called, otherwise BadgerHold won't use any index.
Queries will look like this:
```Go
s.Find(&result, badgerhold.Where("FieldName").Eq(value).And("AnotherField").Lt(AnotherValue).Or(badgerhold.Where("FieldName").Eq(anotherValue)))
```
Fields must be exported, and thus always need to start with an upper-case letter. Available operators include:
- Equal - `Where("field").Eq(value)`
- Not Equal - `Where("field").Ne(value)`
- Greater Than - `Where("field").Gt(value)`
- Less Than - `Where("field").Lt(value)`
- Less than or Equal To - `Where("field").Le(value)`
- Greater Than or Equal To - `Where("field").Ge(value)`
- In - `Where("field").In(val1, val2, val3)`
- IsNil - `Where("field").IsNil()`
- Regular Expression - `Where("field").RegExp(regexp.MustCompile("ea"))`
- Matches Function - `Where("field").MatchFunc(func(ra *RecordAccess) (bool, error))`
- Skip - `Where("field").Eq(value).Skip(10)`
- Limit - `Where("field").Eq(value).Limit(10)`
- SortBy - `Where("field").Eq(value).SortBy("field1", "field2")`
- Reverse - `Where("field").Eq(value).SortBy("field").Reverse()`
- Index - `Where("field").Eq(value).Index("indexName")`
- Contains - `Where("field").Contains(val1)`
- ContainsAll - `Where("field").Contains(val1, val2, val3)`
- ContainsAny - `Where("field").Contains(val1, val2, val3)`
- HasKey - `Where("field").HasKey(val1) // to test if a Map value has a key`
An empty / zero value query matches against all records, because it has no critiera. You can then use `Skip` and `Limit` to page through all records in your dataset:
```Go
q := &badgerhold.Query{}
err := store.Find(&result, q.Skip(10).Limit(50))
```
If you want to run a query's criteria against the Key value, you can use the `badgerhold.Key` constant:
```Go
store.Find(&result, badgerhold.Where(badgerhold.Key).Ne(value))
```
You can access nested structure fields in queries like this:
```Go
type Repo struct {
Name string
Contact ContactPerson
}
type ContactPerson struct {
Name string
}
store.Find(&repo, badgerhold.Where("Contact.Name").Eq("some-name")
```
Instead of passing in a specific value to compare against in a query, you can compare against another field in the same struct. Consider the following struct:
```Go
type Person struct {
Name string
Birth time.Time
Death time.Time
}
```
If you wanted to find any invalid records where a Person's death was before their birth, you could do the following:
```Go
store.Find(&result, badgerhold.Where("Death").Lt(badgerhold.Field("Birth")))
```
Queries can be used in more than just selecting data. You can delete or update data that matches a query.
Using the example above, if you wanted to remove all of the invalid records where Death < Birth:
```Go
// you must pass in a sample type, so BadgerHold knows which bucket to use and what indexes to update
store.DeleteMatching(&Person{}, badgerhold.Where("Death").Lt(badgerhold.Field("Birth")))
```
Or if you wanted to update all the invalid records to flip/flop the Birth and Death dates:
```Go
store.UpdateMatching(&Person{}, badgerhold.Where("Death").Lt(badgerhold.Field("Birth")), func(record interface{}) error {
update, ok := record.(*Person) // record will always be a pointer
if !ok {
return fmt.Errorf("Record isn't the correct type! Wanted Person, got %T", record)
}
update.Birth, update.Death = update.Death, update.Birth
return nil
})
```
If you simply want to count the number of records returned by a query use the `Count` method:
```Go
// need to pass in empty datatype so badgerhold knows what type to count
count, err := store.Count(&Person{}, badgerhold.Where("Death").Lt(badgerhold.Field("Birth")))
```
You can also use `FindOne` which is a shorthand for `Find` + `Limit(1)` which returns a single record instead of a slice
of records, and will return an `ErrNotFound` if no record is found, unlike a normal `Find` query where an empty slice
would be returned with no error.
```Go
result := &ItemTest{}
err := store.FindOne(result, query)
```
### Keys in Structs
A common scenario is to store the badgerhold Key in the same struct that is stored in the badgerDB value. You can automatically populate a record's Key in a struct by using the `badgerhold:"key"` struct tag when running `Find` queries.
Another common scenario is to insert data with an auto-incrementing key assigned by the database. When performing an `Insert`, if the type of the key matches the type of the `badgerhold:"key"` tagged field, the data is passed in by reference, **and** the field's current value is the zero-value for that type, then it is set on the data _before_ insertion.
```Go
type Employee struct {
ID uint64 `badgerhold:"key"`
FirstName string
LastName string
Division string
Hired time.Time
}
// old struct tag, currenty still supported but may be deprecated in the future
type Employee struct {
ID uint64 `badgerholdKey`
FirstName string
LastName string
Division string
Hired time.Time
}
```
Badgerhold assumes only one of such struct tags exists. If a value already exists in the key field, it will be overwritten.
If you want to insert an auto-incrementing Key you can pass the `badgerhold.NextSequence()` func as the Key value.
```Go
err := store.Insert(badgerhold.NextSequence(), data)
```
The key value will be a `uint64`.
If you want to know the value of the auto-incrementing Key that was generated using `badgerhold.NextSequence()`, then make sure to pass a pointer to your data and that the `badgerholdKey` tagged field is of type `uint64`.
```Go
err := store.Insert(badgerhold.NextSequence(), &data)
```
### Slices in Structs and Queries
When querying slice fields in structs you can use the `Contains`, `ContainsAll` and `ContainsAny` criterion.
```Go
val := struct {
Set []string
}{
Set: []string{"1", "2", "3"},
}
bh.Where("Set").Contains("1") // true
bh.Where("Set").ContainsAll("1", "3") // true
bh.Where("Set").ContainsAll("1", "3", "4") // false
bh.Where("Set").ContainsAny("1", "7", "4") // true
```
The `In`, `ContainsAll` and `ContainsAny` critierion accept a slice of `interface{}` values. This means you can build your queries by passing in your values as arguments:
```
where := badgerhold.Where("Id").In("1", "2", "3")
```
However if you have an existing slice of values to test against, you can't pass in that slice because it is not of type
`[]interface{}`.
```Go
t := []string{"1", "2", "3", "4"}
where := badgerhold.Where("Id").In(t...) // compile error
```
Instead you need to copy your slice into another slice of empty interfaces:
```Go
t := []string{"1", "2", "3", "4"}
s := make([]interface{}, len(t))
for i, v := range t {
s[i] = v
}
where := badgerhold.Where("Id").In(s...)
```
You can use the helper function `badgerhold.Slice` which does exactly that.
```Go
t := []string{"1", "2", "3", "4"}
where := badgerhold.Where("Id").In(badgerhold.Slice(t)...)
```
### Unique Constraints
You can create a unique constraint on a given field by using the `badgerhold:"unique"` struct tag:
```Go
type User struct {
Name string
Email string `badgerhold:"unique"` // this field will be indexed with a unique constraint
}
```
The example above will only allow one record of type `User` to exist with a given `Email` field. Any insert, update or upsert that would violate that constraint will fail and return the `badgerhold.ErrUniqueExists` error.
### ForEach
When working with large datasets, you may not want to have to store the entire dataset in memory. It's be much more efficient to work with a single record at a time rather than grab all the records and loop through them, which is what cursors are used for in databases. In BadgerHold you can accomplish the same thing by calling ForEach:
```Go
err := store.ForEach(badgerhold.Where("Id").Gt(4), func(record *Item) error {
// do stuff with record
// if you return an error, then the query will stop iterating through records
return nil
})
```
### Aggregate Queries
Aggregate queries are queries that group results by a field. For example, lets say you had a collection of employees:
```Go
type Employee struct {
FirstName string
LastName string
Division string
Hired time.Time
}
```
And you wanted to find the most senior (first hired) employee in each division:
```Go
result, err := store.FindAggregate(&Employee{}, nil, "Division") //nil query matches against all records
```
This will return a slice of `Aggregate Result` from which you can extract your groups and find Min, Max, Avg, Count, etc.
```Go
for i := range result {
var division string
employee := &Employee{}
result[i].Group(&division)
result[i].Min("Hired", employee)
fmt.Printf("The most senior employee in the %s division is %s.\n",
division, employee.FirstName + " " + employee.LastName)
}
```
Aggregate queries become especially powerful when combined with the sub-querying capability of `MatchFunc`.
Many more examples of queries can be found in the [find_test.go](https://github.com/timshannon/badgerhold/blob/master/find_test.go) file in this repository.
## Comparing
Just like with Go, types must be the same in order to be compared with each other. You cannot compare an int to a int32. The built-in Go comparable types (ints, floats, strings, etc) will work as expected. Other types from the standard library can also be compared such as `time.Time`, `big.Rat`, `big.Int`, and `big.Float`. If there are other standard library types that I missed, let me know.
You can compare any custom type either by using the `MatchFunc` criteria, or by satisfying the `Comparer` interface with your type by adding the Compare method: `Compare(other interface{}) (int, error)`.
If a type doesn't have a predefined comparer, and doesn't satisfy the Comparer interface, then the types value is converted to a string and compared lexicographically.
## Behavior Changes
Since BadgerHold is a higher level interface than Badger DB, there are some added helpers. Instead of _Put_, you have the options of:
- _Insert_ - Fails if key already exists.
- _Update_ - Fails if key doesn't exist `ErrNotFound`.
- _Upsert_ - If key doesn't exist, it inserts the data, otherwise it updates the existing record.
When getting data instead of returning `nil` if a value doesn't exist, BadgerHold returns `badgerhold.ErrNotFound`, and similarly when deleting data, instead of silently continuing if a value isn't found to delete, BadgerHold returns `badgerhold.ErrNotFound`. The exception to this is when using query based functions such as `Find` (returns an empty slice), `DeleteMatching` and `UpdateMatching` where no error is returned.
## When should I use BadgerHold?
BadgerHold will be useful in the same scenarios where BadgerDB is useful, with the added benefit of being able to retire some of your data filtering code and possibly improved performance.
You can also use it instead of SQLite for many scenarios. BadgerHold's main benefit over SQLite is its simplicity when working with Go Types. There is no need for an ORM layer to translate records to types, simply put types in, and get types out. You also don't have to deal with database initialization. Usually with SQLite you'll need several scripts to create the database, create the tables you expect, and create any indexes. With BadgerHold you simply open a new file and put any type of data you want in it.
```Go
options := badgerhold.DefaultOptions
options.Dir = "data"
options.ValueDir = "data"
store, err := badgerhold.Open(options)
defer store.Close()
if err != nil {
// handle error
log.Fatal(err)
}
err = store.Insert("key", &Item{
Name: "Test Name",
Created: time.Now(),
})
```
That's it!
Badgerhold currently has over 80% coverage in unit tests, and it's backed by BadgerDB which is a very solid and well built piece of software, so I encourage you to give it a try.
If you end up using BadgerHold, I'd love to hear about it.