DOC-3179: add UTM info to redis.io links

Author: dwdougherty

src/ds/hashes.md

@@ -44,4 +44,4 @@ HINCRBY bike:1 price 100
 HINCRBY bike:1 price -100
 ```
 
-See [here](https://redis.io/docs/data-types/hashes) for the hash type reference page, and [here](https://redis.io/commands/?group=hash) for the entire set of Redis hash commands.
+See [here](https://redis.io/docs/data-types/hashes?utm_source=redisinsight&utm_medium=main&utm_campaign=tutorials) for the hash type reference page, and [here](https://redis.io/commands/?group=hash?utm_source=redisinsight&utm_medium=main&utm_campaign=tutorials) for the entire set of Redis hash commands.

src/ds/json/intro.md

@@ -10,7 +10,7 @@ Primary features include:
 
 ### Prerequisites
 
-[Redis Stack](https://redis.io/download) >=7.2.0-v7 \
+[Redis Stack](https://redis.io/download?utm_source=redisinsight&utm_medium=main&utm_campaign=tutorials) >=7.2.0-v7 \
 OR \
 [RedisJSON](https://github.com/RedisJSON/RedisJSON/) >=2.6.8 \
 OR \

src/ds/lists.md

@@ -78,4 +78,4 @@ KEYS bike:colors
 **Note**:
 > `LRANGE` will return an empty (null) list if a key no longer exists.
 
-See [here](https://redis.io/docs/data-types/list) for the list type reference page, and [here](https://redis.io/commands/?group=list) for the entire set of Redis list commands.
+See [here](https://redis.io/docs/data-types/list?utm_source=redisinsight&utm_medium=main&utm_campaign=tutorials) for the list type reference page, and [here](https://redis.io/commands/?group=list&utm_source=redisinsight&utm_medium=main&utm_campaign=tutorials) for the entire set of Redis list commands.

src/ds/prob/bloom-cuckoo.md

@@ -16,7 +16,7 @@ How can a Bloom filter be helpful to an online bike shop service? For starters,
 Another use case is targeting ads to users. A per-user Bloom filter can be created and populated with all the products each user has purchased from the shop. When the shop's ad suggestion engine provides a list of possible ads to show a user, it can check each item against the user's Bloom filter. Each item that is not part of the filter are good targets. For each item that might already be part of the filter, a second query can be made to the primary database to confirm. If the second confirmation is negative, then that ad can be added to the target list.
 
 First, create a bloom filter and configure an acceptable false positive rate for your use case.
-You can also specify an initial capacity; the size of the dataset that you expect to add to the filter. Bloom filters can be configured to expand when this capacity is reached - [see the `BF.RESERVE` documentation for details](https://redis.io/commands/bf.reserve/).
+You can also specify an initial capacity; the size of the dataset that you expect to add to the filter. Bloom filters can be configured to expand when this capacity is reached - [see the `BF.RESERVE` documentation for details](https://redis.io/commands/bf.reserve/?utm_source=redisinsight&utm_medium=main&utm_campaign=tutorials).
 
 ```redis Create a Bloom filter
 BF.RESERVE user:778:bought_products 0.001 50 // create new bloom filter at key "user:778:bought_products" with a desired false positive rate of 0.1% (0.001) and anticipated data set size of 50 entries
@@ -50,7 +50,7 @@ BF.INSERT newBloomFilter CAPACITY 10000 ITEMS hello waves goodbye // creates the
 BF.INSERT bloomF NOCREATE ITEMS foo bar // tries to add 2 items to a filter with an error if the filter does not already exist
 ```
 
-You can read more about Bloom filters and their use cases [here](https://redis.io/docs/data-types/probabilistic/bloom-filter/). See [here](https://redis.io/commands/?group=bf) for the complete list of Bloom filter commands.
+You can read more about Bloom filters and their use cases [here](https://redis.io/docs/data-types/probabilistic/bloom-filter/?utm_source=redisinsight&utm_medium=main&utm_campaign=tutorials). See [here](https://redis.io/commands/?group=bf&utm_source=redisinsight&utm_medium=main&utm_campaign=tutorials) for the complete list of Bloom filter commands.
 
 ## Cuckoo Filters
 

src/ds/prob/intro.md

@@ -48,7 +48,7 @@ The following data structures trade perfect accuracy for extreme memory efficien
 
 ### Prerequisites
 
-[Redis Stack Server](https://redis.io/download) >=7.2.0-v7 \
+[Redis Stack Server](https://redis.io/download?utm_source=redisinsight&utm_medium=main&utm_campaign=tutorials) >=7.2.0-v7 \
 OR \
 [RedisBloom](https://oss.redis.com/redisbloom/) >=2.6.10 \
 OR \

src/ds/sets.md

@@ -63,4 +63,4 @@ SPOP bike:1:addons
 SMEMBERS bike:1:addons
 ```
 
-See [here](https://redis.io/docs/data-types/sets) for the set type reference page, and [here](https://redis.io/commands/?group=set) for the entire list of Redis set commands.
+See [here](https://redis.io/docs/data-types/sets?utm_source=redisinsight&utm_medium=main&utm_campaign=tutorials) for the set type reference page, and [here](https://redis.io/commands/?group=set&utm_source=redisinsight&utm_medium=main&utm_campaign=tutorials) for the entire list of Redis set commands.

src/ds/sorted_sets.md

@@ -44,4 +44,4 @@ You can get the rank of any sorted set member using the `ZRANK` command. Note: t
 ZRANK bike:brands "Claude Shannon" WITHSCORE
 ```
 
-See [here](https://redis.io/docs/data-types/sorted-sets) for the sorted set type reference page, and [here](https://redis.io/commands/?group=sorted-set) for the entire list of Redis sorted set commands.
+See [here](https://redis.io/docs/data-types/sorted-sets?utm_source=redisinsight&utm_medium=main&utm_campaign=tutorials) for the sorted set type reference page, and [here](https://redis.io/commands/?group=sorted-set&utm_source=redisinsight&utm_medium=main&utm_campaign=tutorials) for the entire list of Redis sorted set commands.

src/ds/strings.md

@@ -106,4 +106,4 @@ PERSIST bike:1:lock_status
 TTL bike:1:lock_status
 ```
 
-See [here](https://redis.io/docs/data-types/strings) for the string type reference page, and [here](https://redis.io/commands/?group=string) for the entire set of Redis string commands.
+See [here](https://redis.io/docs/data-types/strings?utm_source=redisinsight&utm_medium=main&utm_campaign=tutorials) for the string type reference page, and [here](https://redis.io/commands/?group=string&utm_source=redisinsight&utm_medium=main&utm_campaign=tutorials) for the entire set of Redis string commands.

src/ds/ts/intro.md

@@ -16,7 +16,7 @@ You can ingest and query millions of samples and events at the speed of Redis.
 
 ### Prerequisites
 
-[Redis Stack Server](https://redis.io/download) >=7.2.0-v7 \
+[Redis Stack Server](https://redis.io/download?utm_source=redisinsight&utm_medium=main&utm_campaign=tutorials) >=7.2.0-v7 \
 OR \
 [RedisTimeSeries](https://oss.redis.com/redistimeseries/) >=1.10.11 \
 OR \

src/sq/aggregations.md

@@ -4,7 +4,7 @@ An aggregation query allows you to perform the following actions:
 - Group data based on field values.
 - Apply aggregation functions on the grouped data.
 
-This article explains the basic usage of the [FT.AGGREGATE](https://redis.io/commands/ft.aggregate/) command. For further details, see the [command specification](https://redis.io/commands/ft.aggregate/) and the [aggregations reference documentation](https://redis.io/docs/interact/search-and-query/advanced-concepts/aggregations).
+This article explains the basic usage of the `FT.AGGREGATE` command. For further details, see the [command specification](https://redis.io/commands/ft.aggregate/?utm_source=redisinsight&utm_medium=main&utm_campaign=tutorials) and the [aggregations reference documentation](https://redis.io/docs/interact/search-and-query/advanced-concepts/aggregations?utm_source=redisinsight&utm_medium=main&utm_campaign=tutorials).
 
 The examples in this article use a schema with the following fields:
 
@@ -49,7 +49,7 @@ FT.AGGREGATE index "query_expr" LOAD n "field_1" .. "field_n" APPLY "function_ex
 
 Here is a more detailed explanation of the query syntax:
 
-1. **Query expression**: you can use the same query expressions as you would use with the `FT.SEARCH` command. You can substitute `query_expr` with any of the expressions explained in the articles of this [query topic](https://redis.io/docs/interact/search-and-query/query/). Vector search queries are an exception. You can't combine a vector search with an aggregation query.
+1. **Query expression**: you can use the same query expressions as you would use with the `FT.SEARCH` command. You can substitute `query_expr` with any of the expressions explained in the articles of this [query topic](https://redis.io/docs/interact/search-and-query/query/?utm_source=redisinsight&utm_medium=main&utm_campaign=tutorials). Vector search queries are an exception. You can't combine a vector search with an aggregation query.
 2. **Loaded fields**: if field values weren't already loaded into the aggregation pipeline, you can force their presence via the `LOAD` clause. This clause takes the number of fields (`n`), followed by the field names (`"field_1" .. "field_n"`).
 3. **Mapping function**: this mapping function operates on the field values. A specific field is referenced as `@field_name` within the function expression. The result is returned as `result_field`.
 
@@ -73,7 +73,7 @@ FT.AGGREGATE index "query_expr" ...  GROUPBY n "field_1" .. "field_n" REDUCE AGG
 Here is an explanation of the additional constructs:
 
 1. **Grouping**: you can group by one or many fields. Each ordered sequence of field values then defines one group. It's also possible to group by values that resulted from a previous `APPLY ... AS`.
-2. **Aggregation**: you must replace `AGG_FUNC` with one of the supported aggregation functions (e.g., `SUM` or `COUNT`). A complete list of functions is available in the [aggregations reference documentation](https://redis.io/docs/interact/search-and-query/advanced-concepts/aggregations). Replace `aggregated_result_field` with a value of your choice.
+2. **Aggregation**: you must replace `AGG_FUNC` with one of the supported aggregation functions (e.g., `SUM` or `COUNT`). A complete list of functions is available in the [aggregations reference documentation](https://redis.io/docs/interact/search-and-query/advanced-concepts/aggregations?utm_source=redisinsight&utm_medium=main&utm_campaign=tutorials). Replace `aggregated_result_field` with a value of your choice.
 
 The following query shows you how to group by the field `condition` and apply a reduction based on the previously derived `price_category`. The expression `@price<1000` causes a bicycle to have the price category `1` if its price is lower than 1000 USD. Otherwise, it has the price category `0`. The output is the number of affordable bicycles grouped by price category.
 
@@ -82,7 +82,7 @@ FT.AGGREGATE idx:bicycle "*" LOAD 1 price APPLY "@price<1000" AS price_category
 ```
 
 **Note**:
-> You can also create more complex aggregation pipelines with [FT.AGGREGATE](https://redis.io/commands/ft.aggregate/). Applying multiple reduction functions under one `GROUPBY` clause is possible. In addition, you can also chain groupings and mix in additional mapping steps (e.g., `GROUPBY ... REDUCE ... APPLY ... GROUPBY ... REDUCE`)
+> You can also create more complex aggregation pipelines with `FT.AGGREGATE`. Applying multiple reduction functions under one `GROUPBY` clause is possible. In addition, you can also chain groupings and mix in additional mapping steps (e.g., `GROUPBY ... REDUCE ... APPLY ... GROUPBY ... REDUCE`)
 
 ## Aggregating without grouping