As an open-source real-time data warehouse, provides a rich choice of indexes to speed up data scanning and filtering. Based on user involvement, they can be divided into built-in smart indexes and user-created indexes. Apache Doris automatically generates the former on data ingestion, such as ZoneMap index and prefix index, while the latter is the index users choose for various use cases, including inverted index and NGram BloomFilter index. Apache Doris This post is a deep dive into the inverted index and NGram BloomFilter index, providing a hands-on guide to applying them for various queries. Sample dataset The test dataset comprises about 130 million Amazon customer reviews. It is a few Snappy-compressed Parquet files with a total size of 37GB. These are a few samples: Each row includes 15 columns, including , , , , , , and . customer_id review_id product_id product_category star_rating review_headline review_body A lot of these columns can be accelerated by indexes based on their structures. For example, is a high-cardinality numerical field, while is a low-cardinality fixed-length text field, and and are short and long text fields, respectively. customer_id product_id product_title review_body Queries on these columns can be roughly divided into two types: : searches for certain contents in the field. Text searches review_body : query reviews about certain or from certain . Non-primary key column queries product_id customer_id These are also the main threads of this article. I will present to you how indexes can speed up these queries. Prerequisites For a quick run, here we use a single-node cluster (1 frontend, 1 backend). Deploy Apache Doris: refer to Quick Start Create a table using the following statements: CREATE TABLE `amazon_reviews` ( `review_date` int(11) NULL, `marketplace` varchar(20) NULL, `customer_id` bigint(20) NULL, `review_id` varchar(40) NULL, `product_id` varchar(10) NULL, `product_parent` bigint(20) NULL, `product_title` varchar(500) NULL, `product_category` varchar(50) NULL, `star_rating` smallint(6) NULL, `helpful_votes` int(11) NULL, `total_votes` int(11) NULL, `vine` boolean NULL, `verified_purchase` boolean NULL, `review_headline` varchar(500) NULL, `review_body` string NULL ) ENGINE=OLAP DUPLICATE KEY(`review_date`) COMMENT 'OLAP' DISTRIBUTED BY HASH(`review_date`) BUCKETS 16 PROPERTIES ( "replication_allocation" = "tag.location.default: 1", "compression" = "ZSTD" ); Download datasets: Snappy-compressed Parquet files with a total size of 37GB [2] amazon_reviews_2010 [3] amazon_reviews_2011 [4] amazon_reviews_2012 [5] amazon_reviews_2013 [6] amazon_reviews_2014 [7] amazon_reviews_2015 Execute the following commands to load the datasets. curl --location-trusted -u root: -T amazon_reviews_2010.snappy.parquet -H "format:parquet" http://${BE_IP}:${BE_PORT}/api/${DB}/amazon_reviews/_stream_load curl --location-trusted -u root: -T amazon_reviews_2011.snappy.parquet -H "format:parquet" http://${BE_IP}:${BE_PORT}/api/${DB}/amazon_reviews/_stream_load curl --location-trusted -u root: -T amazon_reviews_2012.snappy.parquet -H "format:parquet" http://${BE_IP}:${BE_PORT}/api/${DB}/amazon_reviews/_stream_load curl --location-trusted -u root: -T amazon_reviews_2013.snappy.parquet -H "format:parquet" http://${BE_IP}:${BE_PORT}/api/${DB}/amazon_reviews/_stream_load curl --location-trusted -u root: -T amazon_reviews_2014.snappy.parquet -H "format:parquet" http://${BE_IP}:${BE_PORT}/api/${DB}/amazon_reviews/_stream_load curl --location-trusted -u root: -T amazon_reviews_2015.snappy.parquet -H "format:parquet" http://${BE_IP}:${BE_PORT}/api/${DB}/amazon_reviews/_stream_load Check and verify: After the above steps, execute the following statements in the MySQL client to check and see the size of the dataset. It can be seen from below that 135589433 rows are loaded, and they take up 25.873GB in Apache Doris, which is 30% smaller than the original Parquet files. mysql> SELECT COUNT() FROM amazon_reviews; +-----------+ | count(*) | +-----------+ | 135589433 | +-----------+ 1 row in set (0.02 sec) mysql> SHOW DATA FROM amazon_reviews; +----------------+----------------+-----------+--------------+-----------+------------+ | TableName | IndexName | Size | ReplicaCount | RowCount | RemoteSize | +----------------+----------------+-----------+--------------+-----------+------------+ | amazon_reviews | amazon_reviews | 25.873 GB | 16 | 135589433 | 0.000 | | | Total | 25.873 GB | 16 | | 0.000 | +----------------+----------------+-----------+--------------+-----------+------------+ 2 rows in set (0.00 sec) Accelerate text searches No index Now, let's try running text searches on the field. Specifically, we're trying to retrieve the top 5 products whose reviews include the keywords "is super awesome.” The results should be sorted in descending order based on the number of reviews. Each result should include the product ID, a randomly selected product title, the average star rating, and the total number of reviews. review_body This is the query statement: SELECT product_id, any(product_title), AVG(star_rating) AS rating, COUNT() AS count FROM amazon_reviews WHERE review_body LIKE '%is super awesome%' GROUP BY product_id ORDER BY count DESC, rating DESC, product_id LIMIT 5; Since the field contains lengthy reviews, such text searches can be time-consuming. Without enabling any indexes, it took to return the results: review_body 7.6 seconds +------------+------------------------------------------+--------------------+-------+ | product_id | any_value(product_title) | rating | count | +------------+------------------------------------------+--------------------+-------+ | B00992CF6W | Minecraft | 4.8235294117647056 | 17 | | B009UX2YAC | Subway Surfers | 4.7777777777777777 | 9 | | B00DJFIMW6 | Minion Rush: Despicable Me Official Game | 4.875 | 8 | | B0086700CM | Temple Run | 5 | 6 | | B00KWVZ750 | Angry Birds Epic RPG | 5 | 6 | +------------+------------------------------------------+--------------------+-------+ 5 rows in set (7.60 sec) NGram BloomFilter index Now, let's try accelerating such text searches using the NGram BloomFilter index. : the value of "N" in "NGram,” representing the length of consecutive characters. In the snippet below, means that the texts will be divided into a number of 10-character strings, which are the basis of the NGram BloomFilter index. gram_size "gram_size"="10" : the size of the BloomFilter in bytes. indicates that the BloomFilter occupies 10240 bytes of space. bf_size "bf_size"="10240" ALTER TABLE amazon_reviews ADD INDEX review_body_ngram_idx(review_body) USING NGRAM_BF PROPERTIES("gram_size"="10", "bf_size"="10240"); This time, the query is finished within . That means NGram BloomFilter brings a speedup of . 0.93 seconds 8 times +------------+------------------------------------------+--------------------+-------+ | product_id | any_value(product_title) | rating | count | +------------+------------------------------------------+--------------------+-------+ | B00992CF6W | Minecraft | 4.8235294117647056 | 17 | | B009UX2YAC | Subway Surfers | 4.7777777777777777 | 9 | | B00DJFIMW6 | Minion Rush: Despicable Me Official Game | 4.875 | 8 | | B0086700CM | Temple Run | 5 | 6 | | B00KWVZ750 | Angry Birds Epic RPG | 5 | 6 | +------------+------------------------------------------+--------------------+-------+ 5 rows in set (0.93 sec) The way it works can be explained in two parts. So how does NGram BloomFilter do the magic? : When , the phrase "hello world" is split into ["hello", "ello ", "llo w", "lo wo", "o wor", " worl", "world"]. These sub-strings are then hashed and added to a BloomFilter of the . Since data in Apache Doris is stored by page, the BloomFilters are generated also by page. NGram tokenization gram_size=5 bf_size : For example, to query the word "hello" in the texts, "hello" is tokenized and compared with the BloomFilters of each page. If the BloomFilter detects a potential match (there might be false positives) in a page, that page is loaded for further matching. Otherwise, that page is skipped. Query acceleration By skipping the irrelevant pages, the BloomFilter index reduces unnecessary data scanning and thus greatly reduces query latency. How to find the optimal parameter configurations for NGram BloomFilter? determines the matching efficiency while impacts the false positive rate. Typically, a large reduces the false positive rate but also requires more storage space. Thus, we suggest that you configure these two parameters based on these two factors: gram_size bf_size bf_size Text length: For short texts (words or phrases), a small (2~4) and a small are recommended. gram_size bf_size For long texts (sentences or paragraphs), a large (5~10) and a large work better. gram_size bf_size Query pattern: If the queries often involve phrases or complete words, a large will be more efficient. gram_size For fuzzy matching or diverse queries, a small allows more flexible matching. gram_size Inverted index is another way to accelerate text searches. Creating an inverted index is simple: Inverted index : Refer to the snippet below to create inverted index for the column of the table. An inverted index supports phrase searching, in which the order of the tokenized words will affect the search results. Add inverted index review_body amazon_reviews : You can also create an inverted index for historical data. Add inverted index for historical data ALTER TABLE amazon_reviews ADD INDEX review_body_inverted_idx(`review_body`) USING INVERTED PROPERTIES("parser" = "english","support_phrase" = "true"); BUILD INDEX review_body_inverted_idx ON amazon_reviews; : You can check and see the created indexes using the following statement: Check and verify mysql> show BUILD INDEX WHERE TableName="amazon_reviews"; +-------+----------------+----------------+-----------------------------------------------------------------------------------------------------------------------------------+-------------------------+-------------------------+---------------+----------+------+----------+ | JobId | TableName | PartitionName | AlterInvertedIndexes | CreateTime | FinishTime | TransactionId | State | Msg | Progress | +-------+----------------+----------------+-----------------------------------------------------------------------------------------------------------------------------------+-------------------------+-------------------------+---------------+----------+------+----------+ | 10152 | amazon_reviews | amazon_reviews | [ADD INDEX review_body_inverted_idx ( review_body ) USING INVERTED PROPERTIES("parser" = "english", "support_phrase" = "true")], | 2024-01-23 15:42:28.658 | 2024-01-23 15:48:42.990 | 11 | FINISHED | | NULL | +-------+----------------+----------------+-----------------------------------------------------------------------------------------------------------------------------------+-------------------------+-------------------------+---------------+----------+------+----------+ 1 row in set (0.00 sec) If you want to see how tokenization works, you can test with the function. Just input the text that needs to be tokenized and the parameters: TOKENIZE mysql> SELECT TOKENIZE('I can honestly give the shipment and package 100%, it came in time that it was supposed to with no hasels, and the book was in PERFECT condition. super awesome buy, and excellent for my college classs', '"parser" = "english","support_phrase" = "true"'); +---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ | tokenize('I can honestly give the shipment and package 100%, it came in time that it was supposed to with no hasels, and the book was in PERFECT condition. super awesome buy, and excellent for my college classs', '"parser" = "english","support_phrase" = "true"') | +---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ | ["i", "can", "honestly", "give", "the", "shipment", "and", "package", "100", "it", "came", "in", "time", "that", "it", "was", "supposed", "to", "with", "no", "hasels", "and", "the", "book", "was", "in", "perfect", "condition", "super", "awesome", "buy", "and", "excellent", "for", "my", "college", "classs"] | +---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ 1 row in set (0.05 sec) With an inverted index, we now retrieve customer reviews containing "is super awesome" using . MATCH_PHRASE SELECT product_id, any(product_title), AVG(star_rating) AS rating, COUNT() AS count FROM amazon_reviews WHERE review_body MATCH_PHRASE 'is super awesome' GROUP BY product_id ORDER BY count DESC, rating DESC, product_id LIMIT 5; The clause searches for text fragments in the column that contains all three keywords "is", "super", and "awesome" in that exact order, with no other words in between. review_body MATCH_PHRASE 'is super awesome' review_body The query is case-insensitive, which is also what sets it apart from the query. The query is more efficient in large datasets. MATCH LIKE MATCH Results show that inverted index has decreased the query latency to , bringing a compared to the NGram BloomFilter index, and compared to having no indexes at all. 0.19 seconds 4-time performance increase a nearly 40-time increase +------------+------------------------------------------+-------------------+-------+ | product_id | any_value(product_title) | rating | count | +------------+------------------------------------------+-------------------+-------+ | B00992CF6W | Minecraft | 4.833333333333333 | 18 | | B009UX2YAC | Subway Surfers | 4.7 | 10 | | B00DJFIMW6 | Minion Rush: Despicable Me Official Game | 5 | 7 | | B0086700CM | Temple Run | 5 | 6 | | B00KWVZ750 | Angry Birds Epic RPG | 5 | 6 | +------------+------------------------------------------+-------------------+-------+ 5 rows in set (0.19 sec) How does an inverted index make it possible? An inverted index splits the texts into words and maps each word to a row number. Then, the tokenized words are sorted alphabetically, and a skip list index is created. When executing queries of specific words, the system locates the row numbers in this orderly mapping using the skip list index and binary search methods. Based on the row numbers, the system retrieves the entire data record. This approach avoids line-by-line matching and reduces computational complexity from O(n) to O(logn). That's how an inverted index speeds up queries on large datasets. To provide a deeper understanding of the inverted index, I will start from its read/write logic. In Doris, logically, an inverted index is applied at the column level of a table. However, from a physical storage and implementation perspective, it is actually built on data files. : When data is written to a data file, it is also synchronously written to the inverted index file, and the row numbers are matched. Writing : In a query, if the condition involves a column for which an inverted index has been built, Doris will go directly to the index file and returns the corresponding row numbers. Then, based on the row numbers, it skips the irrelevant pages and rows and only reads the target rows. Query WHERE In short, an inverted index enables high-speed text searches by mapping, and its implementation relies on the coordination of data files and index files. Accelerate non-primary key column queries To showcase the impact of the inverted index on non-primary key column queries, let's try some multi-dimensional queries. No index Retrieve the review from Customer ID 13916588 about Product ID B002DMK1R0. Without indexes, the system has to scan the entire table. The query is finished within . 1.81 seconds mysql> SELECT product_title,review_headline,review_body,star_rating FROM amazon_reviews WHERE product_id='B002DMK1R0' AND customer_id=13916588; +-----------------------------------------------------------------+----------------------+-----------------------------------------------------------------------------------------------------------------------------+-------------+ | product_title | review_headline | review_body | star_rating | +-----------------------------------------------------------------+----------------------+-----------------------------------------------------------------------------------------------------------------------------+-------------+ | Magellan Maestro 4700 4.7-Inch Bluetooth Portable GPS Navigator | Nice Features But... | This is a great GPS. Gets you where you are going. Don't forget to buy the seperate (grr!) cord for the traffic kit though! | 4 | +-----------------------------------------------------------------+----------------------+-----------------------------------------------------------------------------------------------------------------------------+-------------+ 1 row in set (1.81 sec) Inverted index This query is executed in a different way than what is said above because the system does not have to tokenize the and , but creates a Value→RowID inverted index table. product_id customer_id First of all, create an inverted index via the following statement: ALTER TABLE amazon_reviews ADD INDEX product_id_inverted_idx(product_id) USING INVERTED ; ALTER TABLE amazon_reviews ADD INDEX customer_id_inverted_idx(customer_id) USING INVERTED ; BUILD INDEX product_id_inverted_idx ON amazon_reviews; BUILD INDEX customer_id_inverted_idx ON amazon_reviews; With an inverted index, the same query is finished within . That represents a higher speed compared to the previous 1.81 seconds. 0.06 seconds 30-time mysql> SELECT product_title,review_headline,review_body,star_rating FROM amazon_reviews WHERE product_id='B002DMK1R0' AND customer_id='13916588'; +-----------------------------------------------------------------+----------------------+-----------------------------------------------------------------------------------------------------------------------------+-------------+ | product_title | review_headline | review_body | star_rating | +-----------------------------------------------------------------+----------------------+-----------------------------------------------------------------------------------------------------------------------------+-------------+ | Magellan Maestro 4700 4.7-Inch Bluetooth Portable GPS Navigator | Nice Features But... | This is a great GPS. Gets you where you are going. Don't forget to buy the seperate (grr!) cord for the traffic kit though! | 4 | +-----------------------------------------------------------------+----------------------+-----------------------------------------------------------------------------------------------------------------------------+-------------+ 1 row in set (0.06 sec) Profile This is an excerpt of the SegmentIterator Profile, from which you can tell why an inverted index accelerates query execution. (Note that if you need to check the Profile of a query, make sure you have executed in the MySQL client before you execute the query. Then you can check the Profile at ) SET enable_profile=true; http://FE_IP:FE_HTTP_PORT/QueryProfile SegmentIterator: - FirstReadSeekCount: 0 - FirstReadSeekTime: 0ns - FirstReadTime: 13.119ms - IOTimer: 19.537ms - InvertedIndexQueryTime: 11.583ms - RawRowsRead: 1 - RowsConditionsFiltered: 0 - RowsInvertedIndexFiltered: 16.907403M (16907403) - RowsShortCircuitPredInput: 0 - RowsVectorPredFiltered: 0 - RowsVectorPredInput: 0 - ShortPredEvalTime: 0ns - TotalPagesNum: 27 - UncompressedBytesRead: 3.71 MB - VectorPredEvalTime: 0ns and means that the inverted index has filtered out 16907403 rows and only reads 1 row (the target row). means that it takes 13.119 ms to read the page where the target row is located, and means that the system . RowsInvertedIndexFiltered: 16.907403M (16907403) RawRowsRead: 1 FirstReadTime: 13.119ms InvertedIndexQueryTime: 11.583ms filters out 16907403 rows within only 11.58 ms For comparison, this is the SegmentIterator Profile when no index is used: SegmentIterator: - FirstReadSeekCount: 9.374K (9374) - FirstReadSeekTime: 400.522ms - FirstReadTime: 3s144ms - IOTimer: 2s564ms - InvertedIndexQueryTime: 0ns - RawRowsRead: 16.680706M (16680706) - RowsConditionsFiltered: 226.698K (226698) - RowsInvertedIndexFiltered: 0 - RowsShortCircuitPredInput: 1 - RowsVectorPredFiltered: 16.680705M (16680705) - RowsVectorPredInput: 16.680706M (16680706) - RowsZonemapFiltered: 226.698K (226698) - ShortPredEvalTime: 2.723ms - TotalPagesNum: 5.421K (5421) - UncompressedBytesRead: 277.05 MB - VectorPredEvalTime: 8.114ms Without inverted index, it takes 3.14s to load 16680706 rows ( ). Then, the system conducts filtering by Predicate Evaluate and screens out 16680705 rows. The conditional filtering process only takes less than 10ms, making original data loading the most time-consuming task. FirstReadTime: 3s144ms To sum up, inverted index increases query execution efficiency by enabling quick retrieval of the target rows and thus reducing unnecessary data loading. Accelerate low-cardinality text column queries So, inverted index is a big accelerator for queries on high-cardinality text columns, but that might raise a concern: For low-cardinality columns, will too many indexes bring excessive overheads and undermine query performance? The answer is no. Let me show you why and how. The following example uses as the predicate column for filtering. product_category mysql> SELECT COUNT(DISTINCT product_category) FROM amazon_reviews ; +----------------------------------+ | count(DISTINCT product_category) | +----------------------------------+ | 43 | +----------------------------------+ 1 row in set (0.57 sec) As is shown, the column has only 43 distinct categories, making it a typical low-cardinality text column. Now, let's add an inverted index to it. product_category ALTER TABLE amazon_reviews ADD INDEX product_category_inverted_idx(`product_category`) USING INVERTED; BUILD INDEX product_category_inverted_idx ON amazon_reviews; After adding an inverted index, run the following SQL query to retrieve the top 3 products with the most reviews in the "Mobile_Electronics" product category. SELECT product_id, product_title, AVG(star_rating) AS rating, any(review_body), any(review_headline), COUNT(*) AS count FROM amazon_reviews WHERE product_category = 'Mobile_Electronics' GROUP BY product_title, product_id ORDER BY count DESC LIMIT 10; With inverted index, the query takes 1.54s to finish. +------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+--------------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+---------------------------------+-------+ | product_id | product_title | rating | any_value(review_body) | any_value(review_headline) | count | +------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+--------------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+---------------------------------+-------+ | B00J46XO9U | iXCC Lightning Cable 3ft, iPhone charger, for iPhone X, 8, 8 Plus, 7, 7 Plus, 6s, 6s Plus, 6, 6 Plus, SE 5s 5c 5, iPad Air 2 Pro, iPad mini 2 3 4, iPad 4th Gen [Apple MFi Certified](Black and White) | 4.3766233766233764 | Great cable and works well. Exact fit as Apple cable. I would recommend this to anyone who is looking to save money and for a quality cable. | Apple certified lightning cable | 1078 | | B004911E9M | Wall AC Charger USB Sync Data Cable for iPhone 4, 3GS, and iPod | 2.4281805745554035 | A total waste of money for me because I needed it for a iPhone 4. The plug will only go in upside down and thus won't work at all. | Won't work with a iPhone 4! | 731 | | B002D4IHYM | New Trent Easypak 7000mAh Portable Triple USB Port External Battery Charger/Power Pack for Smartphones, Tablets and more (w/built-in USB cable) | 4.5216095380029806 | I bought this product based on the reviews that i read and i am very glad that i did. I did have a problem with the product charging my itouch after i received it but i emailed the company and they corrected the problem immediately. VERY GOOD customer service, very prompt. The product itself is very good. It charges my power hungry itouch very quickly and the imax battery power lasts for a long time. All in all a very good purchase that i would recommend to anyone who owns an itouch. | Great product & company | 671 | +------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+--------------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+---------------------------------+-------+ 3 rows in set (1.54 sec) Now, let's try again without enabling an inverted index. The same query takes 1.8s to finish. (You can simply disable the inverted index by executing in the MySQL client.) set enable_inverted_index_query=false; +------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+--------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+---------------------------------------+-------+ | product_id | product_title | rating | any_value(review_body) | any_value(review_headline) | count | +------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+--------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+---------------------------------------+-------+ | B00J46XO9U | iXCC Lightning Cable 3ft, iPhone charger, for iPhone X, 8, 8 Plus, 7, 7 Plus, 6s, 6s Plus, 6, 6 Plus, SE 5s 5c 5, iPad Air 2 Pro, iPad mini 2 3 4, iPad 4th Gen [Apple MFi Certified](Black and White) | 4.3766233766233764 | These cables are great. They feel quality, and best of all, they work as they should. I have no issues with them whatsoever and will be buying more when needed. | Just like the original from Apple | 1078 | | B004911E9M | Wall AC Charger USB Sync Data Cable for iPhone 4, 3GS, and iPod | 2.4281805745554035 | I ordered two of these chargers for an Iphone 4. Then I started experiencing weird behavior from the touch screen. It would select the wrong area of the screen, or it would refuse to scroll beyond a certain point and jump back up to the top of the page. This behavior occurs whenever either of the two that I bought are attached and charging. When I remove them, it works fine once again. Needless to say, these items are being returned. | Beware - these chargers are defective | 731 | | B002D4IHYM | New Trent Easypak 7000mAh Portable Triple USB Port External Battery Charger/Power Pack for Smartphones, Tablets and more (w/built-in USB cable) | 4.5216095380029806 | I received this in the mail 4 days ago, and after charging it for 6 hours, I've been using it as the sole source for recharging my 3Gs to see how long it would work. I use my Iphone A LOT every day and usually by the time I get home it's down to 50% or less. After 4 days of using the IMAX to recharge my Iphone, it finally went from 3 bars to 4 this afternoon when I plugged my iphone in. It charges the iphone very quickly, and I've been topping my phone off (stopping around 95% or so) twice a day. This is a great product and the size is very similar to a deck of cards (not like an iphone that someone else posted) and is very easy to carry in a jacket pocket or back pack. I bought this for a 4 day music festival I'm going to, and I have no worries at all of my iphone running out of juice! | FANTASTIC product! | 671 | +------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+--------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+---------------------------------------+-------+ 3 rows in set (1.80 sec) To sum up, an inverted index can bring a 15% speedup for queries on low-cardinality columns. So it is not only harmless but also beneficial to low-cardinality data filtering. In addition, Apache Doris adopts effective dictionary encoding and compression for low-cardinality columns. It also utilizes built-in indexes like ZoneMap for filtering. Thus, it can deliver ideal query performance even without inverted indexes. Conclusion Inverted index in Apache Doris optimizes data filtering based on the predicate column (the clause in SQL queries). It reduces unnecessary data scanning, significantly increases query speed on high-cardinality columns, and guarantees no negative effects on low-cardinality columns. It supports lightweight index management, including ADD/DROP INDEX and BUILD INDEX. It can be easily enabled or disabled via . WHERE enable_inverted_index_query=true/false Inverted index and NGram BloomFilter index apply to different scenarios. This is how you decide which one is the optimal choice: : These cases often involve widely scattered values and a low hit rate. can work in conjunction with the built-in smart indexes in Doris to accelerate these queries. It has well-established support for scalar data types, including characters, numerics, and datetime. Non-primary key column queries Inverted indexes : If the dataset includes short texts that are highly diverse, will be an effective choice for fuzzy matching ( ) on short texts. If the short texts are very similar (with lots of identical content), will be more efficient because it ensures a smaller dictionary and faster retrieval of the row numbers. Text searches on short texts NGram BloomFilter LIKE inverted index : Inverted index is a better choice for long texts. Compared to brutal-force string matching, it largely reduces CPU resource consumption. Text searches on long texts The inverted index has been available in Apache Doris for almost a year and has stood the test of many users in their production environment with massive data. In future versions of Apache Doris regarding inverted index, we plan to add support for: : provides a user-defined tokenizer to fit in different use cases. Self-defined tokenization : Users will be able to create inverted indexes for complex data types, including Array and Map. More data types If you encounter any issues while trying it out in Apache Doris or would like to know more details, join our community and talk to us! Slack Also published . here
