diff --git a/Cargo.lock b/Cargo.lock
index 02c9fca..1e58bfd 100644
--- a/Cargo.lock
+++ b/Cargo.lock
@@ -967,9 +967,9 @@ checksum = "b6c80984affa11d98d1b88b66ac8853f143217b399d3c74116778ff8fdb4ed2e"
 
 [[package]]
 name = "h2"
-version = "0.3.19"
+version = "0.3.20"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "d357c7ae988e7d2182f7d7871d0b963962420b0678b0997ce7de72001aeab782"
+checksum = "97ec8491ebaf99c8eaa73058b045fe58073cd6be7f596ac993ced0b0a0c01049"
 dependencies = [
  "bytes",
  "fnv",
@@ -1107,9 +1107,9 @@ checksum = "c4a1e36c821dbe04574f602848a19f742f4fb3c98d40449f11bcad18d6b17421"
 
 [[package]]
 name = "hyper"
-version = "0.14.26"
+version = "0.14.27"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "ab302d72a6f11a3b910431ff93aae7e773078c769f0a3ef15fb9ec692ed147d4"
+checksum = "ffb1cfd654a8219eaef89881fdb3bb3b1cdc5fa75ded05d6933b2b382e395468"
 dependencies = [
  "bytes",
  "futures-channel",
@@ -1242,9 +1242,9 @@ checksum = "e2abad23fbc42b3700f2f279844dc832adb2b2eb069b2df918f455c4e18cc646"
 
 [[package]]
 name = "libc"
-version = "0.2.146"
+version = "0.2.147"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "f92be4933c13fd498862a9e02a3055f8a8d9c039ce33db97306fd5a6caa7f29b"
+checksum = "b4668fb0ea861c1df094127ac5f1da3409a82116a4ba74fca2e58ef927159bb3"
 
 [[package]]
 name = "libsqlite3-sys"
diff --git a/README.md b/README.md
index 0c35d64..063ef5a 100644
--- a/README.md
+++ b/README.md
@@ -6,7 +6,7 @@ This is an experimental binding using the [s3s](https://crates.io/crates/s3s) cr
 
 ## Why
 
-This crate was built to test the feasiblity of using SQLite as an alternative to formats like [TFRecord](https://www.tensorflow.org/tutorials/load_data/tfrecord) for storing a large number of machine learning training data with an accessible API. Although `tfrecord` and SQLite share a similar few-large-storage-files architecture - making backups and data movement efficient/easy - having both a SQLite interface and [Amazon S3](https://aws.amazon.com/s3/)/`http` API makes access to the contained data easier with tooling that likely already exists on your machine.
+This crate was built to test the feasiblity of using SQLite as an alternative to formats like [TFRecord](https://www.tensorflow.org/tutorials/load_data/tfrecord) for storing a large number of machine learning training data with an accessible API. Although `TFRecord` and SQLite share a similar few-large-storage-files architecture - making backups and data movement efficient/easy - having both a SQLite interface and [Amazon S3](https://aws.amazon.com/s3/)/`http` API makes access to the contained data easier with tooling that likely already exists on your machine.
 
 This concept is backed by benchmarks from SQLite showing that it can be [faster than filesytems](https://www.sqlite.org/fasterthanfs.html) for certain data access patterns.
 
@@ -26,7 +26,7 @@ CREATE TABLE IF NOT EXISTS data (
     metadata        TEXT,
     last_modified   TEXT NOT NULL,
     md5             TEXT
-) STRICT, WITHOUT ROWID;
+) STRICT;
 ```
 
 ### Multipart Uploads
@@ -40,7 +40,7 @@ CREATE TABLE IF NOT EXISTS multipart_upload (
     key                     TEXT NOT NULL,
     last_modified           TEXT NOT NULL,
     access_key              TEXT
-) STRICT, WITHOUT ROWID;
+) STRICT;
 
 CREATE TABLE IF NOT EXISTS multipart_upload_part (
     upload_id               BLOB NOT NULL,
@@ -71,7 +71,7 @@ cargo install --path .
 ## Run
 
 ```bash
-s3ite . --access-key AKIAIOSFODNN7EXAMPLE --secret-key wJalrXUtnFEMI/K7MDENG/bPxRfiCYEXAMPLEKEY
+s3ite . --host 0.0.0.0 --port 8014 --access-key AKIAIOSFODNN7EXAMPLE --secret-key wJalrXUtnFEMI/K7MDENG/bPxRfiCYEXAMPLEKEY
 ```
 
 ## Docker
diff --git a/src/sqlite.rs b/src/sqlite.rs
index 50c6bcb..bc8c654 100644
--- a/src/sqlite.rs
+++ b/src/sqlite.rs
@@ -89,6 +89,7 @@ impl Sqlite {
                             .interact(|connection| {
                                 connection.execute_batch(
                                     "
+                                    PRAGMA journal_mode=WAL;
                                     PRAGMA foreign_keys=true;
                                     PRAGMA analysis_limit=1000;
                                     PRAGMA optimize;
@@ -141,7 +142,12 @@ impl Sqlite {
         let connection = pool.get().await.unwrap();
         connection
             .interact(|connection| {
-                connection.execute_batch("PRAGMA foreign_keys=true;")?;
+                connection.execute_batch(
+                    "
+                    PRAGMA journal_mode=WAL;
+                    PRAGMA foreign_keys=true;
+                ",
+                )?;
 
                 let transaction = connection.transaction()?;
                 Self::try_create_tables(&transaction)?;
@@ -165,7 +171,7 @@ impl Sqlite {
                     metadata TEXT,
                     last_modified TEXT NOT NULL,
                     md5 TEXT
-                ) STRICT, WITHOUT ROWID;",
+                ) STRICT;",
             (),
         )?;
         transaction.execute(
@@ -175,7 +181,7 @@ impl Sqlite {
                     key TEXT NOT NULL,
                     last_modified TEXT NOT NULL,
                     access_key TEXT
-                ) STRICT, WITHOUT ROWID;",
+                ) STRICT;",
             (),
         )?;
         transaction.execute(