added documentation on how to reduce overhead

docs/configuration.asciidoc

@@ -346,7 +346,7 @@ If your service handles data like this, we advise to only enable this feature wi
 
 |============
 | Environment                  | Django/Flask     | Default
-| `ELASTIC_APM_FLUSH_INTERVAL` | `FLUSH_INTERVAL` | `60`
+| `ELASTIC_APM_FLUSH_INTERVAL` | `FLUSH_INTERVAL` | `10`
 |============
 
 Interval with which transactions should be sent to the APM server, in seconds.
@@ -374,8 +374,8 @@ Setting an upper limit will prevent overloading the agent and the APM server wit
 ==== `max_queue_size`
 
 |============
-| Environment                          | Django/Flask     | Default
-| `ELASTIC_APM_MAX_EVENT_QUEUE_LENGTH` | `MAX_QUEUE_SIZE` | `500`
+| Environment                  | Django/Flask     | Default
+| `ELASTIC_APM_MAX_QUEUE_SIZE` | `MAX_QUEUE_SIZE` | `500`
 |============
 
 Maximum queue length of transactions before sending transactions to the APM server.
@@ -405,6 +405,19 @@ For more information, see <<sanitizing-data, Sanitizing Data>>.
 WARNING: We recommend to always include the default set of validators if you customize this setting.
 
 
+[float]
+[[config-transaction-sample-rate]]
+==== `transaction_sample_rate`
+
+|============
+| Environment                           | Django/Flask              | Default
+| `ELASTIC_APM_TRANSACTION_SAMPLE_RATE` | `TRANSACTION_SAMPLE_RATE` | `1.0`
+|============
+
+By default, the agent will sample every transaction (e.g. request to your service).
+To reduce overhead and storage requirements, you can set the sample rate to a value between `0.0` and `1.0`.
+We still record overall time and the result for unsampled transactions, but no context information, tags, or spans.
+
 [float]
 [[config-include-paths]]
 ==== `include_paths`

docs/index.asciidoc

@@ -39,3 +39,4 @@ include::./sanitizing-data.asciidoc[Sanitizing Data]
 include::./run-tests-locally.asciidoc[Run Tests Locally]
 
 include::./api.asciidoc[API documentation]
+include::./tuning.asciidoc[Tuning and Overhead considerations]

docs/tuning.asciidoc

@@ -0,0 +1,75 @@
+[[tuning-and-overhead]]
+== Tuning and Overhead considerations
+
+Using an APM solution comes with certain trade-offs, and the Python agent for Elastic APM is no different.
+Instrumenting your code, measuring timings, recording context data etc. all need resources: 
+
+ * CPU time
+ * memory
+ * bandwidth use
+ * Elasticsearch storage
+
+We invested and continue to invest a lot of effort to keep the overhead of using Elastic APM as low as possible.
+But because every deployment is different, there are some knobs you can turn to adapt it to your specific needs.
+
+[float]
+[[tuning-sample-rate]]
+=== Transaction Sample Rate
+
+The most straight forward way to reduce the overhead of the agent is to tell the agent to do less.
+If you set the <<config-transaction-sample-rate,`transaction_sample_rate`>> to a value below `1.0`,
+the agent will randomly sample only a subset of transactions.
+If a transaction is not sampled, the agent has to do a lot less work,
+as we only record the the name of the transaction, the overall transaction time and the result for unsampled transactions.
+
+[options="header"]
+|============
+|        Field      | Sampled   | Unsampled
+| Transaction name  | yes       | yes
+| Duration          | yes       | yes
+| Result            | yes       | yes
+| Context           | yes       | no
+| Tags              | yes       | no
+| Spans             | yes       | no
+|============
+
+Reducing the sample rate to a fraction of all transactions can make a huge difference in all four of the mentioned resource types.
+
+[float]
+[[tuning-queue]]
+=== Transaction Queue
+
+To reduce the load on the APM Server, the agent does not send every transaction up as it happens.
+Instead, it queues them up, and flushes the queue periodically, or when it reaches a maximum size, using a background thread.
+
+While this reduces the load on the APM Server (and to a certain extent on the agent),
+holding on to the transaction data in a queue uses memory.
+If you notice that using the Python agent results in a large increase of memory use,
+you can use these settings:
+
+ * <<config-flush-interval,`flush_interval`>> to reduce the time between queue flushes
+ * <<config-max-queue-size,`max_queue_size`>> to reduce the maximum size of the queue
+
+The first setting, `flush_interval`, is helpful if you have a sustained high number of transactions.
+The second setting, `max_queue_size`, can help if you experience peaks of transactions
+(a large amount of transactions in a short period of time).
+
+Keep in mind that reducing the value of either setting will cause the agent to send more HTTP requests to the APM Server,
+potentially causing a higher load.
+
+
+[float]
+[[tuning-max-spans]]
+=== Spans per transaction
+
+The average amount of spans per transaction can influence how much time the agent spends in each transaction collecting contextual data for each span,
+and the the storage space needed in Elasticsearch.
+In our experience, most usual transactions should have well below 100 spans.
+In some cases however, the number of spans can explode:
+
+ * long-running transactions
+ * unoptimized code, e.g. doing hundreds of SQL queries in a loop
+
+To avoid that such edge cases overload both the agent and the APM Server,
+the agent stops recording spans when a limit is reached.
+You can configure this limit by changing the <<config-transaction-max-spans,`transaction_max_spans`>> setting.

elasticapm/conf/__init__.py

@@ -155,7 +155,7 @@ class Config(_ConfigBase):
         'elasticapm.processors.sanitize_http_request_querystring',
         'elasticapm.processors.sanitize_http_request_body',
     ])
-    flush_interval = _ConfigValue('FLUSH_INTERVAL', type=int, default=60)
+    flush_interval = _ConfigValue('FLUSH_INTERVAL', type=int, default=10)
     transaction_sample_rate = _ConfigValue('TRANSACTION_SAMPLE_RATE', type=float, default=1.0)
     transaction_max_spans = _ConfigValue('TRANSACTION_MAX_SPANS', type=int, default=500)
     max_queue_size = _ConfigValue('MAX_QUEUE_SIZE', type=int, default=500)

tests/contrib/django/django_tests.py

@@ -1030,7 +1030,10 @@ def test_perf_database_render_no_instrumentation(benchmark, django_elasticapm_cl
 
 
 @pytest.mark.django_db
-@pytest.mark.parametrize('django_elasticapm_client', [{'_wait_to_first_send': 100}], indirect=True)
+@pytest.mark.parametrize('django_elasticapm_client', [{
+    '_wait_to_first_send': 100,
+    'flush_interval': 100
+}], indirect=True)
 def test_perf_transaction_with_collection(benchmark, django_elasticapm_client):
     django_elasticapm_client.instrumentation_store.get_all()
     with mock.patch("elasticapm.traces.TransactionsStore.should_collect") as should_collect: