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SASIIndex, or SASI for short, is an implementation of Cassandra’s Index interface that can be used as an alternative to the existing implementations. SASI’s indexing and querying improves on existing implementations by tailoring it specifically to Cassandra’s needs. SASI has superior performance in cases where queries would previously require filtering. In achieving this performance, SASI aims to be significantly less resource intensive than existing implementations, in memory, disk, and CPU usage. In addition, SASI supports prefix and contains queries on strings (similar to SQL’s LIKE = "foo*" or LIKE = "**foo**"').
The following goes on describe how to get up and running with SASI, demonstrates usage with examples, and provides some details on its implementation.
Using SASI
The examples below walk through creating a table and indexes on its columns, and performing queries on some inserted data.
The examples below assume the demo keyspace has been created and is in use.
cqlsh> CREATE KEYSPACE demo WITH replication = {... 'class': 'SimpleStrategy',... 'replication_factor': '1'... };cqlsh> USE demo;
All examples are performed on the sasi table:
cqlsh:demo> CREATE TABLE sasi (id uuid, first_name text, last_name text,... age int, height int, created_at bigint, primary key (id));
Creating Indexes
To create SASI indexes use CQLs CREATE CUSTOM INDEX statement:
cqlsh:demo> CREATE CUSTOM INDEX ON sasi (first_name) USING 'org.apache.cassandra.index.sasi.SASIIndex'... WITH OPTIONS = {... 'analyzer_class':... 'org.apache.cassandra.index.sasi.analyzer.NonTokenizingAnalyzer',... 'case_sensitive': 'false'... };cqlsh:demo> CREATE CUSTOM INDEX ON sasi (last_name) USING 'org.apache.cassandra.index.sasi.SASIIndex'... WITH OPTIONS = {'mode': 'CONTAINS'};cqlsh:demo> CREATE CUSTOM INDEX ON sasi (age) USING 'org.apache.cassandra.index.sasi.SASIIndex';cqlsh:demo> CREATE CUSTOM INDEX ON sasi (created_at) USING 'org.apache.cassandra.index.sasi.SASIIndex'... WITH OPTIONS = {'mode': 'SPARSE'};
The indexes created have some options specified that customize their behaviour and potentially performance. The index on first_name is case-insensitive. The analyzers are discussed more in a subsequent example. The NonTokenizingAnalyzer performs no analysis on the text. Each index has a mode: PREFIX, CONTAINS, or SPARSE, the first being the default. The last_name index is created with the mode CONTAINS which matches terms on suffixes instead of prefix only. Examples of this are available below and more detail can be found in the section on OnDiskIndex.The created_at column is created with its mode set to SPARSE, which is meant to improve performance of querying large, dense number ranges like timestamps for data inserted every millisecond. Details of the SPARSE implementation can also be found in the section on the OnDiskIndex. The age index is created with the default PREFIX mode and no case-sensitivity or text analysis options are specified since the field is numeric.
After inserting the following data and performing a nodetool flush, SASI performing index flushes to disk can be seen in Cassandra’s logs – although the direct call to flush is not required (see IndexMemtable for more details).
cqlsh:demo> INSERT INTO sasi (id, first_name, last_name, age, height, created_at)... VALUES (556ebd54-cbe5-4b75-9aae-bf2a31a24500, 'Pavel', 'Yaskevich', 27, 181, 1442959315018);cqlsh:demo> INSERT INTO sasi (id, first_name, last_name, age, height, created_at)... VALUES (5770382a-c56f-4f3f-b755-450e24d55217, 'Jordan', 'West', 26, 173, 1442959315019);cqlsh:demo> INSERT INTO sasi (id, first_name, last_name, age, height, created_at)... VALUES (96053844-45c3-4f15-b1b7-b02c441d3ee1, 'Mikhail', 'Stepura', 36, 173, 1442959315020);cqlsh:demo> INSERT INTO sasi (id, first_name, last_name, age, height, created_at)... VALUES (f5dfcabe-de96-4148-9b80-a1c41ed276b4, 'Michael', 'Kjellman', 26, 180, 1442959315021);cqlsh:demo> INSERT INTO sasi (id, first_name, last_name, age, height, created_at)... VALUES (2970da43-e070-41a8-8bcb-35df7a0e608a, 'Johnny', 'Zhang', 32, 175, 1442959315022);cqlsh:demo> INSERT INTO sasi (id, first_name, last_name, age, height, created_at)... VALUES (6b757016-631d-4fdb-ac62-40b127ccfbc7, 'Jason', 'Brown', 40, 182, 1442959315023);cqlsh:demo> INSERT INTO sasi (id, first_name, last_name, age, height, created_at)... VALUES (8f909e8a-008e-49dd-8d43-1b0df348ed44, 'Vijay', 'Parthasarathy', 34, 183, 1442959315024);cqlsh:demo> SELECT first_name, last_name, age, height, created_at FROM sasi;first_name | last_name | age | height | created_at------------+---------------+-----+--------+---------------Michael | Kjellman | 26 | 180 | 1442959315021Mikhail | Stepura | 36 | 173 | 1442959315020Jason | Brown | 40 | 182 | 1442959315023Pavel | Yaskevich | 27 | 181 | 1442959315018Vijay | Parthasarathy | 34 | 183 | 1442959315024Jordan | West | 26 | 173 | 1442959315019Johnny | Zhang | 32 | 175 | 1442959315022(7 rows)
Equality & Prefix Queries
SASI supports all queries already supported by CQL, including LIKE statement for PREFIX, CONTAINS and SUFFIX searches.
cqlsh:demo> SELECT first_name, last_name, age, height, created_at FROM sasi... WHERE first_name = 'Pavel';first_name | last_name | age | height | created_at-------------+-----------+-----+--------+---------------Pavel | Yaskevich | 27 | 181 | 1442959315018(1 rows)
cqlsh:demo> SELECT first_name, last_name, age, height, created_at FROM sasi... WHERE first_name = 'pavel';first_name | last_name | age | height | created_at-------------+-----------+-----+--------+---------------Pavel | Yaskevich | 27 | 181 | 1442959315018(1 rows)
cqlsh:demo> SELECT first_name, last_name, age, height, created_at FROM sasi... WHERE first_name LIKE 'M%';first_name | last_name | age | height | created_at------------+-----------+-----+--------+---------------Michael | Kjellman | 26 | 180 | 1442959315021Mikhail | Stepura | 36 | 173 | 1442959315020(2 rows)
Of course, the case of the query does not matter for the first_name column because of the options provided at index creation time.
cqlsh:demo> SELECT first_name, last_name, age, height, created_at FROM sasi... WHERE first_name LIKE 'm%';first_name | last_name | age | height | created_at------------+-----------+-----+--------+---------------Michael | Kjellman | 26 | 180 | 1442959315021Mikhail | Stepura | 36 | 173 | 1442959315020(2 rows)
Compound Queries
SASI supports queries with multiple predicates, however, due to the nature of the default indexing implementation, CQL requires the user to specify ALLOW FILTERING to opt-in to the potential performance pitfalls of such a query. With SASI, while the requirement to include ALLOW FILTERING remains, to reduce modifications to the grammar, the performance pitfalls do not exist because filtering is not performed. Details on how SASI joins data from multiple predicates is available below in the Implementation Details section.
cqlsh:demo> SELECT first_name, last_name, age, height, created_at FROM sasi... WHERE first_name LIKE 'M%' and age < 30 ALLOW FILTERING;first_name | last_name | age | height | created_at------------+-----------+-----+--------+---------------Michael | Kjellman | 26 | 180 | 1442959315021(1 rows)
Suffix Queries
The next example demonstrates CONTAINS mode on the last_name column. By using this mode, predicates can search for any strings containing the search string as a sub-string. In this case the strings containing a'' or an’’.
cqlsh:demo> SELECT * FROM sasi WHERE last_name LIKE '%a%';id | age | created_at | first_name | height | last_name--------------------------------------+-----+---------------+------------+--------+---------------f5dfcabe-de96-4148-9b80-a1c41ed276b4 | 26 | 1442959315021 | Michael | 180 | Kjellman96053844-45c3-4f15-b1b7-b02c441d3ee1 | 36 | 1442959315020 | Mikhail | 173 | Stepura556ebd54-cbe5-4b75-9aae-bf2a31a24500 | 27 | 1442959315018 | Pavel | 181 | Yaskevich8f909e8a-008e-49dd-8d43-1b0df348ed44 | 34 | 1442959315024 | Vijay | 183 | Parthasarathy2970da43-e070-41a8-8bcb-35df7a0e608a | 32 | 1442959315022 | Johnny | 175 | Zhang(5 rows)cqlsh:demo> SELECT * FROM sasi WHERE last_name LIKE '%an%';id | age | created_at | first_name | height | last_name--------------------------------------+-----+---------------+------------+--------+-----------f5dfcabe-de96-4148-9b80-a1c41ed276b4 | 26 | 1442959315021 | Michael | 180 | Kjellman2970da43-e070-41a8-8bcb-35df7a0e608a | 32 | 1442959315022 | Johnny | 175 | Zhang(2 rows)
Expressions on Non-Indexed Columns
SASI also supports filtering on non-indexed columns like height. The expression can only narrow down an existing query using AND.
cqlsh:demo> SELECT * FROM sasi WHERE last_name LIKE '%a%' AND height >= 175 ALLOW FILTERING;id | age | created_at | first_name | height | last_name--------------------------------------+-----+---------------+------------+--------+---------------f5dfcabe-de96-4148-9b80-a1c41ed276b4 | 26 | 1442959315021 | Michael | 180 | Kjellman556ebd54-cbe5-4b75-9aae-bf2a31a24500 | 27 | 1442959315018 | Pavel | 181 | Yaskevich8f909e8a-008e-49dd-8d43-1b0df348ed44 | 34 | 1442959315024 | Vijay | 183 | Parthasarathy2970da43-e070-41a8-8bcb-35df7a0e608a | 32 | 1442959315022 | Johnny | 175 | Zhang(4 rows)
Delimiter based Tokenization Analysis
A simple text analysis provided is delimiter based tokenization. This provides an alternative to indexing collections, as delimiter separated text can be indexed without the overhead of CONTAINS mode nor using PREFIX or SUFFIX queries.
cqlsh:demo> ALTER TABLE sasi ADD aliases text;cqlsh:demo> CREATE CUSTOM INDEX on sasi (aliases) USING 'org.apache.cassandra.index.sasi.SASIIndex'... WITH OPTIONS = {... 'analyzer_class': 'org.apache.cassandra.index.sasi.analyzer.DelimiterAnalyzer',... 'delimiter': ',',... 'mode': 'prefix',... 'analyzed': 'true'};cqlsh:demo> UPDATE sasi SET aliases = 'Mike,Mick,Mikey,Mickey' WHERE id = f5dfcabe-de96-4148-9b80-a1c41ed276b4;cqlsh:demo> SELECT * FROM sasi WHERE aliases LIKE 'Mikey' ALLOW FILTERING;id | age | aliases | created_at | first_name | height | last_name--------------------------------------+-----+------------------------+---------------+------------+--------+-----------f5dfcabe-de96-4148-9b80-a1c41ed276b4 | 26 | Mike,Mick,Mikey,Mickey | 1442959315021 | Michael | 180 | Kjellman
Text Analysis (Tokenization and Stemming)
Lastly, to demonstrate text analysis an additional column is needed on the table. Its definition, index, and statements to update rows are shown below.
cqlsh:demo> ALTER TABLE sasi ADD bio text;cqlsh:demo> CREATE CUSTOM INDEX ON sasi (bio) USING 'org.apache.cassandra.index.sasi.SASIIndex'... WITH OPTIONS = {... 'analyzer_class': 'org.apache.cassandra.index.sasi.analyzer.StandardAnalyzer',... 'tokenization_enable_stemming': 'true',... 'analyzed': 'true',... 'tokenization_normalize_lowercase': 'true',... 'tokenization_locale': 'en'... };cqlsh:demo> UPDATE sasi SET bio = 'Software Engineer, who likes distributed systems, doesnt like to argue.' WHERE id = 5770382a-c56f-4f3f-b755-450e24d55217;cqlsh:demo> UPDATE sasi SET bio = 'Software Engineer, works on the freight distribution at nights and likes arguing' WHERE id = 556ebd54-cbe5-4b75-9aae-bf2a31a24500;cqlsh:demo> SELECT * FROM sasi;id | age | bio | created_at | first_name | height | last_name--------------------------------------+-----+----------------------------------------------------------------------------------+---------------+------------+--------+---------------f5dfcabe-de96-4148-9b80-a1c41ed276b4 | 26 | null | 1442959315021 | Michael | 180 | Kjellman96053844-45c3-4f15-b1b7-b02c441d3ee1 | 36 | null | 1442959315020 | Mikhail | 173 | Stepura6b757016-631d-4fdb-ac62-40b127ccfbc7 | 40 | null | 1442959315023 | Jason | 182 | Brown556ebd54-cbe5-4b75-9aae-bf2a31a24500 | 27 | Software Engineer, works on the freight distribution at nights and likes arguing | 1442959315018 | Pavel | 181 | Yaskevich8f909e8a-008e-49dd-8d43-1b0df348ed44 | 34 | null | 1442959315024 | Vijay | 183 | Parthasarathy5770382a-c56f-4f3f-b755-450e24d55217 | 26 | Software Engineer, who likes distributed systems, doesnt like to argue. | 1442959315019 | Jordan | 173 | West2970da43-e070-41a8-8bcb-35df7a0e608a | 32 | null | 1442959315022 | Johnny | 175 | Zhang(7 rows)
Index terms and query search strings are stemmed for the bio column because it was configured to use the StandardAnalyzer and analyzed is set to true. The tokenization_normalize_lowercase is similar to the case_sensitive property but for the StandardAnalyzer. These query demonstrates the stemming applied by StandardAnalyzer.
cqlsh:demo> SELECT * FROM sasi WHERE bio LIKE 'distributing';id | age | bio | created_at | first_name | height | last_name--------------------------------------+-----+----------------------------------------------------------------------------------+---------------+------------+--------+-----------556ebd54-cbe5-4b75-9aae-bf2a31a24500 | 27 | Software Engineer, works on the freight distribution at nights and likes arguing | 1442959315018 | Pavel | 181 | Yaskevich5770382a-c56f-4f3f-b755-450e24d55217 | 26 | Software Engineer, who likes distributed systems, doesnt like to argue. | 1442959315019 | Jordan | 173 | West(2 rows)cqlsh:demo> SELECT * FROM sasi WHERE bio LIKE 'they argued';id | age | bio | created_at | first_name | height | last_name--------------------------------------+-----+----------------------------------------------------------------------------------+---------------+------------+--------+-----------556ebd54-cbe5-4b75-9aae-bf2a31a24500 | 27 | Software Engineer, works on the freight distribution at nights and likes arguing | 1442959315018 | Pavel | 181 | Yaskevich5770382a-c56f-4f3f-b755-450e24d55217 | 26 | Software Engineer, who likes distributed systems, doesnt like to argue. | 1442959315019 | Jordan | 173 | West(2 rows)cqlsh:demo> SELECT * FROM sasi WHERE bio LIKE 'working at the company';id | age | bio | created_at | first_name | height | last_name--------------------------------------+-----+----------------------------------------------------------------------------------+---------------+------------+--------+-----------556ebd54-cbe5-4b75-9aae-bf2a31a24500 | 27 | Software Engineer, works on the freight distribution at nights and likes arguing | 1442959315018 | Pavel | 181 | Yaskevich(1 rows)cqlsh:demo> SELECT * FROM sasi WHERE bio LIKE 'soft eng';id | age | bio | created_at | first_name | height | last_name--------------------------------------+-----+----------------------------------------------------------------------------------+---------------+------------+--------+-----------556ebd54-cbe5-4b75-9aae-bf2a31a24500 | 27 | Software Engineer, works on the freight distribution at nights and likes arguing | 1442959315018 | Pavel | 181 | Yaskevich5770382a-c56f-4f3f-b755-450e24d55217 | 26 | Software Engineer, who likes distributed systems, doesnt like to argue. | 1442959315019 | Jordan | 173 | West(2 rows)
Implementation Details
While SASI, at the surface, is simply an implementation of the Index interface, at its core there are several data structures and algorithms used to satisfy it. These are described here. Additionally, the changes internal to Cassandra to support SASI’s integration are described.
The Index interface divides responsibility of the implementer into two parts: Indexing and Querying. Further, Cassandra makes it possible to divide those responsibilities into the memory and disk components. SASI takes advantage of Cassandra’s write-once, immutable, ordered data model to build indexes along with the flushing of the memtable to disk – this is the origin of the name ``SSTable Attached Secondary Index’’.
The SASI index data structures are built in memory as the SSTable is being written and they are flushed to disk before the writing of the SSTable completes. The writing of each index file only requires sequential writes to disk. In some cases, partial flushes are performed, and later stitched back together, to reduce memory usage. These data structures are optimized for this use case.
Taking advantage of Cassandra’s ordered data model, at query time, candidate indexes are narrowed down for searching, minimizing the amount of work done. Searching is then performed using an efficient method that streams data off disk as needed.
Indexing
Per SSTable, SASI writes an index file for each indexed column. The data for these files is built in memory using the OnDiskIndexBuilder. Once flushed to disk, the data is read using the OnDiskIndex class. These are composed of bytes representing indexed terms, organized for efficient writing or searching respectively. The keys and values they hold represent tokens and positions in an SSTable and these are stored per-indexed term in TokenTreeBuilders for writing, and TokenTrees for querying. These index files are memory mapped after being written to disk, for quicker access. For indexing data in the memtable, SASI uses its IndexMemtable class.
OnDiskIndex(Builder)
Each OnDiskIndex is an instance of a modified Suffix Array data structure. The OnDiskIndex is comprised of page-size blocks of sorted terms and pointers to the terms’ associated data, as well as the data itself, stored also in one or more page-sized blocks. The OnDiskIndex is structured as a tree of arrays, where each level describes the terms in the level below, the final level being the terms themselves. The PointerLevel`s and their `PointerBlock`s contain terms and pointers to other blocks that _end_ with those terms. The `DataLevel, the final level, and its DataBlock`s contain terms and point to the data itself, contained in [`TokenTree](https://github.com/apache/cassandra/blob/trunk/src/java/org/apache/cassandra/index/sasi/disk/TokenTree.java)s.
The terms written to the OnDiskIndex vary depending on its mode'': either `PREFIX`, `CONTAINS`, or `SPARSE`. In the `PREFIX` and `SPARSE` cases, terms’ exact values are written exactly once per `OnDiskIndex`. For example, when using a `PREFIX` index with terms `Jason`, `Jordan`, `Pavel`, all three will be included in the index. A `CONTAINS` index writes additional terms for each suffix of each term recursively. Continuing with the example, a `CONTAINS` index storing the previous terms would also store `ason`, `ordan`, `avel`, `son`, `rdan`, `vel`, etc. This allows for queries on the suffix of strings. The `SPARSE` mode differs from `PREFIX` in that for every 64 blocks of terms a [TokenTree](https://github.com/apache/cassandra/blob/trunk/src/java/org/apache/cassandra/index/sasi/disk/TokenTree.java) is built merging all the [TokenTree](https://github.com/apache/cassandra/blob/trunk/src/java/org/apache/cassandra/index/sasi/disk/TokenTree.java)s for each term into a single one. This copy of the data is used for efficient iteration of large ranges of e.g. timestamps. The index mode’’ is configurable per column at index creation time.
TokenTree(Builder)
The TokenTree is an implementation of the well-known B+-tree that has been modified to optimize for its use-case. In particular, it has been optimized to associate tokens, longs, with a set of positions in an SSTable, also longs. Allowing the set of long values accommodates the possibility of a hash collision in the token, but the data structure is optimized for the unlikely possibility of such a collision.
To optimize for its write-once environment the TokenTreeBuilder completely loads its interior nodes as the tree is built and it uses the well-known algorithm optimized for bulk-loading the data structure.
TokenTrees provide the means to iterate over tokens, and file positions, that match a given term, and to skip forward in that iteration, an operation used heavily at query time.
IndexMemtable
The IndexMemtable handles indexing the in-memory data held in the memtable. The IndexMemtable in turn manages either a TrieMemIndex or a SkipListMemIndex per-column. The choice of which index type is used is data dependent. The TrieMemIndex is used for literal types. AsciiType and UTF8Type are literal types by default but any column can be configured as a literal type using the is_literal option at index creation time. For non-literal types the SkipListMemIndex is used. The TrieMemIndex is an implementation that can efficiently support prefix queries on character-like data. The SkipListMemIndex, conversely, is better suited for other Cassandra data types like numbers.
The TrieMemIndex is built using either the ConcurrentRadixTree or ConcurrentSuffixTree from the com.goooglecode.concurrenttrees package. The choice between the two is made based on the indexing mode, PREFIX or other modes, and CONTAINS mode, respectively.
The SkipListMemIndex is built on top of java.util.concurrent.ConcurrentSkipListSet.
Querying
Responsible for converting the internal IndexExpression representation into SASI’s Operation and Expression trees, optimizing the trees to reduce the amount of work done, and driving the query itself, the QueryPlan is the work horse of SASI’s querying implementation. To efficiently perform union and intersection operations, SASI provides several iterators similar to Cassandra’s MergeIterator, but tailored specifically for SASI’s use while including more features. The RangeUnionIterator, like its name suggests, performs set unions over sets of tokens/keys matching the query, only reading as much data as it needs from each set to satisfy the query. The RangeIntersectionIterator, similar to its counterpart, performs set intersections over its data.
QueryPlan
The QueryPlan instantiated per search query is at the core of SASI’s querying implementation. Its work can be divided in two stages: analysis and execution.
During the analysis phase, QueryPlan converts from Cassandra’s internal representation of IndexExpression`s, which has also been modified to support encoding queries that contain ORs and groupings of expressions using parentheses (see the [Cassandra Internal Changes](#cassandra-internal-changes) section below for more details). This process produces a tree of [`Operation](https://github.com/apache/cassandra/blob/trunk/src/java/org/apache/cassandra/index/sasi/plan/Operation.java)s, which in turn may contain Expressions, all of which provide an alternative, more efficient, representation of the query.
During execution, the QueryPlan uses the DecoratedKey-generating iterator created from the Operation tree. These keys are read from disk and a final check to ensure they satisfy the query is made, once again using the Operation tree. At the point the desired amount of matching data has been found, or there is no more matching data, the result set is returned to the coordinator through the existing internal components.
The number of queries (total/failed/timed-out), and their latencies, are maintined per-table/column family.
SASI also supports concurrently iterating terms for the same index across SSTables. The concurrency factor is controlled by the cassandra.search_concurrency_factor system property. The default is 1.
QueryController
Each QueryPlan references a QueryController used throughout the execution phase. The QueryController has two responsibilities: to manage and ensure the proper cleanup of resources (indexes), and to strictly enforce the time bound per query, specified by the user via the range slice timeout. All indexes are accessed via the QueryController so that they can be safely released by it later. The QueryController’s checkpoint function is called in specific places in the execution path to ensure the time-bound is enforced.
QueryPlan Optimizations
While in the analysis phase, the QueryPlan performs several potential optimizations to the query. The goal of these optimizations is to reduce the amount of work performed during the execution phase.
The simplest optimization performed is compacting multiple expressions joined by logical intersections (AND) into a single Operation with three or more Expressions. For example, the query WHERE age < 100 AND fname = 'p*' AND first_name != 'pa*' AND age > 21 would, without modification, have the following tree:
┌───────┐┌────────│ AND │──────┐│ └───────┘ │▼ ▼┌───────┐ ┌──────────┐┌─────│ AND │─────┐ │age < 100 ││ └───────┘ │ └──────────┘▼ ▼┌──────────┐ ┌───────┐│ fname=p* │ ┌─│ AND │───┐└──────────┘ │ └───────┘ │▼ ▼┌──────────┐ ┌──────────┐│fname!=pa*│ │ age > 21 │└──────────┘ └──────────┘
QueryPlan will remove the redundant right branch whose root is the final AND and has leaves fname != pa* and age > 21. These Expressions will be compacted into the parent AND, a safe operation due to AND being associative and commutative. The resulting tree looks like the following:
┌───────┐┌────────│ AND │──────┐│ └───────┘ │▼ ▼┌───────┐ ┌──────────┐┌───────────│ AND │────────┐ │age < 100 ││ └───────┘ │ └──────────┘▼ │ ▼┌──────────┐ │ ┌──────────┐│ fname=p* │ ▼ │ age > 21 │└──────────┘ ┌──────────┐ └──────────┘│fname!=pa*│└──────────┘
When excluding results from the result set, using !=, the QueryPlan determines the best method for handling it. For range queries, for example, it may be optimal to divide the range into multiple parts with a hole for the exclusion. For string queries, such as this one, it is more optimal, however, to simply note which data to skip, or exclude, while scanning the index. Following this optimization the tree looks like this:
┌───────┐┌────────│ AND │──────┐│ └───────┘ │▼ ▼┌───────┐ ┌──────────┐┌───────│ AND │────────┐ │age < 100 ││ └───────┘ │ └──────────┘▼ ▼┌──────────────────┐ ┌──────────┐│ fname=p* │ │ age > 21 ││ exclusions=[pa*] │ └──────────┘└──────────────────┘
The last type of optimization applied, for this query, is to merge range expressions across branches of the tree – without modifying the meaning of the query, of course. In this case, because the query contains all AND`s the `age expressions can be collapsed. Along with this optimization, the initial collapsing of unneeded `AND`s can also be applied once more to result in this final tree using to execute the query:
┌───────┐┌──────│ AND │───────┐│ └───────┘ │▼ ▼┌──────────────────┐ ┌────────────────┐│ fname=p* │ │ 21 < age < 100 ││ exclusions=[pa*] │ └────────────────┘└──────────────────┘
Operations and Expressions
As discussed, the QueryPlan optimizes a tree represented by Operations as interior nodes, and Expressions as leaves. The Operation class, more specifically, can have zero, one, or two Operations as children and an unlimited number of expressions. The iterators used to perform the queries, discussed below in the `Range(Union|Intersection)Iterator'' section, implement the necessary logic to merge results transparently regardless of the [`Operation](https://github.com/apache/cassandra/blob/trunk/src/java/org/apache/cassandra/index/sasi/plan/Operation.java)s children.
Besides participating in the optimizations performed by the QueryPlan, Operation is also responsible for taking a row that has been returned by the query and performing a final validation that it in fact does match. This satisfiesBy operation is performed recursively from the root of the Operation tree for a given query. These checks are performed directly on the data in a given row. For more details on how satisfiesBy works, see the documentation in the code.
Range(Union|Intersection)Iterator
The abstract RangeIterator class provides a unified interface over the two main operations performed by SASI at various layers in the execution path: set intersection and union. These operations are performed in a iterated, or ``streaming’’, fashion to prevent unneeded reads of elements from either set. In both the intersection and union cases the algorithms take advantage of the data being pre-sorted using the same sort order, e.g. term or token order.
The RangeUnionIterator performs the ``Merge-Join’’ portion of the Sort-Merge-Join algorithm, with the properties of an outer-join, or union. It is implemented with several optimizations to improve its performance over a large number of iterators – sets to union. Specifically, the iterator exploits the likely case of the data having many sub-groups of overlapping ranges and the unlikely case that all ranges will overlap each other. For more details see the javadoc.
The RangeIntersectionIterator itself is not a subclass of RangeIterator. It is a container for several classes, one of which, AbstractIntersectionIterator, sub-classes RangeIterator. SASI supports two methods of performing the intersection operation, and the ability to be adaptive in choosing between them based on some properties of the data.
BounceIntersectionIterator, and the BOUNCE strategy, works like the RangeUnionIterator in that it performs a ``Merge-Join’’, however, its nature is similar to a inner-join, where like values are merged by a data-specific merge function (e.g. merging two tokens in a list to lookup in a SSTable later). See the javadoc for more details on its implementation.
LookupIntersectionIterator, and the LOOKUP strategy, performs a different operation, more similar to a lookup in an associative data structure, or ``hash lookup’’ in database terminology. Once again, details on the implementation can be found in the javadoc.
The choice between the two iterators, or the ADAPTIVE strategy, is based upon the ratio of data set sizes of the minimum and maximum range of the sets being intersected. If the number of the elements in minimum range divided by the number of elements is the maximum range is less than or equal to 0.01, then the ADAPTIVE strategy chooses the LookupIntersectionIterator, otherwise the BounceIntersectionIterator is chosen.
The SASIIndex Class
The above components are glued together by the SASIIndex class which implements Index, and is instantiated per-table containing SASI indexes. It manages all indexes for a table via the sasi.conf.DataTracker and sasi.conf.view.View components, controls writing of all indexes for an SSTable via its PerSSTableIndexWriter, and initiates searches with Searcher. These classes glue the previously mentioned indexing components together with Cassandra’s SSTable life-cycle ensuring indexes are not only written when Memtable’s flush, but also as SSTable’s are compacted. For querying, the Searcher does little but defer to QueryPlan and update e.g. latency metrics exposed by SASI.
Cassandra Internal Changes
To support the above changes and integrate them into Cassandra a few minor internal changes were made to Cassandra itself. These are described here.
SSTable Write Life-cycle Notifications
The SSTableFlushObserver is an observer pattern-like interface, whose sub-classes can register to be notified about events in the life-cycle of writing out a SSTable. Sub-classes can be notified when a flush begins and ends, as well as when each next row is about to be written, and each next column. SASI’s PerSSTableIndexWriter, discussed above, is the only current subclass.
Limitations and Caveats
The following are items that can be addressed in future updates but are not available in this repository or are not currently implemented.
The cluster must be configured to use a partitioner that produces
LongToken`s, e.g. `Murmur3Partitioner. Other existing partitioners which don’t produce LongToken e.g.ByteOrderedPartitionerandRandomPartitionerwill not work with SASI.Not Equals and OR support have been removed in this release while changes are made to Cassandra itself to support them.
