A key partition column at Eventbrite is transaction date (txn_date). What is Presto? How are you building/maintaining team cohesion? I found that I had to cast each integer as doubles to divide and get two decimals. 13. Parameters. I am working in AWS and I have created a view and it gives me the output I want from the main Hive Table. Query presto… We look forward to giving Presto’s Cost-Based Optimizer a test drive and kicking the tires on new features such as dynamic filtering & partition pruning! (=AUTOMATIC) session property and I’m really excited to see this feature in action. can be optimized by replacing the four LIKE clauses with a single REGEXP_LIKE clause: 6. The WITH clause is used to define an inline view within a single query. The ORDER BY clause returns the results of a query in sort order. explain select SUBSTRING(last_modified,1,4) ,count(*)  from hive.df_machine_learning.event_text where lower(name) like ‘%wilbraham%’ or (REGEXP_LIKE(lower(name), ‘.*wilbraham. They provide features that store data efficiently by using column-wise compression based on data type, special encoding, and predicate pushdown. Partitioning these entries by day make querying for the 100 or so log events that occurred from Dec. 11-19, 2019, much quicker. What is MySQL? This applies to Presto as well as MySQL! The default join algorithm of Presto is broadcast join, which partitions the left-hand side table of a join and sends (broadcasts) a copy of the entire right-hand side table to all of the worker nodes that have the partitions. Collects table and column statistics for a given table. That said, the best practices for developing efficient SQL via Presto/Hive are different from those used to query standard RDBMS databases. We’re not currently using Presto’s Cost-Based Optimizer (CBO)! As we know, SQL is a declarative language and the ordering of tables used in joins in MySQL, for example,  is *NOT* particularly important. When specified, the partitions that match the partition specification are returned. REGEXP_LIKE vs comparable LIKE/OR combination. The Athena query engine is based in part on HiveQL DDL.. Athena does not support all DDL statements, and there are some differences between HiveQL DDL and Athena DDL. So it is no surprise that Presto’s query optimizer is unable to improve queries that contain many LIKE clauses. 9. Snappy is designed for speed and will not overload your CPU cores. Let’s say you have a large table with a state column and you often required to run analytics-related queries for each state hence, the state column is qualified to be a partition column. These clauses work the same way that they do in a SELECT statement. Columns that are used as WHERE filters are good candidates for partitioning. At Eventbrite, we define Hive tables as PARQUET using compression equal to SNAPPY…. It relies on the stats estimates of the CBO to correctly convert the join distribution type to “broadcast” join. Query presto… Presto does not perform automatic join-reordering, so make sure your largest table is the first table in your sequence of joins. Partitioning divides your table into parts and keeps the related data together based on column values such as date, country, region, etc. Presto provides a web interface for monitoring queries (, https://prestodb.io/docs/current/admin/web-interface.html. The above function is used to run queries on Athena using athenaClient i.e. Using the approx_distinct(x) function on large data sets vs COUNT(DISTINCT x) will result in performance gains. We persist this metadata information in Amazon Aurora and access it through the Presto/Hive connector via the Hive Metastore Service (HMS). Same as SHOW COLUMNS FROM. Fix over-creation of initial splits. Eventbrite data engineering released Presto 330 in March 2020, but we haven’t tested CBO yet. EXPLAIN is an invaluable tool for showing the logical or distributed execution plan of a statement and to validate the SQL statements. SHOWSTATSFOR(SELECTFROMWHERE)will show statistics for the table layout of table trepresenting a subset of data after applying the given filtering Both the column list and the filtering condition used in the WHEREclause can reference table columns. Consider the cardinality within GROUP BY. To view the contents of a partition, see the Query the Data section on the Partitioning Data page. We look forward to using this feature in the near future! directly accesses the data through a specialized distributed query engine that is very similar to those found in commercial parallel relational databases. Describe Table. The LIKE operation is well known to be slow especially when not anchored to the left (i.e. 2. standard deviation of 2.3%) is more than good enough! We look forward to using this feature in the near future! Why Would Webpack Stop Re-compiling? I had a lot of explaining to do that day as the missing index made it to QA and Stage but not Production! If the right-hand side table is “small” then it can be replicated to all the join workers which will save CPU and network costs. To  process the sort, Presto must send all rows of data to a single worker and then sort them. Allow non-VARCHAR columns in DELETE query. To ensure that the benchmarks focus on the effect of the join optimizations: 1. Thanks! DESCRIBE. Fix ORC writer failure when writing NULL values into columns of type ROW, MAP, or ARRAY. The partition specification, which separates the input rows into different partitions. You define partitions at table creation, and they help reduce the amount of data scanned per query, thereby improving performance. Overview. NAME = 'part';-- show columns of the table given database/table names SELECT c. * FROM DBS d JOIN TBLS t ON d. DB_ID = t. DB_ID JOIN SDS s ON t. SD_ID = s. SD_ID JOIN COLUMNS_V2 c ON s. CD_ID = c. CD_ID WHERE t. TBL_NAME = 'orders' AND d. NAME = 'part' ORDER by CD_ID, INTEGER_IDX;-- show partitions of the table given database/table names … Along the way I’ve had to learn new terms such as “federated queries”, “broadcast joins”, “reshuffling”, “join reordering”, and “predicate pushdown”. I’ve been trained to make performance improvements such as:  only choose columns in a SELECT that are absolutely necessary, stay away from LIKE clauses, review the cardinality of columns before adding indexes, and always JOIN on indexed columns. Defining Table Partitions. It allows for flattening nested subqueries. To show the partitions in a table and list them in a specific order, see the Listing Partitions for a Specific Table section on the Querying AWS Glue Data Catalog page. Snappy is designed for speed and will not overload your CPU cores. This is a Avro table with partitions columns of product, procid_, enddt, and trialgroup. 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If you are using the ORDER BY clause to look at the top N values, then use a LIMIT clause to reduce the cost of the sort significantly by pushing the sorting/limiting to individual workers, rather than the sorting being done by a single worker. You can learn more about Eventbrite’s use of Presto by checking out my previous post at, Boosting Big Data workloads with Presto Auto Scaling. Presto is the “SQL-on-Anything” solution that powers Eventbrite’s data warehouse. Presto Examples The Hive connector supports querying and manipulating Hive tables and schemas (databases). It’s been very rewarding for me as the “Old School DBA” to learn new SQL tricks related to a distributed query engine such as Presto. Apache Parquet is an open-source, column-oriented data storage format. Hey @steveodom, I know this was a while ago, but did you ever get the 'change column type' statement working? Presto does not perform automatic join-reordering unless using the Cost Based Optimizer! These features allow our query engines (including Presto) to reach peak performance and query speed. By using dynamic filtering via run-time predicate pushdown, we can squeeze out more performance gains for highly-selective inner-joins. Let’s transition to Presto performance tuning tips and how they compare to standard best practices with MySQL. Fix an issue where VARCHAR columns added to an existing table could not be queried. method LIKE '%hippo%' OR Presto has approximate aggregation functions that give you significant performance improvements. To this effect, we started replicating our existing data stores to Amazon’s Simple Storage Service (S3), a platform proven for its high reliability, and widely used b… Some of the best practices for Presto are the same as relational databases and others are brand new to me. The end result will be long execution times and/or memory errors. method LIKE '%tiger%' OR We have used TPC-DS queries published in this benchmark. to Eventbrite’s Data Foundry team (Jeremy Bakker,  Alex Meyer, Jasper Groot, Rainu Ittycheriah, Gray Pickney, and Beck Cronin-Dixon) for the world-class Presto support, and Steven Fast for reviewing this blog post. Iceberg seeks to improve upon conventional partitioning, such as that done in Apache Hive. This is not supported by Athena apparently. Avoiding Stale Caches¶. ALTER TABLE logs.trades ADD COLUMNS (side string); Alternatively, if you are using Glue as you Meta store (which you absolutely should) you can add columns from the Glue console. To describe the table fields, type the following query. It’s been an instinctual part of my life as a Database Administrator who supports OLTP databases that have sold in excess of 20K tickets per minute to your favorite events. When an exact number may not be required―for instance, if you are looking for a rough estimate of the number of New Years events in the Greater New York area then consider using approx_distinct(). We ran the benchmark queries on QDS Presto 0.180. If you’re battling with memory errors then try a distributed hash join. You’ve been WARNED! select SUBSTRING(last_modified,1,4) ,count(*)  from hive.df_machine_learning.event_text where lower(name) like ‘%wilbraham%’ or (REGEXP_LIKE(lower(name), ‘.*wilbraham. Based on some quick performance tests, we see ~30% increase in run-times with REGEXP_LIKE vs comparable LIKE/OR combination. This query is replicated – it moves the data to the detached directory on all replicas. When using GROUP BY, order the columns by the highest cardinality (that is, most number of unique values) to the lowest. Presto is an open source distributed SQL query engine for running interactive analytic queries against data sources of all sizes ranging from gigabytes to petabytes. values, then use a LIMIT clause to reduce the cost of the sort significantly by pushing the sorting/limiting to individual workers, rather than the sorting being done by a single worker. How To Move From Customer Support to Engineering in 5 Steps, Styleguide-Driven Development at Eventbrite: Introduction, Setting the title of AirDrop shares under iOS 7, How to Craft a Successful Engineering Interview, The Elevator Pitch from a Data Strategist, Varnish and A-B Testing: How to Play Nice. CBO inherently requires the table stats be up-to-date which we only calculate for a small subset of tables! Specifically, I’m supporting Eventbrite’s Data Warehouse which leverages Presto and Apache Hive using the Presto/Hive connector. To describe the table fields, type the following query. Thanks for reading and making it to the end. I remember a specific situation where a missing index caused our production databases to get flooded with table-scans that brought a world-wide on-sale to an immediate halt. List the partitions in table, optionally filtered using the WHERE clause, ordered using the ORDER BY clause and limited using the LIMIT clause. Partitions act as virtual columns. When exploring large datasets often an approximation (with standard deviation of 2.3%) is more than good enough! *’)) group by 1 order by 1; that powers Eventbrite’s data warehouse. Presto is a high performance, distributed SQL query engine for big data.It was developed by Facebook, but now is open-source. When I view the column properties, it shows as column: dummy, type: string.
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