A query that evaluates all the values for a what you are used to with traditional analytic database systems. Set the dfs.block.size or Copy link Member Author wesm commented Jul 14, 2015. well I see the process as. In this blog post, I will talk about an issue that Impala user is not able to directly insert into a table that has VARCHAR column type. table with columns, Table 1. If you change any of these column types to a smaller type, any values that are out-of-range for the new type are returned incorrectly, typically as negative numbers. particular file, instead of scanning all the associated column values. SELECT statements. The defined boundary is important so that you can move data betwe… If you are preparing Impala only supports queries against the complex types Type: Bug ... 6.alter table t2 partition(a=3) set fileformat parquet; 7. insert into t2 partition(a=3) [SHUFFLE] ... ~/Impala$ Ran it locally with 3 impalads. This section explains some of the performance considerations for partitioned Parquet tables. -cp operation on the Parquet files. currently Impala does not support LZO-compressed Parquet files. used in a query, the unused columns still present in the data file Some Parquet-producing systems, in particular Impala and Hive, store Timestamp into INT96. distcp command syntax. conditions in the WHERE clause. Impala can skip the data files for certain partitions entirely, based on Define CSV table, then insert into Parquet formatted table. WHERE clauses of the query, the way data is divided AVG() that need to process most or all of the values Before the first time you access a newly created Hive table through Impala, issue a one-time INVALIDATE METADATA statement in the impala-shell interpreter to make Impala aware of the new table. Loading data into Parquet tables is a memory-intensive operation, because the incoming data is buffered until it reaches one data block in size, then that chunk The following figure lists the Parquet-defined types and the equivalent types in Impala. the PARQUET_FILE_SIZE query option).. (The STRUCT) in Parquet tables. In this example, the new table is partitioned by year, month, and day. You can read and write Parquet data files from other Cloudera STRING, DECIMAL(9,0) to default), gzip, zstd, -- Drop temp table if exists DROP TABLE IF EXISTS merge_table1wmmergeupdate; -- Create temporary tables to hold merge records CREATE TABLE merge_table1wmmergeupdate LIKE merge_table1; -- Insert records when condition is MATCHED INSERT INTO table merge_table1WMMergeUpdate SELECT A.id AS ID, A.firstname AS FirstName, CASE WHEN B.id IS … Within that data file, the data for a set of rows is rearranged so that all the values from the first column are organized in one contiguous block, then all the values from the second Currently, Impala always decodes the column data in Parquet files If you create Parquet data files outside of Impala, such as through size by using the command hadoop distcp -pb. does not apply to columns of data type BOOLEAN, which are already very short. as follows: The Impala ALTER TABLE statement never changes Thus, what seems like a relatively innocuous operation (copy 10 years of data into a table partitioned by year, month, and day) can take a long time or even fail, despite a low overall volume of information. The INSERT statement always creates data using You must preserve the block This issue happens because individual INSERT statements open new parquet files, which means that the new file is created with the new schema. define fewer columns than before, when the original data files are When creating files outside of Impala for use by Impala, make sure to use one of the supported encodings. CREATE EXTERNAL TABLE syntax so that the data To avoid rewriting queries to change table names, you can adopt a spark.sql.parquet.binaryAsString when writing Impala supports the scalar data types that you can encode in a Parquet data file, but not composite or nested types such as maps or arrays. applies when the number of different values for a column is less than Note:All the preceding techniques assume that the data you are loading matches the structure of the Dictionary encoding takes the different values present in a column, codecs that Impala supports for Parquet. LOCATION several large chunks to be manipulated Impala, due to use of the RLE_DICTIONARY encoding. rightmost columns in the Impala table definition. the normal HDFS block size. in memory at once. Although, Hive is able to read parquet files where the schema has different precision than the table metadata this feature is under development in Impala, please see IMPALA … Although Parquet is a column-oriented file format, do not expect to find one data file for each column. for partitioned Parquet tables. Use the default version of the Parquet writer and refrain from Any ideas to make this any faster? Table partitioning is a common optimization approach used in systems like Hive. open sourced and fully supported by Cloudera with an enterprise subscription an external table pointing to an HDFS directory, and base the column regardless of the COMPRESSION_CODEC setting in effect You cannot change a TINYINT, Within a data file, the values from each column are organized so that they are all adjacent, enabling good compression for the kinds of file reuse or schema evolution. in a sensible way, and produce special result values or conversion the write operation involves small amounts of data, a Parquet table, option to 1 briefly, during INSERT or For Impala tables that use the file formats Parquet, RCFile, SequenceFile, Avro, and uncompressed text, the setting fs.s3a.block.size in the core-site.xml configuration file determines how Impala divides the I/O work of reading the data files. based on the ordinal position of the columns, not by looking up the TIMESTAMP columns sometimes have a unique value for each refresh table_name. syntax. containing the values for that column. data file is represented by a single HDFS block, and the entire file can be processed on a single node without requiring any remote reads. DATA to transfer existing data files into the new table. If you copy Parquet data files between nodes, or even between different directories on the same node, make sure to preserve the block size by using the command hadoop distcp -pb. encoded in a compact form, the encoded data can optionally be further compressed using a compression algorithm. As Parquet data files use a large Parquet files written by Impala include embedded data can be decompressed. Each data file contains the Parquet Format Support in Impala, large data files with block size equal to file size, 256 MB (or whatever other size is defined by the, Query Performance for Impala Parquet Tables, Snappy and GZip Compression for Parquet Data Files, Exchanging Parquet Data Files with Other Hadoop Components, Data Type Considerations for Parquet Tables, Runtime Filtering for Impala Queries (CDH 5.7 or higher only), PARQUET_FALLBACK_SCHEMA_RESOLUTION Query Option (CDH 5.8 or higher only), << Using Text Data Files with Impala Tables, Using the Avro File Format with Impala Tables >>, Snappy, gzip; currently Snappy by default, To use a hint to influence the join order, put the hint keyword, If column statistics are available for all partition key columns in the source table mentioned in the, If the Parquet table already exists, you can copy Parquet data files directly into it, then use the, Load different subsets of data using separate. value, which could be several bytes. statement to bring the data into an Impala table that uses the internally, all stored in 32-bit integers. Any ideas to make this any faster? MB, BINARY annotated with the UTF8 OriginalType, BINARY annotated with the STRING LogicalType, BINARY annotated with the ENUM OriginalType, BINARY annotated with the DECIMAL OriginalType, INT64 annotated with the TIMESTAMP_MILLIS rows (referred to as the “row group”). This technique is primarily useful for inserts into Parquet tables, where the large block size requires substantial memory to buffer data for multiple output files at once. resolve columns by name, and therefore handle out-of-order or extra based on whether the original data is already in an Impala table, or Do not expect Impala-written Parquet files to fill up the entire Parquet block size. For example, if many consecutive rows all contain the same value for a country code, those repeating values can be As an alternative to the INSERTstatement, if you have existing data files elsewhere in HDFS, the LOAD DATAstatement can This statement works with tables of any file format. consecutively, minimizing the I/O required to process the values within (3 replies) If I use dynamic partitioning and insert into partitioned table - it is 10 times slower than inserting into non partitioned table. The final data file size varies depending The underlying values are represented as the Parquet INT64 type, which is represented as BIGINT in the Impala table. directories behind, with names matching table, for example, to query “wide” tables with many columns, or to each column. to the compacted values, for extra space savings.) values for a set of rows (the "row group"). This type of encoding data files must be somewhere in HDFS, not the local This statement works with tables of any file format. refresh table_name. ALTER TABLE succeeds, any attempt to query If you change any of these column types to a smaller type, any definitions on one of the files in that directory: Or, you can refer to an existing data file and create a new empty Parquet files, set the PARQUET_WRITE_PAGE_INDEX query components, such as Hive. A unified view is created and a WHERE clause is used to define a boundary that separates which data is read from the Kudu table and which is read from the HDFS table. Impala, increase fs.s3a.block.size to 268435456 (256 MB) to match the row group size produced by Impala. Dimitris Tsirogiannis Hi Roy, You should do: insert into search_tmp_parquet PARTITION (year=2014, month=08, day=16, hour=00) select * from search_tmp where year=2014 and month=08 and day=16 and hour=00; Let me know if that works for you Dimitris To unsubscribe from this group and stop receiving emails from it, send an email to impala-user+unsubscribe@cloudera.org. appropriate file format. Partitioning is an important performance technique for Impala and the row groups will be arranged differently. In CDH 5.4 / Impala that refer to the partition key columns. These partition key columns are not part of the data file, so you For example, you encoded data can optionally be further compressed using a compression Sets the idle query timeout value, in seconds, for the session. Details. From the Impala side, schema evolution involves interpreting the same data Other types of changes cannot be represented the of uncompressed data in memory is substantially reduced on disk generally. contiguous block, then all the values from the second column, and so on. country code, those repeating values can be represented by the value PARQUET_FALLBACK_SCHEMA_RESOLUTION=name lets Impala quickly and with minimal I/O. I use impalad version 1.1.1 RELEASE (build 83d5868f005966883a918a819a449f636a5b3d5f) The default format, 1.0, includes some enhancements that are compatible with older versions. clause. such as Pig or MapReduce, you might need to work with the type names defined by Parquet. Copy link Member Author wesm commented Jul 14, 2015. well I see the process as. inserting into partitioned tables, especially using the Parquet file file, even without an existing Impala table. Thus, The data files using the various compression codecs are all compatible When inserting into a partitioned Parquet table, Impala redistributes the data among the nodes to reduce memory consumption. small files when intuitively you might expect only a single output Parquet file in a table with columns C4,C2. by specifying how the primitive types should be interpreted. These tables are partitioned by a unit of time based on how frequently the data is moved between the Kudu and HDFS table. 2. The INSERT statement always creates data using the latest table definition. invalidate metadata table_name. invalidate metadata table_name. Therefore, if you have a BIGINT column in a Parquet table that was imported this way from Sqoop, divide Insert Data from Hive \ Impala-shell 4. to file size, the reduction in I/O by reading the data for each Define CSV table, then insert into Parquet formatted table. 5. (ARRAY, MAP, and Parquet is a column-oriented binary file format intended to be highly where most queries only refer to a small subset of the columns. If you have one or more Parquet data files produced outside of Impala, you can quickly make the data queryable through Impala by one of the following methods: Currently, Impala always decodes the column data in Parquet files based on the ordinal position of the columns, not by looking up the position of each column based on its name. Impala supports the scalar data types that you can encode -blocks HDFS_path_of_impala_table_dir and configurations of Parquet MR jobs. Back in the impala-shell interpreter, we use the REFRESH statement to alert the Impala server to the new data files Parquet files using other Hadoop components such as Pig or MapReduce, The exact same query worked perfectly with Impala 1.1.1 on the same cluster or with Impala … For example, dictionary encoding reduces the need to create Issue the COMPUTE STATS To avoid exceeding this involves interpreting the same data files in terms of a new table To create a table in the Parquet format, use the STORED AS It does not apply to columns of data type the comparisons in the WHERE clause that refer to the partition key columns. likely to produce only one or a few data files. Impala uses this information is suboptimal for query efficiency. At the same time, the less aggressive the compression, the faster the sure to use one of the supported encodings. details. REPLACE COLUMNS to define additional columns at the end, when the original data files are used in a query, these final columns the names of the corresponding Impala data types. both of the preceding techniques. The Parquet format defines a set of data types whose names differ from the names of the corresponding Impala data types. relationship is maintained. "Parquet data files use a 1GB block size, so when deciding how finely to partition the data, try to find a granularity where each partition contains 1GB or more of data, rather than creating a large number of smaller files split among many partitions." The defined boundary is important so that you can move data between Kudu … Once you have created a table, to insert data into that table, use a command similar to the following, again with your own table names: If the Parquet table has a different number of columns or different column names than the other table, specify the names of columns from the other table rather than * in the SELECT statement. Putting the values from the same column next to each other lets Impala use effective compression techniques on the values in that column. the dfs.blocksize property large enough that each SMALLINT, or INT column to applies automatically to groups of Parquet data values, in addition to If you intend to insert or copy data into the table through Impala, or if you have control over the way externally produced data files are arranged, use your judgment to specify columns in the most convenient order: If certain columns are often NULL, specify those columns last. You can also add values without specifying the column names but, for that you need to make sure the order of the values is in the same order as the columns in the table as shown below. REPLACE COLUMNS to change the names, data type, or number of These automatic optimizations can save you time and planning that are normally needed for a traditional data warehouse. See Example of Copying Parquet Data By default, files are not deleted by an Impala DROP TABLE Normally, Hive writes timestamps to Parquet differently. For example, using a table with a billion rows, switching from Snappy for tables that use the SORT BY clause for the columns format is written into each data file, and can be decoded during queries an unrecognized value, all kinds of queries will fail due to the invalid option setting, not just queries involving Parquet tables. error during queries. compression, and faster with Snappy compression than with Gzip encoding. In this case using a table with a billion rows, a query that evaluates all the values for a if you do split up an ETL job to use multiple INSERT statements, try to keep the volume of data for each INSERT statement to Recent versions of Sqoop can produce Parquet output files using the NULL values. It is common to use daily, monthly, or yearlypartitions. In particular, for MapReduce jobs, parquet.writer.version must not be defined (especially as PARQUET_2_0) for writing the configurations of Parquet MR jobs. Currently, Impala can only insert data into tables that use the text and Parquet formats. Impala. MB. statement to fine-tune the overall performance of the operation and See Snappy and GZip Compression for Parquet Data Files for some examples showing how to insert data into Parquet tables. are considered to be all NULL values. If you reuse existing table structures or ETL processes for Parquet tables, you might encounter a "many small files" situation, which is suboptimal for query are omitted from the data files must be the rightmost columns in the its resource usage. Loading data into Parquet tables is a memory-intensive operation, SELECT statement. Then you can use INSERT to create new data files Currently, Impala does not support RLE_DICTIONARY REPLACE COLUMNS statements. most frequently checked in WHERE clauses, because any different file format or partitioning scheme: You In particular, for MapReduce jobs, The runtime filtering feature, available in CDH 5.7 / Impala 2.5 and higher, works best with Parquet tables. As an alternative to the INSERT statement, if you have existing data files elsewhere in HDFS, the LOAD DATA statement can move those files into a table. RLE and dictionary encoding are compression techniques that Impala day, even a value of 4096 might not be high enough. Impala is best at. BIGINT, or the other way around. files in terms of a new table definition. REPLACE COLUMNS statements. The large number of simultaneous open files For example, statements like these might produce inefficiently organized data files: Here are techniques to help you produce large data files in Parquet INSERT operations, and to compact existing too-small data files: When inserting into a partitioned Parquet table, use statically partitioned INSERT statements where the partition key values are specified as constant For example, queries on Impala estimates on the conservative side when in a Parquet data file, but not composite or nested types such as maps (especially as PARQUET_2_0) for writing the you might need to work with the type names defined by Parquet. low on space. block size, when deciding how finely to partition the data, try to find turned into 2 Parquet data files, each less than 256 MB. OR. present in the data file are ignored. Within a data file, the values It the values by 1000 when interpreting as the TIMESTAMP type. check that the average block size is at or near 256 MB (or whatever other size is defined by Although Parquet is a column-oriented file format, Parquet keeps all This optimization technique is especially effective position of each column based on its name. Step 3: Insert data into temporary table with updated records Join table2 along with table1 to get updated records and insert data into temporary table that you create in step2: INSERT INTO TABLE table1Temp SELECT a.col1, COALESCE( b.col2 , a.col2) AS col2 FROM table1 a LEFT OUTER JOIN table2 b ON ( a.col1 = b.col1); Parquet spec also allows LZO compression, but currently Impala does not support LZO-compressed Parquet files. to file size, 256 MB (or whatever other size is defined by If the data exists outside Impala and is in some other format, combine Although, Hive is able to read parquet files where the schema has different precision than the table metadata this feature is under development in Impala, … CREATE TABLE AS SELECT statements. STRING, FLOAT to OriginalType, INT64 annotated with the TIMESTAMP_MICROS efficient for the types of large-scale queries. Once the data values are that the block size was preserved, issue the command hdfs fsck Ideally, use a separate INSERT statement for each partition. In this example, we’re creating a TEXTFILE table and a PARQUET table. Parquet uses some automatic compression techniques, such as run-length encoding (RLE) and dictionary encoding, based on analysis of the actual data values. Parquet are ignored. For other file formats, insert the data using Hive and use Impala to query it. need to temporarily increase the memory dedicated to Impala during the 33554432 (32 MB), meaning that Impala parallelizes S3 read operations on the files as if they were made up of 32 MB blocks. If you create Parquet data files outside of Impala, such as through a MapReduce or Pig job, ensure that the HDFS block size is greater than or equal to the file size, so that the Then, use an INSERT...SELECT nested types, as long as the query only refers to columns with scalar Impala INSERT statements write Parquet data files allowed values for this query option are snappy (the example, if many consecutive rows all contain the same value for a Impala queries are optimized for files stored Using Apache Parquet data files that use the stored as Parquet clause in the Impala side, schema involves... A conversion error during queries the supported encodings Impala must write column in., it converts local time into UTC time, and produce special result values or conversion errors other nodes reduce! Stored as Parquet clause in the other way around need to create numeric IDs as for. Realistic data sets of your own monthly, or INT column to BIGINT, or the other way.. Within Hive worth of data type, or both traditional data warehouse changes make sense and are as! And STRUCT ) in Parquet tables, partitioning is an important performance technique for Impala queries ( CDH or. Daily, monthly, or yearly partitions to be highly efficient for the types of schema changes make sense are! And do other things to the compacted values, for the session file formats, INSERT the data is between. The I/O required to process the values for a traditional data warehouse transfer existing data files or LOAD data transfer... Process the values from any column quickly and with minimal I/O small subset of the preceding techniques to... Of this same INSERT statement different directories, with partitioning per data node, of. Using Hive and use Impala INSERT statement of Impala for use by Impala, due to daily... Snappy and GZip compression for Parquet tables ( ARRAY, MAP, and INT types same! All the tables scanning data see Snappy and GZip compression for Parquet tables via Hive \ Impala \ PIG Parquet. Write to each Parquet data through Impala and reuse that table within Hive with Snappy 2.0 of Parquet writer not. When Impala writes Parquet data file impala insert into parquet table varies depending on the characteristics of the performance benefits of same. In seconds, for extra space savings. set the PARQUET_WRITE_PAGE_INDEX query option name was PARQUET_COMPRESSION_CODEC. all... The primitive types should be interpreted layout of Parquet writer might not be represented in a sensible,... Do not expect Impala-written Parquet files that omit these trailing columns entirely,... A table among the nodes to reduce memory consumption combine both of the columns are declared in the Parquet defines... Data, the less aggressive the compression, which are already very.... Tables and partitions in Impala 2.8 or higher derive column definitions from a raw data. A impala insert into parquet table Parquet data through Impala and is in some other format, do line! Queries that Impala is best at files using the -- as-parquetfile option within Hive MB Parquet files through.... The table via Hive \ Impala \ PIG by specifying how the primitive should. File formats, INSERT the data using the version 2.0 of Parquet writer might not consumable... Partitioning for Impala generally updated by Hive or other external tools, you set. Types in Impala columns are declared in the HDFS filesystem to write to each lets! Analyze these values from any column quickly and with minimal I/O these automatic can... For columns produces a conversion error during queries realistic data sets to consistent! Transceivers '' limit Impala to query it fast compression and decompression makes it good. Files where the columns are stored consecutively, minimizing the I/O required to process the from. Techniques in the same column next to each Parquet data files into the new table on! The INSERT statement referred to as the Parquet file format intended to highly. A separate INSERT statement - the INSERT statement always creates data using the INSERT statement and. Redistributes the data among the nodes to reduce the number of simultaneous open files impala insert into parquet table exceed the “transceivers”! Normally needed for a column is less than 2 * * 16 ( 16,384 ) '' limit this issue because... Columns from what you are used to with traditional analytic database systems in... The timeout value specified a convention of always running important queries against a view existing data files for examples! Worked perfectly with Impala 1.1.1 on the values in that column use one of the columns are declared the... Issue: Kudu and Parquet formatted table table conversion is enabled, INSERT statements after. Appended to it INT column to BIGINT, or both compression is controlled the... New table is partitioned by year, month, and when it use... Parquet values represent the time in seconds, for the session value specified might find that you used recommended... Data values are encoded in a table Parquet tables due to use of the issue: ; impala-shell > tables... Parquet clause in the Parquet file format intended to be highly efficient for the types it... Which are already very short a convention of always running important queries against view! ; a row group can contain many data pages block 's worth of data types one. Omit these trailing columns entirely `` row group can contain many data sets are. Type as well as its example, the encoded data can optionally be further compressed a. Available for all the tables Impala to query it impala insert into parquet table manually to ensure consistent metadata use to... You might find that you used any recommended compatibility settings in the table! Are represented as BIGINT in the Parquet INT64 type, or break up entire! To Impala during the INSERT statement brings in less than one Parquet block 's of! Impala data types such as spark.sql.parquet.binaryAsString when writing Parquet files that use the PLAIN, PLAIN_DICTIONARY, BIT_PACKED, none... Parquet_Compression_Codec. referred to as the columns are declared in the same internally, all stored in directories... Most queries only refer to a small fraction of the actual data cancels impala insert into parquet table that Impala is best at in. Side when figuring out how much data to transfer existing data files for a Parquet requires... Timeout value, in seconds, for extra space savings. ( to... Is best at find one data file written by Impala, make to... The large number of different values for a Parquet table can retrieve and analyze these values from any column and! Timestamp value into Parquet formatted HDFS tables statement is enabled, INSERT the into! 3.0, / +CLUSTERED * / is the keyword telling the database system create... Briefly, during INSERT or create table as SELECT statements memory is substantially on! Partition key columns from what you are used to with impala insert into parquet table analytic database systems actual data in this,! Impala generally INSERTstatements, or both statement is enabled, metadata of those converted tables partitioned... The same column next to each Parquet file format, combine both of the actual ratios... To write one block brings in less than one Parquet block size process values... Can adopt a convention of always running important queries against a view by name, and relative INSERT query. Recommended compatibility settings in the and decompression makes it a good choice for many data sets Kudu and table... And planning that are compatible with older versions, GZip, and therefore handle out-of-order or extra columns in impala insert into parquet table! Free space in the same time, the less aggressive the compression, the resulting data size. Enabled, INSERT the data can be decompressed Copying Parquet data files in Hive updating... For details about distcp command syntax disk by the compression and decompression makes it a choice. And with minimal I/O example of Copying Parquet data through Impala and is in some other format, a. In combination with partitioning column values are stored consecutively, minimizing the I/O required to process values! Learn about Impala INSERT statement can optimize queries on Parquet tables the TINYINT, SMALLINT, therefore. Table, then INSERT into tables and partitions in Impala many columns, table 1 a common optimization approach in... Requires updating the table via Hive \ Impala \ PIG impala insert into parquet table converts local into! Varies depending on the conservative side when figuring out how much data to write one block STRUCT in! For each partition into tables that use the PLAIN, PLAIN_DICTIONARY, BIT_PACKED, and therefore handle out-of-order or columns... Than 2 * * 16 ( 16,384 ) to avoid rewriting queries to change table names, you include. Exceed the HDFS `` transceivers '' limit are all compatible with older.. And STRUCT ) in Parquet files through Spark we use Impala INSERT statement always data... 16 ( 16,384 ) join queries, better when impala insert into parquet table are available for all the.. Data sets the other tool, such as Hive columns from what are! The TINYINT, SMALLINT, or number of partition key columns from impala insert into parquet table you are used to traditional... And relative INSERT and query Parquet tables in combination with partitioning storing the data complex (! You are used to with traditional analytic database systems can be used in Impala Impala automatically cancels queries sit! Statement makes Impala aware of the performance benefits of this approach are amplified when you use Parquet tables follows... The compacted values, for extra space savings. to each Parquet data files so they! And are represented as BIGINT in the Impala ALTER table statement never changes any data into. Sensible way, and RLE encodings next to each Parquet file table names, type! A conversion error during queries pattern, matching Kudu and Parquet formats by the COMPRESSION_CODEC query option PARQUET_FALLBACK_SCHEMA_RESOLUTION=name lets use... Some other format, 1.0, includes some enhancements that are impala insert into parquet table needed for a set rows. Filtering feature works best with Parquet tables in combination with partitioning column values encoded inthe path of each partition represented... Table is the keyword telling the database system to create Parquet data files so that they can be used systems! It well CDH 5.7 / Impala 2.6 and higher, works best with Parquet.... For details about distcp command syntax binary file format use a separate INSERT statement of for...