List of Pages in Category Developers (210 pages)
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A
- ABORT_ON_DEFAULT_LIMIT_EXCEEDED Query Option
- ABORT_ON_ERROR Query Option
- Accessing Data Stored in Amazon S3 through Spark
- Accessing HBase by using the HBase Shell
- Accessing Table Data with MapReduce
- Accessing Table Data with Pig
- ALTER TABLE Statement
- ALTER VIEW Statement
- APPX_COUNT_DISTINCT Query Option (CDH 5.2 or higher only)
- APPX_MEDIAN Function
- ARRAY Complex Type (CDH 5.5 or higher only)
- AVG Function
)B
- BATCH_SIZE Query Option
- Benchmarking Impala Queries
- BIGINT Data Type
- BOOLEAN Data Type
- Building and Running a Crunch Application with Spark
- Building Spark Applications
C
- CDH 5 and MapReduce
- CHAR Data Type (CDH 5.2 or higher only)
- Cloudera QuickStart
- Cloudera Search Guide
- Cloudera Search Overview
- Comments
- Complex Types (CDH 5.5 or higher only)
- Components of the Impala Server
- COMPRESSION_CODEC Query Option (CDH 5.2 or higher only)
- COMPUTE STATS Statement
- Configuration Settings for HBase
- Configuring Impala Delegation for Hue and BI Tools
- Configuring Impala to Work with JDBC
- Configuring Impala to Work with ODBC
- Connecting to impalad through impala-shell
- Controlling Impala Resource Usage
- COUNT Function
- CREATE DATABASE Statement
- CREATE FUNCTION Statement
- CREATE ROLE Statement (CDH 5.2 or higher only)
- CREATE TABLE Statement
- CREATE VIEW Statement
D
- Data Types
- DDL Statements
- DEBUG_ACTION Query Option
- DECIMAL Data Type (CDH 5.1 or higher only)
- DEFAULT_ORDER_BY_LIMIT Query Option
- DESCRIBE Statement
- Detecting and Correcting HDFS Block Skew Conditions
- Developing and Running a Spark WordCount Application
- Developing Impala Applications
- Developing Spark Applications
- DISABLE_UNSAFE_SPILLS Query Option (CDH 5.2 or higher only)
- DISTINCT Operator
- DML Statements
- DOUBLE Data Type
- DROP DATABASE Statement
- DROP FUNCTION Statement
- DROP ROLE Statement (CDH 5.2 or higher only)
- DROP STATS Statement
- DROP TABLE Statement
E
- EXEC_SINGLE_NODE_ROWS_THRESHOLD Query Option (CDH 5.3 or higher only)
- EXPLAIN Statement
- EXPLAIN_LEVEL Query Option
F
G
- GRANT Statement (CDH 5.2 or higher only)
- GROUP BY Clause
- GROUP_CONCAT Function
- Guidelines for Designing Impala Schemas
H
- HAVING Clause
- HBase Filtering
- HBaseMapReduceIndexerTool
- HBASE_CACHE_BLOCKS Query Option
- HBASE_CACHING Query Option
- How Impala Fits Into the Hadoop Ecosystem
- How Impala Works with Hadoop File Formats
I
- Impala Aggregate Functions
- Impala Analytic Functions
- Impala Bit Functions
- Impala Built-In Functions
- Impala Concepts and Architecture
- Impala Conditional Functions
- Impala Date and Time Functions
- Impala Frequently Asked Questions
- Impala Guide
- Impala Mathematical Functions
- Impala Miscellaneous Functions
- Impala Overview
- Impala Performance Guidelines and Best Practices
- Impala Requirements
- Impala Requirements
- Impala Reserved Words
- Impala Schema Objects and Object Names
- Impala SQL Language Reference
- Impala SQL Statements
- Impala String Functions
- Impala Tutorials
- Impala Type Conversion Functions
- Impala User-Defined Functions (UDFs)
- Impala Web User Interface for Debugging
- impala-shell Command Reference
- impala-shell Configuration Options
- Importing Data Into HBase
- INSERT Statement
- INT Data Type
- Integrated Resource Management with YARN
- INVALIDATE METADATA Statement
J
L
- LIMIT Clause
- Literals
- LIVE_PROGRESS Query Option (CDH 5.5 or higher only)
- LIVE_SUMMARY Query Option (CDH 5.5 or higher only)
- LOAD DATA Statement
M
- Managing Disk Space for Impala Data
- MAP Complex Type (CDH 5.5 or higher only)
- MapReduce 2.0 (YARN)
- MapReduce Batch Indexing Reference
- MapReduceIndexerTool
- MAX Function
- MAX_ERRORS Query Option
- MAX_IO_BUFFERS Query Option
- MAX_SCAN_RANGE_LENGTH Query Option
- MEM_LIMIT Query Option
- MIN Function
- Modifying Impala Startup Options
N
O
- OFFSET Clause
- ORDER BY Clause
- Overview of Impala Aliases
- Overview of Impala Databases
- Overview of Impala Functions
- Overview of Impala Identifiers
- Overview of Impala Tables
- Overview of Impala Views
P
- PARQUET_FILE_SIZE Query Option
- Partitioning for Impala Tables
- Performance Considerations for Join Queries
- Planning for Impala Deployment
- Porting SQL from Other Database Systems to Impala
- Ports Used by Impala
Q
- Query Hints in Impala SELECT Statements
- Query Options for the SET Statement
- QUERY_TIMEOUT_S Query Option (CDH 5.2 or higher only)
R
- Reading Data from HBase
- REAL Data Type
- REFRESH Statement
- REQUEST_POOL Query Option
- RESERVATION_REQUEST_TIMEOUT Query Option (CDH 5 only)
- REVOKE Statement (CDH 5.2 or higher only)
- Running Commands and SQL Statements in impala-shell
- Running Spark Applications
- Running Spark Python Applications
S
- Scalability Considerations for Impala
- SELECT Statement
- SET Statement
- SHOW Statement
- SMALLINT Data Type
- Spark and Hadoop Integration
- Spark and IPython and Jupyter Notebooks
- Spark Indexing
- SQL Differences Between Impala and Hive
- SQL Operators
- STDDEV, STDDEV_SAMP, STDDEV_POP Functions
- STRING Data Type
- STRUCT Complex Type (CDH 5.5 or higher only)
- Subqueries in Impala SELECT Statements
- SUM Function
- Supported Sources, Sinks, and Channels
- SYNC_DDL Query Option
T
- Table and Column Statistics
- Testing Impala Performance
- TIMESTAMP Data Type
- TINYINT Data Type
- Troubleshooting Impala
- TRUNCATE TABLE Statement (CDH 5.5 or higher only)
- Tuning Impala for Performance
U
- Understanding Impala Query Performance - EXPLAIN Plans and Query Profiles
- UNION Clause
- USE Statement
- Using Avro with MapReduce
- Using CDH with Isilon Storage
- Using HDFS Caching with Impala (CDH 5.1 or higher only)
- Using Hive with HBase
- Using Impala Logging
- Using Impala through a Proxy for High Availability
- Using Impala to Query HBase Tables
- Using Impala to Query the Amazon S3 Filesystem
- Using Impala with Isilon Storage
- Using MapReduce Batch Indexing with Cloudera Search
- Using MapReduce with HBase
- Using Pig with HBase
- Using PySpark
- Using Snappy for MapReduce Compression
- Using Spark MLlib
- Using Spark SQL
- Using Spark Streaming
- Using Text Data Files with Impala Tables
- Using the Avro File Format with Impala Tables
- Using the Cloudera Manager Java API for Cluster Automation
- Using the Impala Shell (impala-shell Command)
- Using the Parquet File Format with Impala Tables
- Using the Parquet File Format with Impala, Hive, Pig, and MapReduce
- Using the RCFile File Format with Impala Tables
- Using the SequenceFile File Format with Impala Tables
- Using the spark-avro Library to Access Avro Data Sources
V
- VARCHAR Data Type (CDH 5.2 or higher only)
- VARIANCE, VARIANCE_SAMP, VARIANCE_POP, VAR_SAMP, VAR_POP Functions
- V_CPU_CORES Query Option (CDH 5 only)
W