Gocassa is a high-level library on top of gocql.
Current version: v1.4.0
Compared to gocql it provides query building, adds data binding, and provides easy-to-use "recipe" tables for common query use-cases. Unlike cqlc, it does not use code generation.
For docs, see: https://godoc.org/github.com/gocassa/gocassa
Below is a basic example showing how to connect to a Cassandra cluster and setup a simple table. For more advanced examples see the "Table Types" section below.
package main
import(
"fmt"
"time"
"github.com/gocassa/gocassa"
)
type Sale struct {
Id string
CustomerId string
SellerId string
Price int
Created time.Time
}
func main() {
keySpace, err := gocassa.ConnectToKeySpace("test", []string{"127.0.0.1"}, "", "")
if err != nil {
panic(err)
}
salesTable := keySpace.Table("sale", &Sale{}, gocassa.Keys{
PartitionKeys: []string{"Id"},
})
err = salesTable.Set(Sale{
Id: "sale-1",
CustomerId: "customer-1",
SellerId: "seller-1",
Price: 42,
Created: time.Now(),
}).Run()
if err != nil {
panic(err)
}
result := Sale{}
if err := salesTable.Where(gocassa.Eq("Id", "sale-1")).ReadOne(&result).Run(); err != nil {
panic(err)
}
fmt.Println(result)
}You can pass additional options to a gocassa Op to further configure your queries, for example the following query orders the results by the field "Name" in descending order and limits the results to a total of 100.
err := salesTable.List("seller-1", nil, 0, &results).WithOptions(gocassa.Options{
ClusteringOrder: []ClusteringOrderColumn{
{DESC, "Name"},
},
Limit: 100,
}).Run()Gocassa provides multiple table types with their own unique interfaces:
- a raw CQL table called simply
Table- this lets you do pretty much any query imaginable - and a number of single purpose 'recipe' tables (
Map,Multimap,TimeSeries,MultiTimeSeries,MultiMapMultiKey), which aims to help the user by having a simplified interface tailored to a given common query use case
salesTable := keySpace.Table("sale", &Sale{}, gocassa.Keys{
PartitionKeys: []string{"Id"},
})
result := Sale{}
err := salesTable.Where(gocassa.Eq("Id", "sale-1")).ReadOne(&result).Run()MapTable provides only very simple CRUD functionality:
// …
salesTable := keySpace.MapTable("sale", "Id", &Sale{})
result := Sale{}
salesTable.Read("sale-1", &result).Run()
}Read, Set, Update, and Delete all happen by "Id".
MultimapTable can list rows filtered by equality of a single field (eg. list sales based on their sellerId):
salesTable := keySpace.MultimapTable("sale", "SellerId", "Id", &Sale{})
// …
results := []Sale{}
err := salesTable.List("seller-1", nil, 0, &results).Run()For examples on how to do pagination or Update with this table, refer to the example (linked under code snippet).
TimeSeriesTable provides an interface to list rows within a time interval:
salesTable := keySpace.TimeSeriesTable("sale", "Created", "Id", &Sale{}, 24 * time.Hour)
//...
results := []Sale{}
err := salesTable.List(yesterdayTime, todayTime, &results).Run()MultiTimeSeriesTable is like a cross between MultimapTable and TimeSeriesTable. It can list rows within a time interval, and filtered by equality of a single field. The following lists sales in a time interval, by a certain seller:
salesTable := keySpace.MultiTimeSeriesTable("sale", "SellerId", "Created", "Id", &Sale{}, 24 * time.Hour)
//...
results := []Sale{}
err := salesTable.List("seller-1", yesterdayTime, todayTime, &results).Run()MultiMapMultiKeyTable can perform CRUD operations on rows filtered by equality of multiple fields (eg. read a sale based on their city , sellerId and Id of the sale):
salePartitionKeys := []Sale{"City"}
saleClusteringKeys := []Sale{"SellerId","Id"}
salesTable := keySpace.MultimapMultiKeyTable("sale", salePartitionKeys, saleClusteringKeys, Sale{})
// …
result := Sale{}
saleFieldCity = salePartitionKeys[0]
saleFieldSellerId = saleClusteringKeys[0]
saleFieldSaleId = saleClusteringKeys[1]
field := make(map[string]interface{})
id := make(map[string]interface{})
field[saleFieldCity] = "London"
id[saleFieldSellerId] = "141-dasf1-124"
id[saleFieldSaleId] = "512hha232"
err := salesTable.Read(field, id , &result).Run()When setting structs in gocassa the library first converts your value to a map. Each exported field is added to the map unless
- the field's tag is "-", or
- the field is empty and its tag specifies the "omitempty" option
Each fields default name in the map is the field name but can be specified in the struct field's tag value. The "cql" key in the struct field's tag value is the key name, followed by an optional comma and options. Examples:
// Field is ignored by this package.
Field int `cql:"-"`
// Field appears as key "myName".
Field int `cql:"myName"`
// Field appears as key "myName" and
// the field is omitted from the object if its value is empty,
// as defined above.
Field int `cql:"myName,omitempty"`
// Field appears as key "Field" (the default), but
// the field is skipped if empty.
// Note the leading comma.
Field int `cql:",omitempty"`
// All fields in the EmbeddedType are squashed into the parent type.
EmbeddedType `cql:",squash"`When encoding maps with non-string keys the key values are automatically converted to strings where possible, however it is recommended that you use strings where possible (for example map[string]T).
In case you get the following error:
Column family names shouldn't be more than 48 characters long (got "somelongishtablename_multitimeseries_start_id_24h0m0s")
You can use the TableName options to override the default internal ones:
tbl = tbl.WithOptions(Options{TableName: "somelongishtablename_mts_start_id_24h0m0s"})
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent