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  ## About Cloud Bigtable

  [Cloud Bigtable][cloud-bigtable] is Google's NoSQL Big Data database service. It's
  the same database that powers many core Google services, including Search, Analytics, Maps, and
  Gmail.

  Be sure to activate the Cloud Bigtable API and the Cloud Bigtable Admin API under APIs & Services in the GCP Console to use Cloud Bigtable from your project.

  See the Bigtable client library documentation ([Admin API](https://googleapis.dev/java/google-cloud-clients/latest/com/google/cloud/bigtable/admin/v2/package-summary.html) and [Data API](https://googleapis.dev/java/google-cloud-clients/latest/com/google/cloud/bigtable/data/v2/package-summary.html)) to learn how to
  interact with Cloud Bigtable using this Client Library.

  ## Concepts

  Cloud Bigtable is composed of instances, clusters, nodes and tables.

  ### Instances
  Instances are containers for clusters.

  ### Clusters
  Clusters represent the actual Cloud Bigtable service. Each cluster belongs to a single Cloud Bigtable instance, and an instance can have up to 4 clusters. When your application
  sends requests to a Cloud Bigtable instance, those requests are actually handled by one of the clusters in the instance.

  ### Nodes
  Each cluster in a production instance has 3 or more nodes, which are compute resources that Cloud Bigtable uses to manage your data.

  ### Tables
  Tables contain the actual data and are replicated across all of the clusters in an instance.


  ## Clients
  The Cloud Bigtable API consists of:

  ### Data API
  Allows callers to persist and query data in a table. It's exposed by [BigtableDataClient](https://googleapis.dev/java/google-cloud-clients/latest/com/google/cloud/bigtable/data/v2/BigtableDataClient.html).

  ### Admin API
  Allows callers to create and manage instances, clusters, tables, and access permissions. This API is exposed by: [BigtableInstanceAdminClient](https://googleapis.dev/java/google-cloud-clients/latest/com/google/cloud/bigtable/admin/v2/BigtableInstanceAdminClient.html) for Instance and Cluster level resources.

  See [BigtableTableAdminClient](https://googleapis.dev/java/google-cloud-clients/latest/com/google/cloud/bigtable/admin/v2/BigtableTableAdminClient.html) for table management.

  See [BigtableDataClient](https://googleapis.dev/java/google-cloud-clients/latest/com/google/cloud/bigtable/data/v2/BigtableDataClient.html) for the data client.

  See [BigtableInstanceAdminClient](https://googleapis.dev/java/google-cloud-clients/latest/com/google/cloud/bigtable/admin/v2/BigtableInstanceAdminClient.html) for the instance admin client.

  See [BigtableTableAdminClient](https://googleapis.dev/java/google-cloud-clients/latest/com/google/cloud/bigtable/admin/v2/BigtableTableAdminClient.html) for the table admin client.

  #### Calling Cloud Bigtable

  The Cloud Bigtable API is split into 3 parts: Data API, Instance Admin API and Table Admin API.

  Here is a code snippet showing simple usage of the Data API. Add the following imports
  at the top of your file:

  ```java
  import com.google.cloud.bigtable.data.v2.BigtableDataClient;
  import com.google.cloud.bigtable.data.v2.models.Query;
  import com.google.cloud.bigtable.data.v2.models.Row;

  ```

  Then, to make a query to Bigtable, use the following code:
  ```java
  // Instantiates a client
  String projectId = "my-project";
  String instanceId = "my-instance";
  String tableId = "my-table";

  // Create the client.
  // Please note that creating the client is a very expensive operation
  // and should only be done once and shared in an application.
  BigtableDataClient dataClient = BigtableDataClient.create(projectId, instanceId);

  try {
    // Query a table
    Query query = Query.create(tableId)
        .range("a", "z")
        .limit(26);

    for (Row row : dataClient.readRows(query)) {
      System.out.println(row.getKey());
    }
  } finally {
    dataClient.close();
  }
  ```

  The Admin APIs are similar. Here is a code snippet showing how to create a table. Add the following
  imports at the top of your file:

  ```java
  import static com.google.cloud.bigtable.admin.v2.models.GCRules.GCRULES;
  import com.google.cloud.bigtable.admin.v2.BigtableTableAdminClient;
  import com.google.cloud.bigtable.admin.v2.models.CreateTableRequest;
  import com.google.cloud.bigtable.admin.v2.models.Table;
  ```

  Then, to create a table, use the following code:
  ```java
  String projectId = "my-instance";
  String instanceId = "my-database";

  BigtableTableAdminClient tableAdminClient = BigtableTableAdminClient
    .create(projectId, instanceId);

  try {
    tableAdminClient.createTable(
        CreateTableRequest.of("my-table")
          .addFamily("my-family")
    );
  } finally {
    tableAdminClient.close();
  }
  ```

  TIP: If you are experiencing version conflicts with gRPC, see [Version Conflicts](#version-conflicts).

  ## OpenCensus Tracing

  Cloud Bigtable client supports [OpenCensus Tracing](https://opencensus.io/tracing/),
  which gives insight into the client internals and aids in debugging production issues.
  By default, the functionality is disabled. For example to enable tracing using
  [Google Stackdriver](https://cloud.google.com/trace/docs/):

  [//]: # (TODO: figure out how to keep opencensus version in sync with pom.xml)

  If you are using Maven, add this to your pom.xml file
  ```xml
  <dependency>
    <groupId>io.opencensus</groupId>
    <artifactId>opencensus-impl</artifactId>
    <version>0.24.0</version>
    <scope>runtime</scope>
  </dependency>
  <dependency>
    <groupId>io.opencensus</groupId>
    <artifactId>opencensus-exporter-trace-stackdriver</artifactId>
    <version>0.24.0</version>
    <exclusions>
      <exclusion>
        <groupId>io.grpc</groupId>
        <artifactId>*</artifactId>
      </exclusion>
      <exclusion>
        <groupId>com.google.auth</groupId>
        <artifactId>*</artifactId>
      </exclusion>
    </exclusions>
  </dependency>
  ```
  If you are using Gradle, add this to your dependencies
  ```Groovy
  compile 'io.opencensus:opencensus-impl:0.24.0'
  compile 'io.opencensus:opencensus-exporter-trace-stackdriver:0.24.0'
  ```
  If you are using SBT, add this to your dependencies
  ```Scala
  libraryDependencies += "io.opencensus" % "opencensus-impl" % "0.24.0"
  libraryDependencies += "io.opencensus" % "opencensus-exporter-trace-stackdriver" % "0.24.0"
  ```

  At the start of your application configure the exporter:

  ```java
  import io.opencensus.exporter.trace.stackdriver.StackdriverTraceConfiguration;
  import io.opencensus.exporter.trace.stackdriver.StackdriverTraceExporter;

  StackdriverTraceExporter.createAndRegister(
    StackdriverTraceConfiguration.builder()
        .setProjectId("YOUR_PROJECT_ID")
        .build());
  ```

  By default traces are [sampled](https://opencensus.io/tracing/sampling) at a rate of about 1/10,000.
  You can configure a higher rate by updating the active tracing params:

  ```java
  import io.opencensus.trace.Tracing;
  import io.opencensus.trace.samplers.Samplers;

  Tracing.getTraceConfig().updateActiveTraceParams(
      Tracing.getTraceConfig().getActiveTraceParams().toBuilder()
          .setSampler(Samplers.probabilitySampler(0.01))
          .build()
  );
  ```

  ## OpenCensus Stats

  Cloud Bigtable client supports [Opencensus Metrics](https://opencensus.io/stats/),
  which gives insight into the client internals and aids in debugging production issues.
  All Cloud Bigtable Metrics are prefixed with `cloud.google.com/java/bigtable/`. The
  metrics will be tagged with:
   * `bigtable_project_id`: the project that contains the target Bigtable instance.
     Please note that this id could be different from project that the client is running
     in and different from the project where the metrics are exported to.
  * `bigtable_instance_id`: the instance id of the target Bigtable instance
  * `bigtable_app_profile_id`: the app profile id that is being used to access the target
    Bigtable instance

  ### Available operation level metric views:

  * `cloud.google.com/java/bigtable/op_latency`: A distribution of latency of
    each client method call, across all of it's RPC attempts. Tagged by
    operation name and final response status.

  * `cloud.google.com/java/bigtable/completed_ops`: The total count of
    method invocations. Tagged by operation name and final response status.

  * `cloud.google.com/java/bigtable/read_rows_first_row_latency`: A
    distribution of the latency of receiving the first row in a ReadRows
    operation.

  * `cloud.google.com/java/bigtable/attempt_latency`: A distribution of latency of
    each client RPC, tagged by operation name and the attempt status. Under normal
    circumstances, this will be identical to op_latency. However, when the client
    receives transient errors, op_latency will be the sum of all attempt_latencies
    and the exponential delays

  * `cloud.google.com/java/bigtable/attempts_per_op`: A distribution of attempts that
    each operation required, tagged by operation name and final operation status.
    Under normal circumstances, this will be 1.


  By default, the functionality is disabled. For example to enable metrics using
  [Google Stackdriver](https://cloud.google.com/monitoring/docs/):


  [//]: # (TODO: figure out how to keep opencensus version in sync with pom.xml)

  If you are using Maven, add this to your pom.xml file
  ```xml
  <dependency>
    <groupId>io.opencensus</groupId>
    <artifactId>opencensus-impl</artifactId>
    <version>0.24.0</version>
    <scope>runtime</scope>
  </dependency>
  <dependency>
    <groupId>io.opencensus</groupId>
    <artifactId>opencensus-exporter-stats-stackdriver</artifactId>
    <version>0.24.0</version>
    <exclusions>
      <exclusion>
        <groupId>io.grpc</groupId>
        <artifactId>*</artifactId>
      </exclusion>
      <exclusion>
        <groupId>com.google.auth</groupId>
        <artifactId>*</artifactId>
      </exclusion>
    </exclusions>
  </dependency>
  ```
  If you are using Gradle, add this to your dependencies
  ```Groovy
  compile 'io.opencensus:opencensus-impl:0.24.0'
  compile 'io.opencensus:opencensus-exporter-stats-stackdriver:0.24.0'
  ```
  If you are using SBT, add this to your dependencies
  ```Scala
  libraryDependencies += "io.opencensus" % "opencensus-impl" % "0.24.0"
  libraryDependencies += "io.opencensus" % "opencensus-exporter-stats-stackdriver" % "0.24.0"
  ```

  At the start of your application configure the exporter and enable the Bigtable stats views:

  ```java
  import io.opencensus.exporter.stats.stackdriver.StackdriverStatsConfiguration;
  import io.opencensus.exporter.stats.stackdriver.StackdriverStatsExporter;

  StackdriverStatsExporter.createAndRegister(
      StackdriverStatsConfiguration.builder()
          .setProjectId("YOUR_PROJECT_ID")
          .build()
  );

  BigtableDataSettings.enableOpenCensusStats();
  ```

  ## Version Conflicts

  google-cloud-bigtable depends on gRPC directly which may conflict with the versions brought
  in by other libraries, for example Apache Beam. This happens because internal dependencies
  between gRPC libraries are pinned to an exact version of grpc-core
  (see [here](https://github.com/grpc/grpc-java/commit/90db93b990305aa5a8428cf391b55498c7993b6e)).
  If both google-cloud-bigtable and the other library bring in two gRPC libraries that depend
  on the different versions of grpc-core, then dependency resolution will fail.
  The easiest way to fix this is to depend on the gRPC bom, which will force all the gRPC
  transitive libraries to use the same version.

  Add the following to your project's pom.xml.

  ```
      <dependencyManagement>
        <dependencies>
          <dependency>
            <groupId>io.grpc</groupId>
            <artifactId>grpc-bom</artifactId>
            <version>1.28.0</version>
            <type>pom</type>
            <scope>import</scope>
          </dependency>
        </dependencies>
      </dependencyManagement>
  ```

  ## Container Deployment

  While deploying this client in [Google Kubernetes Engine(GKE)](https://cloud.google.com/kubernetes-engine) with [CoS](https://cloud.google.com/container-optimized-os/docs/). Please make sure to provide CPU configuration in your deployment file. With default configuration JVM detects only 1 CPU, which affects the number of channels with the client, resulting in performance repercussion.

  Also, The number of `grpc-nio-worker-ELG-1-#` thread is same as number of CPUs. These are managed by a single `grpc-default-executor-#` thread, which is shared among multiple client instances.

  For example:
  ```yaml
  appVersion: v1
  ...
  spec:
    ...
    container:
      resources:
        requests:
          cpu: "1" # Here 1 represents 100% of single node CPUs whereas other than 1 represents the number of CPU it would use from a node.
  ```
  see [Assign CPU Resources to Containers](https://kubernetes.io/docs/tasks/configure-pod-container/assign-cpu-resource/#specify-a-cpu-request-and-a-cpu-limit) for more information.