- Release Notes
- Introduction to CelerData Cloud Serverless
- Quick Start
- Sign up for CelerData Cloud Serverless
- A quick tour of the console
- Connect to CelerData Cloud Serverless
- Create an IAM integration
- Create and assign a warehouse
- Create an external catalog
- Load data from cloud storage
- Load data from Apache Kafka/Confluent Cloud
- Try your first query
- Invite new users
- Design data access control policy
- Warehouses
- Catalog, database, table, view, and MV
- Overview of database objects
- Catalog
- Table types
- Asynchronous materialized views
- Data Loading
- Data access control
- Networking and private connectivity
- Usage and Billing
- Organization and Account
- Integration
- Query Acceleration
- Reference
- AWS IAM policies
- Information Schema
- Overview
- be_bvars
- be_cloud_native_compactions
- be_compactions
- character_sets
- collations
- column_privileges
- columns
- engines
- events
- global_variables
- key_column_usage
- load_tracking_logs
- loads
- materialized_views
- partitions
- pipe_files
- pipes
- referential_constraints
- routines
- schema_privileges
- schemata
- session_variables
- statistics
- table_constraints
- table_privileges
- tables
- tables_config
- task_runs
- tasks
- triggers
- user_privileges
- views
- Data Types
- System Metadatabase
- Keywords
- SQL Statements
- Account Management
- Data Definition
- CREATE TABLE
- ALTER TABLE
- DROP CATALOG
- CREATE TABLE LIKE
- REFRESH EXTERNAL TABLE
- RESTORE
- SET CATALOG
- DROP TABLE
- RECOVER
- USE
- CREATE MATERIALIZED VIEW
- DROP DATABASE
- ALTER MATERIALIZED VIEW
- DROP REPOSITORY
- CANCEL RESTORE
- DROP INDEX
- DROP MATERIALIZED VIEW
- CREATE DATABASE
- CREATE TABLE AS SELECT
- BACKUP
- CANCEL BACKUP
- CREATE REPOSITORY
- CREATE INDEX
- Data Manipulation
- INSERT
- SHOW CREATE DATABASE
- SHOW BACKUP
- SHOW ALTER MATERIALIZED VIEW
- SHOW CATALOGS
- SHOW CREATE MATERIALIZED VIEW
- SELECT
- SHOW ALTER
- SHOW MATERIALIZED VIEW
- RESUME ROUTINE LOAD
- ALTER ROUTINE LOAD
- SHOW TABLES
- STREAM LOAD
- SHOW PARTITIONS
- CANCEL REFRESH MATERIALIZED VIEW
- SHOW CREATE CATALOG
- SHOW ROUTINE LOAD TASK
- SHOW RESTORE
- CREATE ROUTINE LOAD
- STOP ROUTINE LOAD
- SHOW DATABASES
- BROKER LOAD
- SHOW ROUTINE LOAD
- PAUSE ROUTINE LOAD
- SHOW SNAPSHOT
- SHOW CREATE TABLE
- CANCEL LOAD
- REFRESH MATERIALIZED VIEW
- SHOW REPOSITORIES
- SHOW LOAD
- Administration
- DESCRIBE
- SQL Functions
- Function List
- String Functions
- CONCAT
- HEX
- LOWER
- SPLIT
- LPAD
- SUBSTRING
- PARSE_URL
- INSTR
- REPEAT
- LCASE
- REPLACE
- HEX_DECODE_BINARY
- RPAD
- SPLIT_PART
- STRCMP
- SPACE
- CHARACTER_LENGTH
- URL_ENCODE
- APPEND_TAILING_CHAR_IF_ABSENT
- LTRIM
- HEX_DECODE_STRING
- URL_DECODE
- LEFT
- STARTS_WITH
- CONCAT
- GROUP_CONCAT
- STR_TO_MAP
- STRLEFT
- STRRIGHT
- MONEY_FORMAT
- RIGHT
- SUBSTRING_INDEX
- UCASE
- TRIM
- FIND_IN_SET
- RTRIM
- ASCII
- UPPER
- REVERSE
- LENGTH
- UNHEX
- ENDS_WITH
- CHAR_LENGTH
- NULL_OR_EMPTY
- LOCATE
- CHAR
- Predicate Functions
- Map Functions
- Binary Functions
- Geospatial Functions
- Lambda Expression
- Utility Functions
- Bitmap Functions
- BITMAP_SUBSET_LIMIT
- TO_BITMAP
- BITMAP_AGG
- BITMAP_FROM_STRING
- BITMAP_OR
- BITMAP_REMOVE
- BITMAP_AND
- BITMAP_TO_BASE64
- BITMAP_MIN
- BITMAP_CONTAINS
- SUB_BITMAP
- BITMAP_UNION
- BITMAP_COUNT
- BITMAP_UNION_INT
- BITMAP_XOR
- BITMAP_UNION_COUNT
- BITMAP_HAS_ANY
- BITMAP_INTERSECT
- BITMAP_AND_NOT
- BITMAP_TO_STRING
- BITMAP_HASH
- INTERSECT_COUNT
- BITMAP_EMPTY
- BITMAP_MAX
- BASE64_TO_ARRAY
- BITMAP_TO_ARRAY
- Struct Functions
- Aggregate Functions
- RETENTION
- MI
- MULTI_DISTINCT_SUM
- WINDOW_FUNNEL
- STDDEV_SAMP
- GROUPING_ID
- HLL_HASH
- AVG
- HLL_UNION_AGG
- COUNT
- BITMAP
- HLL_EMPTY
- SUM
- MAX_BY
- PERCENTILE_CONT
- COVAR_POP
- PERCENTILE_APPROX
- HLL_RAW_AGG
- STDDEV
- CORR
- COVAR_SAMP
- MIN_BY
- MAX
- VAR_SAMP
- STD
- HLL_UNION
- APPROX_COUNT_DISTINCT
- MULTI_DISTINCT_COUNT
- VARIANCE
- ANY_VALUE
- COUNT_IF
- GROUPING
- PERCENTILE_DISC
- Array Functions
- ARRAY_CUM_SUM
- ARRAY_MAX
- ARRAY_LENGTH
- ARRAY_REMOVE
- UNNEST
- ARRAY_SLICE
- ALL_MATCH
- ARRAY_CONCAT
- ARRAY_SORT
- ARRAY_POSITION
- ARRAY_DIFFERENCE
- ARRAY_CONTAINS
- ARRAY_JOIN
- ARRAY_INTERSECT
- CARDINALITY
- ARRAY_CONTAINS_ALL
- ARRAYS_OVERLAP
- ARRAY_MIN
- ARRAY_MAP
- ELEMENT_AT
- ARRAY_APPEND
- ARRAY_SORTBY
- ARRAY_TO_BITMAP
- ARRAY_GENERATE
- ARRAY_AVG
- ARRAY_FILTER
- ANY_MATCH
- REVERSE
- ARRAY_AGG
- ARRAY_DISTINCT
- ARRAY_SUM
- Condition Functions
- Math Functions
- Date and Time Functions
- DAYNAME
- MINUTE
- FROM_UNIXTIME
- HOUR
- MONTHNAME
- MONTHS_ADD
- ADD_MONTHS
- DATE_SUB
- PREVIOUS_DAY
- TO_TERA_DATA
- MINUTES_SUB
- WEEKS_ADD
- HOURS_DIFF
- UNIX_TIMESTAMP
- DAY
- DATE_SLICE
- DATE
- CURTIME
- SECONDS_SUB
- MONTH
- WEEK
- TO_DATE
- TIMEDIFF
- MONTHS_DIFF
- STR_TO_JODATIME
- WEEK_ISO
- MICROSECONDS_SUB
- TIME_SLICE
- MAKEDATE
- DATE_TRUNC
- JODATIME
- DAYOFWEEK
- YEARS_SUB
- TIMESTAMP_ADD
- HOURS_SUB
- STR2DATE
- TIMESTAMP
- FROM_DAYS
- WEEK_OF_YEAR
- YEAR
- TIMESTAMP_DIFF
- TO_TERA_TIMESTAMP
- DAYOFMONTH
- DAYOFYEAR
- DATE_FORMAT
- MONTHS_SUB
- NEXT_DAY
- MINUTES_DIFF
- DATA_ADD
- MINUTES_ADD
- CURDATE
- DAY_OF_WEEK_ISO
- CURRENt_TIMESTAMP
- STR_TO_DATE
- LAST_DAY
- WEEKS_SUB
- TO_DAYS
- DATEDIFF
- NOW
- TO_ISO8601
- TIME_TO_SEC
- QUARTER
- SECONDS_DIFF
- UTC_TIMESTAMP
- DATA_DIFF
- SECONDS_ADD
- ADDDATE
- WEEKSDIFF
- CONVERT_TZ
- MICROSECONDS_ADD
- SECOND
- YEARS_DIFF
- YEARS_ADD
- HOURS_ADD
- DAYS_SUB
- DAYS_DIFF
- Cryptographic Functions
- Percentile Functions
- Bit Functions
- JSON Functions
- Hash Functions
- Scalar Functions
- Table Functions
Best practices of warehouses
With the multi-warehouse feature, you can flexibly manage the compute resources in your CelerData cloud account. By allocating different tasks to distinct warehouses, you can achieve physical isolation of computing resources.
Multi-Warehouse's capabilities shine in scenarios like:
Isolation of query and write operation
Typically, there are two types of tasks running in CelerData Cloud Serverless: query and write operations. Both types of tasks consume resources such as disk I/O, bandwidth, CPU, memory, and more. These two task types contend for resources. For example, during periods of high query load, resource constraints in disk I/O can result in decreased write throughput. In cases of insufficient memory, it may even lead to import failures. To ensure that these two task types do not interfere with each other, you need hard isolation of compute resources between them.
Online-offline hybrid analysis
Multi-Warehouse offers superior resource isolation and data sharing capabilities for businesses combining online and offline operations. With unified data storage, this approach streamlines storage costs and simplifies data management.
Ad hoc query
You can flexibly scale up or down the warehouse for ad hoc query requests.
Offline tasks
In cases where an offline task is time-sensitive, Multi-Warehouse enables rapid resource allocation adjustments, ensuring urgent tasks are accomplished within time constraints.