The following example illustrates pattern matching in locales. In non-default locales, LIKE and ILIKE perform locale-sensitive string comparisons, including some automatic normalization, using the same algorithm as the "=" operator on VARCHAR types.ĮSCAPE expressions evaluate to exactly one octet-or one UTF-8 character for non-default locales. ILIKE handles language-independent case-folding. In the default locale, LIKE and ILIKE handle UTF-8 character-at-a-time, locale-insensitive comparisons. LIKEB and ILIKEB predicates do byte-at-a-time ASCII comparisons.LIKE and ILIKE are stable for character strings, but immutable for binary strings, while LIKEB and ILIKEB are immutable for both cases.In particular, ILIKE works by setting S=2 (ignore case) in the current session locale. Exact behavior depends on collation parameters such as strength. LIKE operates on UTF-8 character strings.To check descriptions of the functions, refer to Verticas documentation. Below is the list of the enabled functions which could be used in transformations. The following differences pertain to LIKE and its variants: Not all Vertica functions are enabled in Data Integration. Vertica also supports several non-standard variants, notably ILIKE, which is equivalent to LIKE except it performs case-insensitive searches. In non-default locales, LIKE and ILIKE perform locale-sensitive string comparisons, including some automatic normalization, using the same algorithm as the '' operator on VARCHAR types.
The LIKE predicate is compliant with the SQL standard. In the default locale, LIKE and ILIKE handle UTF-8 character-at-a-time, locale-insensitive comparisons. If the data values to match end with an indeterminate amount of white space, append the wildcard character % to pattern.
LIKE does not ignore trailing white space characters. To match a sequence of characters anywhere within a string, the pattern must start and end with a percent sign. LIKE requires that the entire string expression match the pattern. WHERE dms.summary >= ''::TIMESTAMPTZ(3) - dms.Note: ESCAPE is not valid for the above symbols. filter condition forces an unnecessary type conversion, and the range predicate could be replaced by an equi filter bigger HASH table for join probably neededĭms.game_id = b.game_id - varchar(100) IN "dms", int in "b" ! - type conversion for joinĪND dms.SERVER = b.SERVER - varchar(30) in "dms", varchar(255) in "b" - type conversion for joinĪND dms.product = b.product - varchar(80) in "dms", varchar(100) in "b" - type conversion for join ON ( - 3 longer VARCHARs - with no commonly sorted projections. unless the optimizer manages a GROUPBY pushdown, grouping by game_id first, and then joining with games_info , b.game AS game - varchar(500) - candidate for a 4.25 bigger HASH table in memory than above SELECTĬount(DISTINCT dms.member_id) AS unique_players - varchar(80), candidate for a big HASH table in memory Let's look at your query and the physical design you're presented with.
Id like to receive emails from Micro Focus to stay up-to-date on products, services, news, events and promotions. 150 Cambridgepark Drive, Cambridge, MA 02140. TRANSACTION_ID | STATEMENT_ID | CPU_CYCLES_US | DURATION_MS | QUERY QC.TRANSACTION_ID = QR.TRANSACTION_ID AND Or try something like the following query to find Top10 long running queries in the last 6 hours: SELECT QC.TRANSACTION_ID,QC.STATEMENT_ID, QC.CPU_CYCLES_US, QC.DURATION_MS, LEFT(QR.REQUEST,30) QUERYįROM QUERY_CONSUMPTION QC, QUERY_REQUESTS QR