Adding upstream version 8.2402.0+dfsg.upstream/8.2402.0+dfsg

Signed-off-by: Daniel Baumann <daniel.baumann@progress-linux.org>

1 files changed, 82 insertions, 0 deletions

diff --git a/source/tutorials/hash_sampling.rst b/source/tutorials/hash_sampling.rst
new file mode 100644
index 0000000..4fe6ecf
--- /dev/null
+++ b/source/tutorials/hash_sampling.rst
@@ -0,0 +1,82 @@
+Hash-based Sampling
+===================
+
+
+Rsyslog supports various sampling mechanisms. These can be used on client systems to save servers from getting overwhelmed. Here we introduce a new sampling mechanism "Hash-based sampling".
+
+Let's consider the following setup of systems running services and generating logs.
+There are three services A, B and C.
+These services work together to create a request processing pipeline.
+User request lands up at system A, which processes the request, generates logs and forwards it to service B.
+B processes the request it received from A, generates logs and forwards it to C and so on and so forth.
+
+::
+
+    +-+-+-+-+-+-+      +-+      +-+     +-+      +-+-++-+-+
+    |UserRequest|  ->  |A|   -> |B|  -> |C| ->  | Database |
+    +-+-+-+-+-+-+      +-+      +-+     +-+      +-+-++-+-+
+
+
+Consider three sample user requests
+    ::
+
+    {"request_id": "reqid1", "data": "payload_1_original"}
+    {"request_id": "reqid2", "data": "payload_2_original"}
+    {"request_id": "reqid3", "data": "payload_3_original"}
+
+Service A generated logs
+    ::
+
+    {"request_id": "reqid1", "datetime": "<time>", "Processing_state" : "A", "Exception" : "none"}
+    {"request_id": "reqid2", "datetime": "<time>", "Processing_state" : "A_prime", "Exception" : "Unknown flow"}
+    {"request_id": "reqid3", "datetime": "<time>", "Processing_state" : "A_double_prime", "Exception" : "Parsing Failed"}
+
+Service B generated logs
+    ::
+
+    {"request_id": "reqid1", "datetime": "<time>", "Processing_state" : "B", "Exception" : "none"}
+    {"request_id": "reqid2", "datetime": "<time>", "Processing_state" : "A_Failed_at_1st_Stage", "Exception" : "none"}
+    {"request_id": "reqid3", "datetime": "<time>", "Processing_state" : "B_Failed", "Exception" : "Field not found"}
+
+Service C generated logs
+    ::
+
+    {"request_id": "reqid1", "datetime": "<time>", "Processing_state" : "C", "Exception" : "none"}
+    {"request_id": "reqid2", "datetime": "<time>", "Processing_state" : "NO_OP", "Exception" : "none"}
+    {"request_id": "reqid3", "datetime": "<time>", "Processing_state" : "C_Failed", "Exception" : "NullPointer"}
+
+
+We can sample logs based on request_id thereby getting **all-or-none** logs associated with a request_id. This is a kind of transactional guarantee on logs for a request ID. This helps to create an end to end picture of pipeline even on sampled data.
+
+Using hash64mod for sampling
+    ::
+
+     if (hash64mod($!msg!request_id, 100) <= 30) then {
+      //send out
+     }
+
+With hash64mod sampling, we get sampled logs like
+    ::
+
+    {"request_id": "reqid3", "data": "payload_3_original"}
+    {"request_id": "reqid3", "datetime": "<time>", "Processing_state" : "A_double_prime", "Exception" : "Parsing Failed"}
+    {"request_id": "reqid3", "datetime": "<time>", "Processing_state" : "B_Failed", "Exception" : "Field not found"}
+    {"request_id": "reqid3", "datetime": "<time>", "Processing_state" : "C_Failed", "Exception" : "NullPointer"}
+
+We will get all-or-none logs associated to hash-string (request ID in this case or combination of field_1 & field_2 & field_3) giving a full view of a request's life cycle in the cluster across various systems. The only constraint in hash-based sampling is, it will work only if we have the same unique identifier (single field or combination of fields) in logs across services.
+
+Same as hash64mod, we can use hash64 to add hash as tags that can be used later to filter logs.
+
+Using hash64 for filtering later
+    ::
+
+     set $.hash = hash64($!msg!field_1 & $!msg!field_2 & $!msg!field_3)
+     set $!tag= $syslogtag & $.hash;
+     //send out
+
+Hash-based sampling is helpful on a single system too. Say a single process generates 4 log lines for each transaction it commits. We can get all-or-none guarantees around these 4 logs generated for each transaction. Thereby giving a complete picture of a transaction (while sampling).
+
+**Note**
+  * Hash-based sampling can be used only when logs have **same unique identifier** across services.
+  * Default hash implementation is djb2 hash and xxhash can be enabled using compile-time flag (hash implementation can change release to release, check changelog).
+  * For parsing log to generate json to act on individual fields use mmnormalize.