Support\Big Data\BigData_CheatSheet.md
-- list files in HDFS
hadoop fs -ls <PATH_OF_DIRECTORY>
-- creating directories in HDFS
hadoop fs -mkdir <PATH_OF_DIRECTORY>
-- creating multiple level directories in HDFS
hadoop fs -mkdir -p <DIR1/DIR2/DIR3>
-- copy files from local file system into HDFS
hadoop fs -copyFromLocal <LOCAL_PATH> <HDFS_PATH>
hadoop fs -put <LOCAL_PATH> <HDFS_PATH>
-- copy file from HDFS to local file system
hadoop fs -copyToLocal <HDFS_PATH> <LOCAL_PATH>
hadoop fs -get <HDFS_PATH> <LOCAL_PATH>
-- copy or move file from one HDFS location to another HDFS location
hadoop fs -cp <SR_HDFS_PATH> <TARGET_HDFS_PATH>
hadoop fs -mv <SR_HDFS_PATH> <TARGET_HDFS_PATH>
-- delete file without move to trash
hadoop fs -rm -r -f -skipTrash <HDFS_PATH>
-- get file system usage
hadoop fs -du -s -h <HDFS_PATH>
hadoop fs -df -h <HDFS_PATH>
-- get metadata of files in HDFS
hdfs fsck <HDFS_PATH> -files -blocks -locations- interchangeable constructs
- hive is case sensitive
- secmicolon to terminate statements
-
Numeric
- TINYINT, SMALLINT, INT, BIGINT
- FLOAT
- DOUBLE
- DECIMAL
-
Date/Time
- TIMESTAMP
- Strings must be in format "YYYY-MM-DD HH:MM:SS.fffffffff"
- Interger types as UNIX timestamp in seconds from UNIX epoch (1-JAN-1970 00:00:00)
- Floating point types same as Integer with decimal precision
- DATE
- TIMESTAMP
-
String Data Types
- STRING
- VARCHAR
- CHAR
-
Misc.
- BOOLEAN
- BINARY
- BINARY is an array of Bytes and similar to VARBINARY in many RDBMSs. BINARY columns are stored within the record, not separately like BLOBs . We can include arbitrary bytes in BINARY column and these bytes are not parsed by Hive as numbers or strings.
| Type | Syntax |
|---|---|
| Arrays | ARRAY<data_type> |
| Maps | MAP<primitive_type,data_type> |
| Struc | STRUCT<col_name:data_type [COMMENT col_comment], ...> |
| Union Type | UNIONTYPE<data_type, data_type, ...> |
-
Default delimeters for the fields in collection data types
Delimiter Code Description \n \n Record or row delimiter ^A (Ctrl + A) Field delimiter Field delimiter ^B (Ctrl + B) \002 Element delimiter in ARRAY and STRUCTs ^C (Ctrl + C) \003 Delimits key/value pairs in a MAP -
Example
CREATE TABLE movies (
movie_name string,
participants ARRAY<string>,
release_dates MAP<string, timestamp>,
studio_addr STRUCT<state:string, city:string, zip:string>,
complex_participants MAP<string, STRUCT<address:string, attributes MAP<string, string>>>,
misc UNIONTYPE <int, string, ARRAY<double>>
);
SELECT
movie_name,
participants[0],
release_dates["USA"],
studio_addr.zip,
complex_participants["Leonardo DiCaprio"].attributes["fav_color"],
misc
FROM
movies;-
sample output:
x x x x x "Inception" 2010-07-16 00:00:00 91505 "Dark Green" {0:800} "Planes" 2013-08-09 00:00:00 91505 "Green" {2:[1.0,2.3,5.6]}
CREATE TABLE user (
name STRING,
id BIGINT,
isFTE BOOLEAN,
role VARCHAR(64),
salary DECIMAL(8,2),
phones ARRAY<INT>,
deductions MAP<CHAR, FLOAT>,
address STRUCT<street:STRING, city:STRING, state:STRING, zip:INT>,
others UNIONTYPE<FLOAT,BOOLEAN,STRING>,
misc BINARY
)
ROW FORMAT DELIMITED
FIELDS TERMINATED BY '\001'
COLLECTION ITEMS TERMINATED BY '\002'
MAP KEYS TERMINATED BY '\003'
LINES TERMINATED BY '\n';- Create database
CREATE (DATABASE|SCHEMA) [IF NOT EXISTS] database_name
[COMMENT some_comment]
[LOCATION hdfs_path]
[WITH DBPROPERTIES(property_name=property_value, ...)];- Use database
USE db_name;- Drop database
DROP (DATABASE|SCHEMA) [IF EXISTS] database_name;- Show database
SHOW DATABASES;CREATE [EXTERNAL] TABLE [IF NOT EXISTS] [db_name.]table_name
[(col_name data type [COMMENT col_comment], ...)]
[PARTITIONED BY (col_name data type [COMMENT col_comment], ...)]
[ROW FORMATTED row_format]
[STORED AS file_format]
[LOCATION hdfs_path]
[TBLPROPERTIES (property_name=property_value, ...)];
-- External Example 1:
CREATE EXTERNAL TABLE users (
user_id INT,
age INT,
gender STRING,
occupation STRING,
zip_code STRING
)
ROW FORMAT DELIMITED FIELDS TERMINATED BY '|'
STORED AS TEXTFILE
LOCATION '/pluralsight/userinfo'
;
-- For external table, you still can directly insert data into it via "INSERT INTO TABLE ..." syntax.
-- This is same as managed table: You can use "INSERT INTO TABLE ..." to insert data to both managed table and external table
-- Create table **AS** Example 1
-- New table will have data
CREATE TABLE
occupation_count STORED AS RCFile
AS
SELECT
COUNT(*), occupation
FROM users
GROUP BY
occupation;
-- Create table **LIKE** Example 1:
-- It will create a new table with no records (empty)
-- It is similar to Oracle
-- "CREATE TABLE y AS SELECT *FROM y WHERE 1=0"
CREATE TABLE
occupation2
LIKE occupation_count;
- there is no "IF NOT EXISTS" as part of truncate syntax
- can only truncate managed table
- can't truncate external table
TRUNCATE TABLE table_name [PARTITION partition_spec];DROP TABLE IF EXISTS [db_name.]table_name;SHOW TABLES [IN database_name] ['identifier_with_wildcards'];DESCRIBE [EXTENDED|FORMATTED] [db_name.]table_name;SHOW CREATE TABLE [db_name.]table_name;EXPLAIN query;- Load data from local file system
- Load data from HDFS location (files in the source location will be deleted). One should have write permissions on HDFS location.
- By default data will be appended, to overwrite we can use OVERWRITE clause.
-- from local file system
LOAD DATA LOCAL INPATH '/data/retail_db/orders' INTO TABLE orders;
-- from HDFS location
dfs -mkdir /user/training/retail_db;
dfs -put /data/retail_db/orders /user/training/retail_db/.;
LOAD DATA INPATH '/user/training/retail_db/orders' INTO TABLE orders;
-- Overwrite existing data
LOAD DATA LOCAL INPATH '/data/retail_db/orders' OVERWRITE INTO TABLE orders;-
Advantages
Partitioning is used for distributing execution load horizontally. As the data is stored as slices/parts, query response time is faster to process the small part of the data instead of looking for a search in the entire data set. For example, In a large user table where the table is partitioned by country, then selecting users of country ‘IN’ will just scan one directory ‘country=IN’ instead of all the directories.
-
Limitations
Having too many partitions in table creates large number of files and directories in HDFS, which is an overhead to NameNode since it must keep all metadata for the file system in memory only.
Partitions may optimize some queries based on Where clauses, but may be less responsive for other important queries on grouping clauses.
In Mapreduce processing, Huge number of partitions will lead to huge no of tasks (which will run in separate JVM) in each mapreduce job, thus creates lot of overhead in maintaining JVM start up and tear down. For small files, a separate task will be used for each file. In worst scenarios, the overhead of JVM start up and tear down can exceed the actual processing time.
CREATE TABLE table_name (col_name_1 data_type_1, ....)
PARTITIONED BY (col_name_n data_type_n [COMMENT col_comment], ...);
-- Note that we didn’t include country and state columns in table definition
-- but included in partition definition.
CREATE TABLE partitioned_user(
firstname VARCHAR(64),
lastname VARCHAR(64),
address STRING,
city VARCHAR(64),
post STRING,
phone1 VARCHAR(64),
phone2 STRING,
email STRING,
web STRING
)
PARTITIONED BY (country VARCHAR(64), state VARCHAR(64))
STORED AS SEQUENCEFILE;
CREATE EXTERNAL TABLE table_name (col_name_1 data_type_1, ....)
PARTITIONED BY (col_name_n data_type_n [COMMENT col_comment], ...);- We can create external partitioned tables as well, just by using the EXTERNAL keyword in the CREATE statement
- but for creation of External Partitioned Tables, we do not need to mention LOCATION clause as we will mention locations of each partitions separately while inserting data into table.
Data insertion into partitioned tables can be done in two modes.
- Static Partitioning
- Dynamic Partitioning
In this mode, input data should contain the columns listed only in table definition but not the columns defined in partitioned by clause.
- Loading Data into Managed Partitioned Table From Local FS
-- Example
LOAD DATA LOCAL INPATH '${env:HOME}/staticinput.txt'
INTO TABLE partitioned_user
PARTITION (country = 'US', state = 'CA');
-- This will create separate directory under the default warehouse directory in HDFS.
-- /user/hive/warehouse/partitioned_user/country=US/state=CA/
-- Similarly we have to add other partitions, which will create corresponding directories in HDFS. Or else we can load the entire directory into Hive table with single command and can add partitions for each file with ALTER command.
LOAD DATA LOCAL INPATH '${env:HOME}/inputdir'
INTO TABLE partitioned_user;
INSERT OVERWRITE TABLE partitioned_user
PARTITION (country = 'US', state = 'AL')
SELECT * FROM another_user au
WHERE au.country = 'US' AND au.state = 'AL';We can overwrite an existing partition with help of OVERWRITE INTO TABLE partitioned_user clause.
- Loading Data into External Partitioned Table from HDFS
There is alternative for bulk loading of partitions into hive table. As data is already present in HDFS and should be made accessible by Hive, we will just mention the locations of the HDFS files for each partition.
If our files are on Local FS, they can be moved to a directory in HDFS and we can add partition for each file in that directory with commands similar to below.
hive> ALTER TABLE partitioned_user ADD PARTITION (country = 'US', state = 'CA')
LOCATION '/hive/external/tables/user/country=us/state=ca'Similarly we need to repeat the above alter command for all partition files in the directory so that a meta data entry will be created in metastore, mapping the partition and table.
Instead of loading each partition with single SQL statement as shown above, which will result in writing lot of SQL statements for huge no of partitions, Hive supports dynamic partitioning with which we can add any number of partitions with single SQL execution. Hive will automatically splits our data into separate partition files based on the values of partition keys present in the input files.
For dynamic partition loading we will not provide the values for partition keys
hive>INSERT INTO TABLE partitioned_user
PARTITION (country, state)
SELECT
firstname ,
lastname ,
address ,
city ,
post ,
phone1 ,
phone2 ,
email ,
web ,
country ,
state
FROM temp_user;-
We can also mix dynamic and static partitions by specifying it as PARTITION(country = ‘US’, state). But static partition keys must come before the dynamic partition keys.
-
But by default, Dynamic Partitioning is disabled in Hive to prevent accidental partition creations. To use dynamic partitioning we need to set below properties either in Hive Shell or in hive-site.xml file.
Please check here for dynamic partition settings
We can set these through hive shell with below commands:
set hive.exec.dynamic.partition=true;
set hive.exec.dynamic.partition.mode=nonstrict;
set hive.exec.max.dynamic.partitions=1000;
set hive.exec.max.dynamic.partitions.pernode=1000;Pre-Requisites:
- Hive Transactions Manager should be set to DbTxnManager SET hive.txn.manager=org.apache.hadoop.hive.ql.lockmgr.DbTxnManager;
- We need to enable concurrency SET hive.support.concurrency=true;
- Once we set the above properties, we should be able to insert data into any table.
- For updates and deletes, table should be bucketed and file format need to be ORC or any ACID Compliant Format.
- We also need to set table property transactions to true TBLPROPERTIES ('transactional'='true');
CREATE TABLE orders_transactional (
order_id INT,
order_date STRING,
order_customer_id INT,
order_status STRING
) CLUSTERED BY (order_id) INTO 8 BUCKETS
STORED AS orc
TBLPROPERTIES('transactional' = 'true');
INSERT INTO orders_transactional VALUES
(1, '2013-07-25 00:00:00.0', 1000, 'COMPLETE');
INSERT INTO orders_transactional VALUES
(2, '2013-07-25 00:00:00.0', 2001, 'CLOSED'),
(3, '2013-07-25 00:00:00.0', 1500, 'PENDING');
UPDATE orders_transactional
SET order_status = 'COMPLETE'
WHERE order_status = 'PENDING';
DELETE FROM orders_transactional
WHERE order_status <> 'COMPLETE';
SELECT * FROM orders_transactional;SELECT DISTINCT
col1,
col2,
col3
FROM
some_table;SELECT
col1 + col2 AS col3
FROM
some_table;SELECT
'(ID|Name)?+.+'
FROM
some_table;SELECT
col1,
col2,
col3
FROM
some_table
WHERE
where_condition
LIMIT
number_of_records;
above is same as below
FROM
some_table
SELECT
col1,
col2,
col3
WHERE
where_condition
LIMIT
number_of_records;
SELECT
t3.mycol
FROM
(
SELECT
col_a + col_b AS mycol
FROM
some_table
UNION ALL
SELECT
col_y AS mycol
FROM
another_table
) t3
JOIN t4 on (t4.col_x = t3.mycol);- execute host command within hive shell
hive>!host_command;
hive>!clear;
hive>!pwd;
hive>~hdfs dfs -ls;- show all Hive setting with hive shell
hive>set;
