- StarRocks
- Introduction to StarRocks
- Quick Start
- Deployment
- Deployment overview
- Prepare
- Deploy
- Deploy classic StarRocks
- Deploy and use shared-data StarRocks
- Manage
- Table Design
- Data Loading
- Concepts
- Overview of data loading
- Load data from a local file system or a streaming data source using HTTP PUT
- Load data from HDFS or cloud storage
- Continuously load data from Apache Kafka®
- Bulk load using Apache Sparkâ„¢
- Load data using INSERT
- Load data using Stream Load transaction interface
- Realtime synchronization from MySQL
- Continuously load data from Apache Flink®
- Change data through loading
- Transform data at loading
- Data Unloading
- Query Data Sources
- Query Acceleration
- Gather CBO statistics
- Synchronous materialized view
- Asynchronous materialized view
- Colocate Join
- Lateral Join
- Query Cache
- Index
- Computing the Number of Distinct Values
- Sorted streaming aggregate
- Administration
- Management
- Data recovery
- User Privilege and Authentication
- Performance Tuning
- Reference
- SQL Reference
- User Account Management
- Cluster Management
- ADD SQLBLACKLIST
- ADMIN CANCEL REPAIR TABLE
- ADMIN CHECK TABLET
- ADMIN REPAIR TABLE
- ADMIN SET CONFIG
- ADMIN SET REPLICA STATUS
- ADMIN SHOW CONFIG
- ADMIN SHOW REPLICA DISTRIBUTION
- ADMIN SHOW REPLICA STATUS
- ALTER RESOURCE GROUP
- ALTER SYSTEM
- CANCEL DECOMMISSION
- CREATE FILE
- CREATE RESOURCE GROUP
- DELETE SQLBLACKLIST
- DROP FILE
- DROP RESOURCE GROUP
- EXPLAIN
- INSTALL PLUGIN
- KILL
- SET
- SHOW BACKENDS
- SHOW BROKER
- SHOW COMPUTE NODES
- SHOW FILE
- SHOW FRONTENDS
- SHOW FULL COLUMNS
- SHOW INDEX
- SHOW PLUGINS
- SHOW PROC
- SHOW PROCESSLIST
- SHOW RESOURCE GROUP
- SHOW SQLBLACKLIST
- SHOW TABLE STATUS
- SHOW VARIABLES
- UNINSTALL PLUGIN
- DDL
- ALTER DATABASE
- ALTER MATERIALIZED VIEW
- ALTER TABLE
- ALTER VIEW
- ALTER RESOURCE
- ANALYZE TABLE
- BACKUP
- CANCEL ALTER TABLE
- CANCEL BACKUP
- CANCEL RESTORE
- CREATE ANALYZE
- CREATE DATABASE
- CREATE EXTERNAL CATALOG
- CREATE INDEX
- CREATE MATERIALIZED VIEW
- CREATE REPOSITORY
- CREATE RESOURCE
- CREATE TABLE AS SELECT
- CREATE TABLE LIKE
- CREATE TABLE
- CREATE VIEW
- CREATE FUNCTION
- DROP ANALYZE
- DROP STATS
- DROP CATALOG
- DROP DATABASE
- DROP INDEX
- DROP MATERIALIZED VIEW
- DROP REPOSITORY
- DROP RESOURCE
- DROP TABLE
- DROP VIEW
- DROP FUNCTION
- HLL
- KILL ANALYZE
- RECOVER
- REFRESH EXTERNAL TABLE
- RESTORE
- SET CATALOG
- SHOW ANALYZE JOB
- SHOW ANALYZE STATUS
- SHOW META
- SHOW RESOURCES
- SHOW FUNCTION
- TRUNCATE TABLE
- USE
- DML
- ALTER LOAD
- ALTER ROUTINE LOAD
- BROKER LOAD
- CANCEL LOAD
- CANCEL EXPORT
- CANCEL REFRESH MATERIALIZED VIEW
- CREATE ROUTINE LOAD
- DELETE
- EXPORT
- GROUP BY
- INSERT
- PAUSE ROUTINE LOAD
- REFRESH MATERIALIZED VIEW
- RESUME ROUTINE LOAD
- SELECT
- SHOW ALTER TABLE
- SHOW ALTER MATERIALIZED VIEW
- SHOW BACKUP
- SHOW CATALOGS
- SHOW CREATE CATALOG
- SHOW CREATE MATERIALIZED VIEW
- SHOW CREATE TABLE
- SHOW CREATE VIEW
- SHOW DATA
- SHOW DATABASES
- SHOW DELETE
- SHOW DYNAMIC PARTITION TABLES
- SHOW EXPORT
- SHOW LOAD
- SHOW MATERIALIZED VIEWS
- SHOW PARTITIONS
- SHOW PROPERTY
- SHOW REPOSITORIES
- SHOW RESTORE
- SHOW ROUTINE LOAD
- SHOW ROUTINE LOAD TASK
- SHOW SNAPSHOT
- SHOW TABLES
- SHOW TABLET
- SHOW TRANSACTION
- SPARK LOAD
- STOP ROUTINE LOAD
- STREAM LOAD
- SUBMIT TASK
- UPDATE
- Auxiliary Commands
- Data Types
- Keywords
- AUTO_INCREMENT
- Function Reference
- Java UDFs
- Window functions
- Lambda expression
- Aggregate Functions
- array_agg
- avg
- any_value
- approx_count_distinct
- bitmap
- bitmap_agg
- count
- grouping
- grouping_id
- hll_empty
- hll_hash
- hll_raw_agg
- hll_union
- hll_union_agg
- max
- max_by
- min
- multi_distinct_sum
- multi_distinct_count
- percentile_approx
- percentile_cont
- percentile_disc
- retention
- stddev
- stddev_samp
- sum
- variance, variance_pop, var_pop
- var_samp
- window_funnel
- Array Functions
- array_agg
- array_append
- array_avg
- array_concat
- array_contains
- array_contains_all
- array_cum_sum
- array_difference
- array_distinct
- array_filter
- array_intersect
- array_join
- array_length
- array_map
- array_max
- array_min
- array_position
- array_remove
- array_slice
- array_sort
- array_sortby
- array_sum
- arrays_overlap
- array_to_bitmap
- cardinality
- element_at
- reverse
- unnest
- Bit Functions
- Bitmap Functions
- base64_to_bitmap
- bitmap_agg
- bitmap_and
- bitmap_andnot
- bitmap_contains
- bitmap_count
- bitmap_from_string
- bitmap_empty
- bitmap_has_any
- bitmap_hash
- bitmap_intersect
- bitmap_max
- bitmap_min
- bitmap_or
- bitmap_remove
- bitmap_to_array
- bitmap_to_base64
- bitmap_to_string
- bitmap_union
- bitmap_union_count
- bitmap_union_int
- bitmap_xor
- intersect_count
- sub_bitmap
- to_bitmap
- JSON Functions
- Overview of JSON functions and operators
- JSON operators
- JSON constructor functions
- JSON query and processing functions
- Map Functions
- Binary Functions
- Conditional Functions
- Cryptographic Functions
- Date Functions
- add_months
- adddate
- convert_tz
- current_date
- current_time
- current_timestamp
- date
- date_add
- date_format
- date_slice
- date_sub, subdate
- date_trunc
- datediff
- day
- dayname
- dayofmonth
- dayofweek
- dayofyear
- days_add
- days_diff
- days_sub
- from_days
- from_unixtime
- hour
- hours_add
- hours_diff
- hours_sub
- microseconds_add
- microseconds_sub
- minute
- minutes_add
- minutes_diff
- minutes_sub
- month
- monthname
- months_add
- months_diff
- months_sub
- now
- quarter
- second
- seconds_add
- seconds_diff
- seconds_sub
- str_to_date
- str2date
- time_slice
- time_to_sec
- timediff
- timestamp
- timestampadd
- timestampdiff
- to_date
- to_days
- unix_timestamp
- utc_timestamp
- week
- week_iso
- weekofyear
- weeks_add
- weeks_diff
- weeks_sub
- year
- years_add
- years_diff
- years_sub
- Geographic Functions
- Math Functions
- String Functions
- append_trailing_char_if_absent
- ascii
- char
- char_length
- character_length
- concat
- concat_ws
- ends_with
- find_in_set
- group_concat
- hex
- hex_decode_binary
- hex_decode_string
- instr
- lcase
- left
- length
- locate
- lower
- lpad
- ltrim
- money_format
- null_or_empty
- parse_url
- repeat
- replace
- reverse
- right
- rpad
- rtrim
- space
- split
- split_part
- starts_with
- strleft
- strright
- substring
- trim
- ucase
- unhex
- upper
- Pattern Matching Functions
- Percentile Functions
- Scalar Functions
- Utility Functions
- cast function
- hash function
- System variables
- User-defined variables
- Error code
- System limits
- SQL Reference
- FAQ
- Benchmark
- Developers
- Contribute to StarRocks
- Code Style Guides
- Use the debuginfo file for debugging
- Development Environment
- Trace Tools
- Integration
Integrate Jupyter with StarRocks
This guide describes how to integrate your StarRocks cluster with Jupyter, the latest web-based interactive development environment for notebooks, code, and data. All of this is made possible via Jupysql which allows you to run SQL and plot large datasets in Jupyter via a %sql, %%sql, and %sqlplot magics.
We will use Jupysql on top of Jupyter to run queries on top of StarRocks. Once the data is loaded into the cluster, we'll query & visualize it via SQL plotting.
Prerequisites
Before getting started, you must have the following software installed locally:
- Jupysql:
pip install jupysql
- Jupyterlab:
pip install jupyterlab
- SKlearn Evaluation:
pip install sklearn-evaluation
- Python
- pymysql:
pip install pymysql
NOTE
Once we have our requirements we can open jupyter lab simply by calling
jupyterlab
- this will open the notebook interface. If already running in a notebook, you can simply run the cell bellow to get the dependencies.
# Install required packages
%pip install --quiet jupysql sklearn-evaluation pymysql
Note: you may need to restart the kernel to use updated packages.
import pandas as pd
from sklearn_evaluation import plot
# Import jupysql Jupyter extension to create SQL cells
%load_ext sql
%config SqlMagic.autocommit=False
You'd need to make sure your StarRocks instance is up and reachable for the next stages.
Note: You will need to adjust the connection string according to the instance type you're trying to connect to (url, user, password). In the example below we've used a local instance. To learn more about it, check out Introduction to StarRocks.
Connecting to StarRocks via Jupysql
In this example, we've used a docker instance and that's reflecting the data in the connection string. We'll use root to connect to our local StarRocks instance. We can then create a database and check we can actually read and write data into it.
%sql mysql+pymysql://root:@localhost:9030
And now we create and use that jupysql database:
%sql CREATE DATABASE jupysql;
%sql USE jupysql;
We can then create the table:
%%sql
CREATE TABLE tbl(c1 int, c2 int) distributed by hash(c1) properties ("replication_num" = "1");
INSERT INTO tbl VALUES (1, 10), (2, 20), (3, 30);
SELECT * FROM tbl;
Saving and loading queries
Now once we've created a database, we can write some sample data into it and query it. JupySQL allows you to break queries into multiple cells, simplifying the process of building large queries. It allows us to write complex queries, save them and execute them when needed, in a similar manner to CTEs in SQL.
# This is pending for the next Jupysql release
%%sql --save initialize-table --no-execute
CREATE TABLE tbl(c1 int, c2 int) distributed by hash(c1) properties ("replication_num" = "1");
INSERT INTO tbl VALUES (1, 1), (2, 2), (3, 3);
SELECT * FROM tbl;
Note: --save
stores the query, not the data.
Note that we are using --with;
this will retrieve previously saved queries, and prepend them (using CTEs), then, we save the query in track_fav.
Plotting directly on StarRocks
Jupysql comes with a few plots by default, allowing the user to visualize the data directly in SQL. We can use a bar plot to visualize the data in our newly created table:
top_artist = %sql SELECT * FROM tbl
top_artist.bar()
We can note we now have a new bar plot without any extra code.
We hope you enjoyed this guide, now you can run SQL directly from your notebook via Jupysql (by ploomber). This adds lots of possibilities around StarRocks for Data scientists and Engineers. In case you got stuck or need any support, please reach out to us via slack.