Understanding types

Goal

By the end of this guide you should be able to identify the data types used in DataPrime and understand how to work with them effectively in queries, including type conversions and casting.

Why it matters

Every field in your logs, traces, or enriched data in DataPrime has a type. Whether it's a number, a timestamp, or a string of text, understanding types is critical for writing accurate queries, performing aggregations, and avoiding unexpected null results. If your logic doesn’t match the type, your query might look right—but silently fail.

Supported types in DataPrime

Here’s a breakdown of the main types you’ll encounter and use:

Type	Description	JSON Representation
string	UTF-8 encoded character sequences	"hello world"
number	Integer or float. Max is 2^53	123, 45.67
bool	Boolean value (true/false)	true
timestamp	Nanosecond-precision point in time	1609459200000000000
interval	Time duration (3h30m, 5s)	"3h30m" (or number in nanoseconds)
regexp	Regular expression used in filtering and matching	"/[a-z]+/"
null	1. Absence of a value 2. Non-computable expression 3. null value of the JSON field	null
array	Collection of primitive values of a single type	["a", "b", "c"]

What happens if types mismatch?

Types will raise an error prior to execution if there is a mismatch.

For example this query will show the error message below:

filter timestamp > '10'

When values are cast in an incompatible way, many functions and operations return null silently.

Type-specific usage rules

• Strings: Use for free-text searches and ~, contains(), etc.
• Numbers: Required for mathematical operations and aggregations like avg, sum.
• Timestamps: Combine with interval types for time math (e.g., now() - timestamp > 5m)
• Intervals: Use with time functions like diffTime, addTime.
• Arrays: Ensure consistent item types. Use functions like arrayContains, arrayLength.
• Booleans: Filter conditions and flags.
• Null: Any null used in a boolean condition is treated as false.

Null semantics

In DataPrime, null represents an undefined or missing value. It behaves similarly to SQL NULL, but with some important differences. A query will evaluate to null in the following cases:

• Field does not exist (i.e. the log has no foo field at all)

• Field exists but is explicitly null (i.e. {foo: null})

• Expression cannot be computed This includes cases where a value cannot be converted or an operation fails. Examples:

  • 'foo':bool → null
  • create x from 'foo' | choose parseInterval(x) as value → value: null
  • extract '{"value":"foo"}' into target using jsonobject() | choose target.value + 1 → null

Aggregations:

Nulls are preserved in aggregation results unless the function ignores them.

• aggregate collect(foo) includes nulls in the output array unless specified with the ignoreNulls flag. See documentation.
• aggreagte any_value(foo) returns a non-null value if one exists.

Joins:

Rows with null in the join key do not match.

Equality:

• A non-existent key value is equal to null. If foo does not exist as a key in your logs then:
  

  foo == null // true
  

• Two non-existent key values are equal to each other. If foo and bar are non-existent keys then:
  

  foo == bar // true
  

• Two objects are equal if and only if all fields match exactly, including nulls.
  

  {"a": 1, "b": null} == {"a": 1, "b": null}  // true  
  {"a": 1} == {"a": 1, "b": null}             // false  
  

• Arrays must match element by element, including nulls.
  

  {"k": [null, 1]} == {"k": [null, 1]}  // true  
  {"k": [null]} == {"k": []}            // false  
  {"k": [1, 2]} == {"k": [2, 1]}        // false  
  

Gotchas to watch for

• Type mismatches often fail silently (especially in filters).
• Some fields may appear numeric but are strings (e.g., status: "200").
• Intervals passed as strings must follow strict formatting (1d2h3s is valid, 3s1d is not).
• Comparing a field to null is true if and only if the field is missing or explicitly null.
• Joins skip rows where the join key is null.
• Objects cannot be converted into strings. The only exception to this is $d:string which is done to support full-text manipulation using DataPrime functions.