Understanding N1QL Optimizer to Tune Queries

©2016 Couchbase Inc.
Understand Query Optimizer to Tune Queries
SitaramVemulapalli | Senior Software Engineer,Couchbase R&D | sitaram.vemulapalli@couchbase.com
Keshav Murthy | Director, Couchbase R&D | keshav@couchbase.com
1

©2016 Couchbase Inc.©2016 Couchbase Inc.
Agenda
• Brief Introduction to N1QL
• Understand Query Plan
• How Query Plan Generated
• Query + Indexing features
• Q&A
2

©2016 Couchbase Inc. 3
Introduction to N1QL

SQL
4
ResultSet
Input: Relations Output: Relation

NoSQL
5
{
"Name" : "Jane Smith",
"DOB" : "1990-01-30",
"Billing" : [
{
"type" : "visa",
"cardnum" : "5827-2842-2847-3909",
"expiry" : "2019-03"
},
{
"type" : "master",
"cardnum" : "6274-2842-2847-3909",
"expiry" : "2019-03"
}
],
"Connections" : [
{
"CustId" : "XYZ987",
"Name" : "Joe Smith"
},
{
"CustId" : "PQR823",
"Name" : "Dylan Smith"
}
{
}
],
"Purchases" : [
{ "id":12, item: "mac", "amt": 2823.52 }
{ "id":19, item: "ipad2", "amt": 623.52 }
]
}
LoyaltyInfo ResultDocuments
Orders
CUSTOMER
Built Manually; Expensive
Input: JSON Documents Output: JSON Documents

N1QL Changes the Possibilities for NoSQL

NoSQL
{
"Name" : "Jane Smith",
"DOB" : "1990-01-30",
"Billing" : [
{
"type" : "visa",
"cardnum" : "5827-2842-2847-3909",
"expiry" : "2019-03"
},
{
"type" : "master",
"cardnum" : "6274-2842-2847-3909",
"expiry" : "2019-03"
}
],
"Connections" : [
{
"CustId" : "XYZ987",
"Name" : "Joe Smith"
},
{
}
{
}
],
"Purchases" : [
{ "id":12, item: "mac", "amt": 2823.52 }
{ "id":19, item: "ipad2", "amt": 623.52 }
]
}
LoyaltyInfo ResultDocuments
Orders
CUSTOMER
Input: JSON Documents Output: JSON Documents

Goal of N1QL: SQL for JSON
Give developers and enterprises an
expressive, powerful, and complete language
for querying, transforming, and manipulating
JSON data.

©2016 Couchbase Inc.©2016 Couchbase Inc. 9
SELECT Customers.ID,
Customers.Name,
SUM(OrderLine.Amount)
FROM Orders UNNEST
Orders.LineItems AS OrderLine
JOIN Customers ON KEYS Orders.CustID
GROUP BY Customers.ID, Customers.Name
HAVING SUM(OrderLine.Amount) > 10000
ORDER BY SUM(OrderLine.Amount) DESC
LIMIT 100 ;

Understanding Query Plan

N1QL: Query Execution Flow
Clients
1. Submit the query over REST API 8. Query result
2. Parse, Analyze, create Plan 7. Evaluate: Filter, Join, Aggregate, Sort, Paginate
3. Scan Request;
index filters
6. Fetch the documents
Index
Service
Query
Service
Data
Service
4. Get qualified doc keys
5. Fetch Request,
doc keys
SELECT c_id,
c_first,
c_last,
c_max
FROM CUSTOMER
WHERE c_id = 49165;
{
"c_first": "Joe",
"c_id": 49165,
"c_last": "Montana",
"c_max" : 50000
}

Inside a Query Service
Client
FetchParse Plan Join Filter
Pre-Aggregate
Offset Limit ProjectSortAggregateScan
Query Service
Index
Service
Data
Service

Inside a Query Service
FetchParse Plan Join Filter
Pre-Aggregate
Offset Limit ProjectSortAggregateScan
Query Service
Index
Service
Data
Service
Prepare
Phase
Projection
Phase
Run
Phase
Scan
Phase
Fetch
Phase

Query Execution: Plan
Fetch
Parse
Plan
Join
Filter
Offset
Limit
Project
Sort
Aggregate
Scan
• Each query can be executed in several ways
• Create the query execution plan
• Access path for each keyspace referenced
• Decide on the filters to push down
• Determine Join order and join method
• Create the execution tree
• For each keyspace reference:
• Look at the available indices
• Match the filters in the query with index condition and keys
• Choose one or more indices for each keyspace
• Decide if a covering index can be used
• Covering index is preferred
• Create index filters and post scan, post join filters

Fetch
Parse
Plan
Join
Filter
Offset
Limit
Project
Sort
Aggreg
ate
Scan
• Covering Index Scan
• Predicate pushdown to Indexer
• Pushdown limit to IndexScan (4.5)
• Use Index Order (4.5)
CREATE INDEX ts_c_id ON
`travel-sample` (country, id)
WHERE type = "airline";
EXPLAIN SELECT country, id
FROM `travel-sample` t
WHERE type = "airline" AND
country = "United States" AND
id BETWEEN 10 AND 1000
ORDER BY country, id
OFFSET 5
LIMIT 10;
{
"#operator": "IndexScan",
"covers": [
"cover ((`t`.`country`))",
"cover ((`t`.`id`))",
"cover ((meta(`t`).`id`))"
],
"filter_covers": {
"cover ((`t`.`type`))": "airline"
},
"index": "ts_c_id",
"index_id": "ccd0f0c297114733",
"keyspace": "travel-sample",
"limit": "(5 + 10)",
"namespace": "default",
"spans": [
{
"Range": {
"High": [
""United States"",
"1000"
],
"Inclusion": 3,
"Low": [
""United States"",
"10"
]
}
}
],
"using": "gsi"
}

Fetch
Parse
Plan
Join
Filter
Offset
Limit
Project
Sort
Aggreg
ate
Scan
• NonCovering Index Scan
• Predicate pushdown to Indexer
• Pushdown limit to IndexScan (4.5)
• Use Index order (4.5)
CREATE INDEX ts_c_id ON `travel-
sample`(country, id) WHERE type =
"airline";
EXPLAIN SELECT country, id, name
WHERE type = "airline" AND country =
"United States" AND id BETWEEN 10 AND
1000
OFFSET 5
LIMIT 10;
{
"index": "ts_c_id",
"index_id": "ccd0f0c297114733",
"limit": "(5 + 10)",
"spans": [
{
"Range": {
"High": [
""United States"",
"1000"
],
"Inclusion": 3,
"Low": [
""United States"",
"10"
]
}
}
],
"using": "gsi"
}

N1QL: Covering Index and Covered Query
Clients
1. Submit the query over REST API 6. Query result
2. Parse, Analyze, create Plan 5. Evaluate: Filter, Join, Aggregate, Sort, Paginate
3. Scan Request; index
filters
Index
Service
Query
Service
4. Get qualified doc keys
create index i1 on CUSTOMER(c_id, c_max, c_first, c_last)
SELECT c_id,
c_first,
c_last,
c_max
FROM CUSTOMER
WHERE c_id = 49165;
{
"c_first": "Joe",
"c_id": 49165,
"c_last": "Montana",
"c_max" : 50000
}

Fetch
Parse
Plan
Join
Filter
Offset
Limit
Project
Sort
Aggreg
ate
Scan
• NonCovering Query
• Fetch (Only for Non Covering Scan)
• Filter
"airline";
1000
OFFSET 5
LIMIT 10;
{
"#operator": "Fetch",
"namespace": "default"
}
{
"#operator": "Filter",
"condition": "((((`t`.`type`) =
"airline") and ((`t`.`country`) =
"United States")) and ((`t`.`id`) between
10 and 1000))"
}

Fetch
Parse
Plan
Join
Filter
Offset
Limit
Project
Sort
Aggreg
ate
Scan
• IntersectScan
• For non covered queries only
CREATE INDEX ts_c ON `travel-
sample`(country) WHERE type = "airline"
;
CREATE INDEX ts_n ON `travel-
sample`(name) WHERE type = "airline" ;
EXPLAIN SELECT country, name
"United States" AND name = "US
Airways";
{"#operator": "IntersectScan",
"scans": [
{ "#operator": "IndexScan",
"index": "ts_c",
"spans": [
{
"Range": {
"High": [ ""United States”"],
"Inclusion": 3,
"Low": [ ""United States””]
}
}
]
},
{ "#operator": "IndexScan",
"index": "ts_n",
"spans": [
{
"Range": {
"High": [""US Airways”” ],
"Inclusion": 3,
"Low": [""US Airways”” ]
}
}
]
}
]
}

Fetch
Parse
Plan
Join
Filter
Offset
Limit
Project
Sort
Aggreg
ate
Scan
• Array Index Key Scan (4.5)
CREATE INDEX ts_aix1 ON `travel-
sample` (DISTINCT ARRAY v.day FOR
v IN schedule END) WHERE type =
"route";
SELECT META(t).id
WHERE type = "route" AND ANY v IN
schedule SATISFIES v.day IN [1,2]
END;
{ "#operator": "DistinctScan",
"scan": {
"index": "ts_aix1",
"spans": [
{
"Range": {
"High": [
"1"
],
"Inclusion": 3,
"Low": [
"1"
]
}
},
{
"Range": {
"High": [
"2"
],
"Inclusion": 3,
"Low": [
"2"
]
}
}
]
}
}

Fetch
Parse
Plan
Join
Filter
Offset
Limit
Project
Sort
Aggreg
ate
Scan
• IndexCountScan (4.5)
"airline";
EXPLAIN SELECT COUNT(1) AS count
1000;
{ "#operator": "IndexCountScan",
"covers": [
"cover ((`t`.`id`))",
],
"index": "ts_c_id",
"spans": [
{
"Range": {
"High": [
""United States"",
"1000"
],
"Inclusion": 3,
"Low": [
""United States"",
"10"
]
}
}
]
}
{ "#operator": "IndexCountProject",
"result_terms": [
{
"as": "count",
"expr": "count(1)"
}
]
}

Fetch
Parse
Plan
Join
Filter
Offset
Limit
Project
Sort
Aggreg
ate
Scan
• JOIN, LEFT JOIN.
• NEST, LEFT NEST will have
operator NEST.
• LEFT side keyspace of JOIN can
be covered.
• Non-covered query.
CREATE INDEX ts_rix1 ON `travel-
sample` (DISTINCT ARRAY v.day FOR v
IN schedule END, airlineid, schedule)
WHERE type = "route";
EXPLAIN SELECT r, a
FROM `travel-sample` r
LEFT JOIN `travel-sample` a
ON KEYS r.airlineid
WHERE r.type = "route" AND ANY v IN
r.schedule SATISFIES v.day = 1 END
LIMIT 1;
"scan": {
"index": "ts_rix1",
"spans": [
{
"Range": {
"High": [
"1"
],
"Inclusion": 3,
"Low": [
"1"
]
}
}
]
}
{
"#operator": "Join",
"as": "a",
"on_keys": "(`r`.`airlineid`)",
"outer": true
}

Fetch
Parse
Plan
Join
Filter
Offset
Limit
Project
Sort
Aggreg
ate
Scan
• JOIN LEFT side covered query.
CREATE INDEX ts_rix1 ON `travel-
sample` (DISTINCT ARRAY v.day FOR v
IN schedule END, airlineid, schedule)
WHERE type = "route";
EXPLAIN SELECT r.schedule, META(r).id
FROM `travel-sample` r
LEFT JOIN `travel-sample` a
ON KEYS r.airlineid
WHERE r.type = "route" AND ANY v IN
r.schedule SATISFIES v.day = 1 END
LIMIT 1;
"scan": {
"covers": [
"cover ((distinct (array (`v`.`day`) for
`v` in (`r`.`schedule`) end)))",
"cover ((`r`.àirlineid`))",
"cover ((`r`.`schedule`))",
"cover ((meta(`r`).ìd`))"
],
"filter_covers": {
"cover ((`r`.`type`))": "route"
}
"index": "ts_rix1",
"spans": [
{
"Range": {
"High": [
"1"
],
"Inclusion": 3,
"Low": [
"1"
]
}
}
]
}
{
"#operator": "Join",
"as": "a",
"on_keys": ”cover ((`r`.àirlineid`))",
"outer": true
}

How does this work?
CREATE INDEX brewery_state ON `beer-sample`(state);
CREATE INDEX beer_brewery_id ON `beer-sample`(brewery_id);
SELECT brewery.name as breweryname, ARRAY_AGG(beer.name) beerlist
FROM `beer-sample` brewery
LEFT OUTER JOIN
`beer-sample` beer
ON KEY beer.brewery_id FOR brewery
WHERE brewery.state = "California"
GROUP BY brewery.name;
brewerybrewery_state
[“California”,
“21st_sfo_ale”]
beer_brewery_id
(brewery_id =
“21st_sfo_ale”)
beer
[“21st_sfo_ale”
, ”pale_ale”]
(state = ‘California’)

Fetch
Parse
Plan
Join
Filter
Offset
Limit
Project
Sort
Aggreg
ate
Scan
• Index JOIN, LEFT Index JOIN.
• Index NEST, LEFT Index NEST will have
operator NEST.
• LEFT side keyspace of JOIN can be
covered.
• In 4.6 RIGHT side keyspace of Index JOIN
can be covered. NOT for Index NEST.
sample`(country) WHERE type =
"airline" ;
CREATE INDEX ts_aid ON `travel-
sample`(airlineid, airline) WHERE
type = "route" ;
EXPLAIN SELECT r, a
FROM `travel-sample` a
JOIN `travel-sample` r
ON KEY r.airlineid FOR a
WHERE r.type = "route" AND a.type =
"airline" AND a.country = "United
States"
LIMIT 1;
{
"index": "ts_c",
"spans": [
{
"Range": {
"High": [
""United States""
],
"Inclusion": 3,
"Low": [
""United States""
]
}
}
]
}
{
"#operator": "IndexJoin",
"as": "r",
"for": "a",
"on_key": "(`r`.`airlineid`)",
"scan": {
"index": "ts_aid"
}
}

Fetch
Parse
Plan
Join
Filter
Offset
Limit
Project
Sort
Aggreg
ate
Scan
• Covered Index Join (4.5)
"airline" ;
type = "route" ;
EXPLAIN SELECT a.country, r.airline
States";
{
"covers": [
"cover ((à`.`country`))",
"cover ((meta(à`).ìd`))"
],
"filter_covers": {
"cover ((à`.`type`))": "airline"
},
"index": "ts_c",
"spans": [
{
"Range": {
"High": [
""United States""
],
"Inclusion": 3,
"Low": [
""United States""
]
}
}
]
}
{
"as": "r",
"for": "a",
"on_key": "(`r`.àirlineid`)",
"scan": {
"index": "ts_aid"
}
}

Fetch
Parse
Plan
Join
Filter
Offset
Limit
Project
Sort
Aggreg
ate
Scan
• LEFT & RIGHT Covered Index Join (4.6)
"airline" ;
type = "route" ;
EXPLAIN SELECT a.country, r.airline
States";
{"#operator": "IndexScan",
"covers": [
"cover ((à`.`country`))",
"cover ((meta(à`).ìd`))"
],
"filter_covers": {
"cover ((à`.`type`))": "airline"
},
"index": "ts_c”
………
}
{
"as": "r",
"for": "a",
"on_key": "cover ((`r`.àirlineid`))",
"scan": {
"covers": [
"cover ((`r`.àirlineid`))",
"cover ((`r`.àirline`))",
"cover ((meta(`r`).ìd`))"
],
"filter_covers": {
"cover ((`r`.`type`))": "route"
},
"index": "ts_aid"
}
}

Fetch
Parse
Plan
Join
Filter
Offset
Limit
Project
Sort
Aggregate
Scan
• Aggregate
"airline";
EXPLAIN SELECT country, count(1) count
WHERE type = "airline"
GROUP BY country;
{
"#operator": "InitialGroup",
"aggregates": [
"count(1)"
],
"group_keys": [
"(`t`.`country`)"
]
}
{
"#operator": "FinalGroup",
"aggregates": [
"count(1)"
],
"group_keys": [
"(`t`.`country`)"
]
}
{
"#operator": "IntermediateGroup",
"aggregates": [
"count(1)"
],
"group_keys": [
"(`t`.`country`)"
]
}

Fetch
Parse
Plan
Join
Filter
Offset
Limit
Project
Sort
Aggreg
ate
Scan
• Sort
• Offset
• Limit
"airline";
1000
ORDER BY country, id DESC
OFFSET 5
LIMIT 10;
{
"#operator": "Order",
"limit": "10",
"offset": "5",
"sort_terms": [
{
"expr": "(`t`.`country`)"
},
{
"desc": true,
"expr": "(`t`.`id`)"
}
]
}
{
"#operator": "Offset",
"expr": "5"
}
{
"#operator": "Limit",
"expr": "10"
}

Fetch
Parse
Plan
Join
Filter
Offset
Limit
Project
Sort
Aggreg
ate
Scan
• Project
"airline";
1000
ORDER BY country, id DESC
OFFSET 5
LIMIT 10;
{
"#operator": "InitialProject",
"result_terms": [
{
"expr": "(`t`.`country`)"
},
{
"expr": "(`t`.`id`)"
},
{
"expr": "(`t`.`name`)"
}
]
}
{
"#operator": "FinalProject"
}

How Query Plan Generated

Query Plan Generation
• Rule based optimization
• The index chosen by N1QL will satisfy the query
• Each query block has is optimized separately & stitched.
32

• Index Selection Criteria
• If the query contains a USE KEYS clause, no index scan or full / primary scan is performed.
The input document keys are taken directly from the USE KEYS clause.
• If there is an explicit or implicit query predicate:
• Covering secondary scan;
• Regular secondary scan -- longest satisfying keys, intersect scan;
• UNNEST scan;
• Regular primary scan
• If the query does not contain a predicate
• covering primary scan;
• regular primary scan.
• If a primary scan is selected, and there is no primary index available, the query
errors out.
33

• Covering Primary Scan
• A covering primary scan is a primary scan that does not perform a subsequent document fetch. It is
used for queries that need a full / primary scan and only reference META().id.
• Regular Primary Scan
• A regular primary scan also performs a subsequent document fetch. It is used for queries that need a
full / primary scan and reference some document data other than META().id.
34
SELECT META(t).id FROM `travel-sample` t;
SELECT META(t).cas FROM `travel-sample` t;
SELECT * FROM `travel-sample` t;
SELECT t.type FROM `travel-sample` t;

Preliminaries :
• Online indexes
• Only online indexes are considered
• Preferred indexes
• USE INDEX hint is provided the indexes in that list are only considered
• Satisfying Index condition
• Partial / filtered indexes that index condition is super set of query predicate are
considered
• Satisfying Index keys
• Indexes whose leading keys satisfy query predicate are considered
• Longest satisfying index keys
• Redundancy is eliminated båy keeping longest satisfying index keys in same order.
• Index with satisfying keys (a,b,c) is retained over index with satisfying (a,b)
35

Covering Secondary Scan
• Each satisfied index with most number of index keys is examined for query coverage
• Shortest covering index will be used.
Regular Secondary Scan
• Indexes in with most number of matching index keys are used
• When more than one index are qualified, IntersectScan is used.
• To avoid IntersectScan provide hint with USE INDEX.
36
CREATE INDEX ts_name ON `travel-sample`(country, name) WHERE type = "airline";
SELECT country, name, type, META().id
FROM `travel-sample`
WHERE type = "airline" AND country = "United States";
SELECT country, name, type, id

UNNEST Scan
• Only array indexes are considered. And only queries with UNNEST clauses are considered
IndexCountScan
• Queries with single projection of COUNT aggregate, NO JOIN’s, GROUP BY is considered
• Chosen Index needs to be covered with single range, exact range will be able to push to indexer and
argument to COUNT needs to be constant or leading key
37
CREATE INDEX ts_c ON `travel-sample`(country) WHERE type = "airline";
SELECT COUNT(1) AS count

• In order to satisfy anAND predicate, an index only needs to satisfy any term in the AND
predicate.
• In order to satisfy an OR predicate, an index must satisfy every term in the OR predicate.
38

Index Satisfies these predicates Index doesn’t satisfy these predicates
CREATE INDEX idx_dept ON employee( dept_id, hire_date, base_comp );
hire_date >= "2010" AND dept_id = "xyz"
hire_date >= "2010" AND dept_id = "xyz" AND
last_name = "Smith"
dept_id = "abc" AND base_comp > 50000
dept_id = "abc" AND bonus > 2000
dept_id = "abc" OR dept_id = "xyz"
(hire_date >= "2010" AND dept_id >= "xyz")
OR (hire_date >= "2010" AND dept_id = "abc")
dept_id = "abc" OR (hire_date >= "2010" AND
dept_id >= "xyz")
(hire_date >= "2010" AND dept_id >= "xyz")
OR (dept_id = "abc" AND bonus > 2000)
hire_date >= "2010" AND base_comp > 50000
hire_date >= "2010" AND bonus > 2000
bonus > 2000 AND base_comp > 50000
bonus > 2000 AND last_name = "Smith"
dept_id = "abc" OR hire_date >= "2010"
dept_id = "abc" OR dept_id = "xyz" OR
base_comp > 50000
dept_id = "abc" OR dept_id = "xyz" OR
bonus > 2000
hire_date > "2010" OR base_comp > 50000

Query OptimizationTo
Exploit Index Features

Query OptimizationTo Exploit Index Features
• N1QL Query engine can exploit the following index features.
• By creating right index and query that takes advantage performs better.
• Pushing predicates hint to Index
• Index Filters
• Using Index Order
• Pushing LIMIT to Index
• Pushing Offset as LIMIT (limit = limit + offset) to Index
• Top n Heap for Order
• Index Count (GSI Index only)
• Pushing MIN to Index
• Duplicate Indexes and Load balancing
• USE INDEX
41

USE INDEX
• Queries can specify which index to use with USE INDEX clause
• Multiple indexes qualify for query and one performs better than others
• To avoid Intersect Scan
• If there are many indexes on data source and queries are ad-hoc USE INDEX can reduce prepare time
Pushing predicates
• Predicates are pushed to indexer when possible
• These can be examined as spans in Index section of the EXPLAIN plan
• The spans can be exact or superset and never be subset of the predicates
• If the spans are exact the applying query predicates again will not eliminate the document
Index Filters
• If Index filter has equal on expression, that expression not required to include in the index keys.This allows keep
the index LEAN . Queries can exploit index filter condition for covered queries.
42
CREATE INDEX ts_c ON `travel-sample`(country) WHERE type = "airline" ;
SELECT type, country FROM `travel-sample`

Using Index Order
• Query ORDER BY list matches with index keys list order left to right with single range span
• Query doesn’t have any JOINs, GROUP BY or other clauses that can change the order produced by
indexer
• The query can exploit index order and avoid expensive sort and fetching lot of data unnecessarily in
some cases
43
CREATE INDEX ts_c_id ON `travel-sample`(country, id) WHERE type = "airline" ;
SELECT country, id FROM `travel-sample`
WHERE type = "airline" AND country = "United States”
ORDER BY id; --4.6 Uses Index order without any change
ORDER BY country, id; --4.5 Uses Index order
CREATE INDEX ts_c_nid ON `travel-sample`(country, -id) WHERE type = "airline" ;
SELECT country, -(-id) FROM `travel-sample`
ORDER BY country, -id; -- id prodcued in DESC order

Push LIMIT to Index
• Pushing the LIMIT is hint to indexer when to stop
• Pushing LIMIT can be done only when the predicates are pushed to indexer, spans are exact and
matches index order
• In case multiple spans LIMIT can be pushed to each span
44
CREATE INDEX ts_c_id ON `travel-sample`(country, id) WHERE type = "airline" ;
WHERE type = "airline" AND country = "United States" AND id BETWEEN 0 AND 1000
LIMIT 10; --LIMIT can be pushed
WHERE type = "airline" AND country IN ["United States", "United Kingdom"] AND id BETWEEN 0
AND 1000
LIMIT 10; --LIMIT can be pushed
CREATE INDEX ts_c_id ON `travel-sample`(id, country) WHERE type = "airline" ;
WHERE type = "airline" AND country = "United States" AND id BETWEEN 0 AND 1000
LIMIT 10; --LIMIT can’t be pushed. Indexer produces {"id":11, "country":"France"}

Push Offset
• If LIMIT is present offset is added to limit and pushed limit to Indexer
Top-n Heap for Order
• Query requires order and has LIMIT less than 64K it usesTop-n Heap to eliminate unwanted documents before
order.
Index Count
• Count is done by indexer
• Only GSI index supports Index count
• Queries with single projection of COUNT aggregate, NO JOIN’s, GROUP BY is considered
argument to COUNT needs to be constant or leading key
45
CREATE INDEX ts_c ON `travel-sample`(country) WHERE type = "airline";
SELECT COUNT(1) AS count

MIN
• Queries with single projection of MIN aggregate, NO JOIN’s, GROUP BY is considered.
argument to MIN needs to be leading key.
• If the required conditions are satisfied limit 1 pushed to indexer.
46
CREATE INDEX ts_c ON `travel-sample`(country) WHERE
type = "airline";
SELECT MIN(country)
WHERE type = "airline" AND country > "United
States";
{
"covers": [
],
"filter_covers": {
"cover ((`t`.`type`))": "airline"
},
"index": "ts_c",
"limit": "1",
"spans": [
{
"Range": {
"Inclusion": 0,
"Low": [
""United States""
]
}
}
]
}

Duplicate Indexes and Load balancing
• Allows duplicate indexes (i.e. different name but identical schema) on same or different Indexer services
• Query Service will choose one of the index during prepare time
• During execution Indexer client does load balancing and uses one of the indexer to service query
47

Keshav Murthy
Director
Contact information
SitaramVemulapalli
Sr. Software Engineer

Understanding N1QL Optimizer to Tune Queries

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