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Data Model and Queries

Indexes and Fields

Index and fields are the main data models of Pilosa. You can check the Pilosa documentation for more detail about the data model.

The schema.Index function is used to create an index instance. Note that this does not create an index on the server; the index object simply defines the schema.

schema := pilosa.NewSchema()
repository := schema.Index("repository")

You can pass options while creating index instances:

repository := schema.Index("repository", pilosa.OptIndexKeys(true))

Field definitions are created with a call to the Field function of an index:

stargazer := repository.Field("stargazer")

You can pass options to fields:

stargazer := repository.Field("stargazer", pilosa.OptFieldTypeTime(TimeQuantumYearMonthDay))

In case the schema already exists on the server, you can retrieve that instead of creating the schema:

client := pilosa.DefaultClient()
schema, err := client.Schema()
if err != nil {
    // act on the error
}
repository := schema.Index("repository")

Queries

Once you have indexes and field definitions, you can create queries for them. Some of the queries work on the columns; corresponding methods are attached to the index. Other queries work on rows with related methods attached to fields.

For instance, Row queries work on rows; use a Field object to create those queries:

rowQuery := stargazer.Row(1)  // corresponds to PQL: Row(stargazer=1)

Union queries work on columns; use the index to create them:

query := repository.Union(rowQuery1, rowQuery2)

In order to increase throughput, you may want to batch queries sent to the Pilosa server. The index.BatchQuery function is used for that purpose:

query := repository.BatchQuery(
    stargazer.Row(1),
    repository.Union(stargazer.Row(100), stargazer.Row(5)))

The recommended way of creating query instances is using dedicated functions attached to index and field objects, but sometimes it would be desirable to send raw queries to Pilosa. You can use index.RawQuery method for that. Note that query string is not validated before sending to the server:

query := repository.RawQuery("Row(stargazer=5)")

Raw queries are only sent to the coordinator node of a Pilosa cluster, so currently there's a possible performance hit using them instead of ORM functions attached to index or field instances.

This client supports range queries using bit sliced indexes (BSI). Read the Range Encoded Bitmaps blog post for more information about the BSI implementation of range encoding in Pilosa.

In order to use BSI range queries, an integer field should be created. The field should have its minimum and maximum set. Here's how you would do that:

index := schema.Index("animals")
captivity := index.Field("captivity", pilosa.OptFieldTypeInt(0, 956))

If the field with the necessary field already exists on the server, you don't need to create the field instance, client.SyncSchema(schema) would load that to schema. You can then add some data:

// Add the captivity values to the field.
data := []int{3, 392, 47, 956, 219, 14, 47, 504, 21, 0, 123, 318}
query := index.BatchQuery()
for i, x := range data {
	column := uint64(i + 1)
	query.Add(captivity.SetIntValue(column, x))
}
client.Query(query)

Let's write a range query:

// Query for all animals with more than 100 specimens
response, _ := client.Query(captivity.GT(100))
fmt.Println(response.Result().Row().Columns)

// Query for the total number of animals in captivity
response, _ = client.Query(captivity.Sum(nil))
fmt.Println(response.Result().Value())

If you pass a row query to Sum as a filter, then only the columns matching the filter will be considered in the Sum calculation:

// Let's run a few set queries first
client.Query(index.BatchQuery(
    field.Set(42, 1),
    field.Set(42, 6)))
// Query for the total number of animals in captivity where row 42 is set
response, _ = client.Query(captivity.Sum(field.Row(42)))
fmt.Println(response.Result().Value())

See the functions further below for the list of functions that can be used with a Field.

Please check Pilosa documentation for PQL details. Here is a list of methods corresponding to PQL calls:

Index:

  • Union(rows *PQLRowQuery...) *PQLRowQuery
  • Intersect(rows *PQLRowQuery...) *PQLRowQuery
  • Difference(rows *PQLRowQuery...) *PQLRowQuery
  • Xor(rows ...*PQLRowQuery) *PQLRowQuery
  • Not(row) *PQLRowQuery
  • Count(row *PQLRowQuery) *PQLBaseQuery
  • SetColumnAttrs(columnID uint64, attrs map[string]interface{}) *PQLBaseQuery
  • Options(row *PQLRowQuery, opts ...OptionsOption) *PQLBaseQuery

Field:

  • Row(rowID uint64) *PQLRowQuery
  • Set(rowID uint64, columnID uint64) *PQLBaseQuery
  • SetTimestamp(rowID uint64, columnID uint64, timestamp time.Time) *PQLBaseQuery
  • Clear(rowID uint64, columnID uint64) *PQLBaseQuery
  • TopN(n uint64) *PQLRowQuery
  • RowTopN(n uint64, row *PQLRowQuery) *PQLRowQuery
  • FilterFieldTopN(n uint64, row *PQLRowQuery, field string, values ...interface{}) *PQLRowQuery
  • Range(rowID uint64, start time.Time, end time.Time) *PQLRowQuery
  • RowRange(rowID uint64, start time.Time, end time.Time) *PQLRowQuery
  • SetRowAttrs(rowID uint64, attrs map[string]interface{}) *PQLBaseQuery
  • ClearRow(rowIDOrKey interface{}) *PQLBaseQuery
  • Store(row *PQLRowQuery, rowIDOrKey interface{}) *PQLBaseQuery
  • LT(n int) *PQLRowQuery
  • LTE(n int) *PQLRowQuery
  • GT(n int) *PQLRowQuery
  • GTE(n int) *PQLRowQuery
  • Between(a int, b int) *PQLRowQuery
  • Sum(row *PQLRowQuery) *PQLBaseQuery
  • Min(row *PQLRowQuery) *PQLBaseQuery
  • Max(row *PQLRowQuery) *PQLBaseQuery
  • SetIntValue(columnID uint64, value int) *PQLBaseQuery
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