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Regular Expression Matching and Capture Groups in Rel


The Rel library has included basic regular expression (opens in a new tab) support for a while. For example, regex_match (opens in a new tab) tests whether a string matches a regular expression.

regex_match("^.*@.*$", "")

The relation string_replace also supports regular expressions, for example string_trim is defined using a regular expression:

def string_trim[s] = string_replace[
    s, regex_compile["^\\s+|\\s+$"], ""]

Until now, it was not yet possible to extract matching substrings using regular expressions. We are excited to announce that we have now added support for this.

The relation regex_match_all finds all substrings in a string that match the regular expression. The relation includes the matched substring as well as corresponding offsets.

// read query
def output = regex_match_all["(cat|dog)s?", "cats are not dogs"]


We also introduce the capture_group_by_index relation to capture a substring that matches groups in a regular expression. This relation searches for matches in an input string starting from a given offset.

Each group in the regular expression is automatically given a unique number starting with 1.

// read query
def email = ""
def pattern = "^(.*)@(.*).com$"
def output = email, capture_group_by_index[pattern, email, 1]


Along with numerical index, Rel supports regular expressions with named capture groups. The capture_group_by_name relation includes the captured substring for the corresponding group name.

// read query
def my_string = "Meeting is at 11:45 AM"
def pattern = "(?<hour>\\d+):(?<minute>\\d+)"
def output = capture_group_by_name[pattern, my_string, 1]


The regular expression capabilities are implemented using the foreign function interfaces, but these relations are designed to be used as any relation. For example, when a specific capture group is needed, it can be specified upfront, as illustrated in this example:

// read query
def my_group = capture_group_by_name[
    "^.*@(?<domain>.*)\\.com$", "", 1]
def output = my_group["domain"]


With the new regular expression features we expect to cover more of the common data engineering use-cases. We’re excited to learn about how you are using Rel — please let us know about any future features you’d like to see.

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